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Cross-Image Context for Single Image Inpainting | https://papers.nips.cc/paper_files/paper/2022/hash/09b6e009612875dd0a7291d5f4fd8b49-Abstract-Conference.html | Tingliang Feng, Wei Feng, Weiqi Li, Di Lin | https://papers.nips.cc/paper_files/paper/2022/hash/09b6e009612875dd0a7291d5f4fd8b49-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17803-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/09b6e009612875dd0a7291d5f4fd8b49-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/09b6e009612875dd0a7291d5f4fd8b49-Supplemental-Conference.pdf | Visual context is of crucial importance for image inpainting. The contextual information captures the appearance and semantic correlation between the image regions, helping to propagate the information of the complete regions for reasoning the content of the corrupted regions. Many inpainting methods compute the visual... | null | null |
Efficient and Effective Augmentation Strategy for Adversarial Training | https://papers.nips.cc/paper_files/paper/2022/hash/09d22e4155aa4fdadf3dac8c6bd940fe-Abstract-Conference.html | Sravanti Addepalli, Samyak Jain, Venkatesh Babu R | https://papers.nips.cc/paper_files/paper/2022/hash/09d22e4155aa4fdadf3dac8c6bd940fe-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19113-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/09d22e4155aa4fdadf3dac8c6bd940fe-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/09d22e4155aa4fdadf3dac8c6bd940fe-Supplemental-Conference.pdf | Adversarial training of Deep Neural Networks is known to be significantly more data-hungry when compared to standard training. Furthermore, complex data augmentations such as AutoAugment, which have led to substantial gains in standard training of image classifiers, have not been successful with Adversarial Training. W... | null | null |
Multi-Sample Training for Neural Image Compression | https://papers.nips.cc/paper_files/paper/2022/hash/09e7121c046e0ad54aada522d3e1f967-Abstract-Conference.html | Tongda Xu, Yan Wang, Dailan He, Chenjian Gao, Han Gao, Kunzan Liu, Hongwei Qin | https://papers.nips.cc/paper_files/paper/2022/hash/09e7121c046e0ad54aada522d3e1f967-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18681-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/09e7121c046e0ad54aada522d3e1f967-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/09e7121c046e0ad54aada522d3e1f967-Supplemental-Conference.pdf | This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (SOTA) methods adopt uniform posterior to approximate quantization noise, and single-sample pathwise estimator to approximate the gradient of evidence lower bound (ELBO). In this paper, we propose to train NIC with multip... | null | null |
Adaptive Data Debiasing through Bounded Exploration | https://papers.nips.cc/paper_files/paper/2022/hash/0a166a3d98720697d9028bbe592fa177-Abstract-Conference.html | Yifan Yang, Yang Liu, Parinaz Naghizadeh | https://papers.nips.cc/paper_files/paper/2022/hash/0a166a3d98720697d9028bbe592fa177-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19131-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a166a3d98720697d9028bbe592fa177-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a166a3d98720697d9028bbe592fa177-Supplemental-Conference.zip | Biases in existing datasets used to train algorithmic decision rules can raise ethical and economic concerns due to the resulting disparate treatment of different groups. We propose an algorithm for sequentially debiasing such datasets through adaptive and bounded exploration in a classification problem with costly and... | null | null |
Learning to Navigate Wikipedia by Taking Random Walks | https://papers.nips.cc/paper_files/paper/2022/hash/0a245311a23460d1846043d4156445d6-Abstract-Conference.html | Manzil Zaheer, Kenneth Marino, Will Grathwohl, John Schultz, Wendy Shang, Sheila Babayan, Arun Ahuja, Ishita Dasgupta, Christine Kaeser-Chen, Rob Fergus | https://papers.nips.cc/paper_files/paper/2022/hash/0a245311a23460d1846043d4156445d6-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18723-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a245311a23460d1846043d4156445d6-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a245311a23460d1846043d4156445d6-Supplemental-Conference.pdf | A fundamental ability of an intelligent web-based agent is seeking out and acquiring new information. Internet search engines reliably find the correct vicinity but the top results may be a few links away from the desired target. A complementary approach is navigation via hyperlinks, employing a policy that comprehends... | null | null |
When does return-conditioned supervised learning work for offline reinforcement learning? | https://papers.nips.cc/paper_files/paper/2022/hash/0a2f65c9d2313b71005e600bd23393fe-Abstract-Conference.html | David Brandfonbrener, Alberto Bietti, Jacob Buckman, Romain Laroche, Joan Bruna | https://papers.nips.cc/paper_files/paper/2022/hash/0a2f65c9d2313b71005e600bd23393fe-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17342-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a2f65c9d2313b71005e600bd23393fe-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a2f65c9d2313b71005e600bd23393fe-Supplemental-Conference.zip | Several recent works have proposed a class of algorithms for the offline reinforcement learning (RL) problem that we will refer to as return-conditioned supervised learning (RCSL). RCSL algorithms learn the distribution of actions conditioned on both the state and the return of the trajectory. Then they define a policy... | null | null |
Provable Subspace Identification Under Post-Nonlinear Mixtures | https://papers.nips.cc/paper_files/paper/2022/hash/0a6059857ae5c82ea9726ee9282a7145-Abstract-Conference.html | Qi Lyu, Xiao Fu | https://papers.nips.cc/paper_files/paper/2022/hash/0a6059857ae5c82ea9726ee9282a7145-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17290-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a6059857ae5c82ea9726ee9282a7145-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a6059857ae5c82ea9726ee9282a7145-Supplemental-Conference.pdf | Unsupervised mixture learning (UML) aims at identifying linearly or nonlinearly mixed latent components in a blind manner. UML is known to be challenging: Even learning linear mixtures requires highly nontrivial analytical tools, e.g., independent component analysis or nonnegative matrix factorization. In this work, th... | null | null |
S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint | https://papers.nips.cc/paper_files/paper/2022/hash/0a630402ee92620dc2de3b704181de9b-Abstract-Conference.html | Wenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen, Kwan-Yee K. Wong | https://papers.nips.cc/paper_files/paper/2022/hash/0a630402ee92620dc2de3b704181de9b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19032-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a630402ee92620dc2de3b704181de9b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a630402ee92620dc2de3b704181de9b-Supplemental-Conference.pdf | In this paper, we address the "dual problem" of multi-view scene reconstruction in which we utilize single-view images captured under different point lights to learn a neural scene representation. Different from existing single-view methods which can only recover a 2.5D scene representation (i.e., a normal / depth map ... | null | null |
AdaFocal: Calibration-aware Adaptive Focal Loss | https://papers.nips.cc/paper_files/paper/2022/hash/0a692a24dbc744fca340b9ba33bc6522-Abstract-Conference.html | Arindam Ghosh, Thomas Schaaf, Matthew Gormley | https://papers.nips.cc/paper_files/paper/2022/hash/0a692a24dbc744fca340b9ba33bc6522-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17973-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a692a24dbc744fca340b9ba33bc6522-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a692a24dbc744fca340b9ba33bc6522-Supplemental-Conference.pdf | Much recent work has been devoted to the problem of ensuring that a neural network's confidence scores match the true probability of being correct, i.e. the calibration problem. Of note, it was found that training with focal loss leads to better calibration than cross-entropy while achieving similar level of accuracy \... | null | null |
Learning Robust Dynamics through Variational Sparse Gating | https://papers.nips.cc/paper_files/paper/2022/hash/0a97df4ce5b403ea87645010e9005130-Abstract-Conference.html | Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, Samira Ebrahimi Kahou | https://papers.nips.cc/paper_files/paper/2022/hash/0a97df4ce5b403ea87645010e9005130-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18129-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0a97df4ce5b403ea87645010e9005130-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0a97df4ce5b403ea87645010e9005130-Supplemental-Conference.zip | Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet been applied successfully to environments with many objects. In environ... | null | null |
Where to Pay Attention in Sparse Training for Feature Selection? | https://papers.nips.cc/paper_files/paper/2022/hash/0aa800df4298539770b57824afc77a89-Abstract-Conference.html | Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu | https://papers.nips.cc/paper_files/paper/2022/hash/0aa800df4298539770b57824afc77a89-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19410-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0aa800df4298539770b57824afc77a89-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0aa800df4298539770b57824afc77a89-Supplemental-Conference.pdf | A new line of research for feature selection based on neural networks has recently emerged. Despite its superiority to classical methods, it requires many training iterations to converge and detect the informative features. For datasets with a large number of samples or a very high dimensional feature space, the comput... | null | null |
Maximizing Revenue under Market Shrinkage and Market Uncertainty | https://papers.nips.cc/paper_files/paper/2022/hash/0aeb9a0f0a9715e853953ceb96531473-Abstract-Conference.html | Maria-Florina F. Balcan, Siddharth Prasad, Tuomas Sandholm | https://papers.nips.cc/paper_files/paper/2022/hash/0aeb9a0f0a9715e853953ceb96531473-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18483-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0aeb9a0f0a9715e853953ceb96531473-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0aeb9a0f0a9715e853953ceb96531473-Supplemental-Conference.pdf | A shrinking market is a ubiquitous challenge faced by various industries. In this paper we formulate the first formal model of shrinking markets in multi-item settings, and study how mechanism design and machine learning can help preserve revenue in an uncertain, shrinking market. Via a sample-based learning mechanism,... | null | null |
General Cutting Planes for Bound-Propagation-Based Neural Network Verification | https://papers.nips.cc/paper_files/paper/2022/hash/0b06c8673ebb453e5e468f7743d8f54e-Abstract-Conference.html | Huan Zhang, Shiqi Wang, Kaidi Xu, Linyi Li, Bo Li, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter | https://papers.nips.cc/paper_files/paper/2022/hash/0b06c8673ebb453e5e468f7743d8f54e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18121-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b06c8673ebb453e5e468f7743d8f54e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b06c8673ebb453e5e468f7743d8f54e-Supplemental-Conference.pdf | Bound propagation methods, when combined with branch and bound, are among the most effective methods to formally verify properties of deep neural networks such as correctness, robustness, and safety. However, existing works cannot handle the general form of cutting plane constraints widely accepted in traditional solve... | null | null |
An $\alpha$-regret analysis of Adversarial Bilateral Trade | https://papers.nips.cc/paper_files/paper/2022/hash/0b2832072ff6df19e586c74e27d90f12-Abstract-Conference.html | Yossi Azar, Amos Fiat, Federico Fusco | https://papers.nips.cc/paper_files/paper/2022/hash/0b2832072ff6df19e586c74e27d90f12-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19035-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b2832072ff6df19e586c74e27d90f12-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b2832072ff6df19e586c74e27d90f12-Supplemental-Conference.pdf | We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary ({\sl i.e.}, determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incenti... | null | null |
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0b4145b562cc22fb7fa50a2cd17c191d-Abstract-Conference.html | Mingyu Yang, Jian Zhao, Xunhan Hu, Wengang Zhou, Jiangcheng Zhu, Houqiang Li | https://papers.nips.cc/paper_files/paper/2022/hash/0b4145b562cc22fb7fa50a2cd17c191d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17152-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b4145b562cc22fb7fa50a2cd17c191d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b4145b562cc22fb7fa50a2cd17c191d-Supplemental-Conference.zip | Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all agents share the same policy or value network. However, in many complex multi-agent tasks, different agents are expected to possess specific ab... | null | null |
Mildly Conservative Q-Learning for Offline Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0b5669c3b07bb8429af19a7919376ff5-Abstract-Conference.html | Jiafei Lyu, Xiaoteng Ma, Xiu Li, Zongqing Lu | https://papers.nips.cc/paper_files/paper/2022/hash/0b5669c3b07bb8429af19a7919376ff5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18947-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b5669c3b07bb8429af19a7919376ff5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b5669c3b07bb8429af19a7919376ff5-Supplemental-Conference.pdf | Offline reinforcement learning (RL) defines the task of learning from a static logged dataset without continually interacting with the environment. The distribution shift between the learned policy and the behavior policy makes it necessary for the value function to stay conservative such that out-of-distribution (OOD)... | null | null |
Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments | https://papers.nips.cc/paper_files/paper/2022/hash/0b5eb45a22ff33956c043dd271f244ea-Abstract-Conference.html | Yining Chen, Elan Rosenfeld, Mark Sellke, Tengyu Ma, Andrej Risteski | https://papers.nips.cc/paper_files/paper/2022/hash/0b5eb45a22ff33956c043dd271f244ea-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16821-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b5eb45a22ff33956c043dd271f244ea-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b5eb45a22ff33956c043dd271f244ea-Supplemental-Conference.zip | Domain generalization aims at performing well on unseen test environments with data from a limited number of training environments. Despite a proliferation of proposed algorithms for this task, assessing their performance both theoretically and empirically is still very challenging. Distributional matching algorithms s... | null | null |
Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats | https://papers.nips.cc/paper_files/paper/2022/hash/0b6b00f384aa33fec1f3d6bcf9550224-Abstract-Conference.html | Hongwei Jin, Zishun Yu, Xinhua Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/0b6b00f384aa33fec1f3d6bcf9550224-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17048-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b6b00f384aa33fec1f3d6bcf9550224-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b6b00f384aa33fec1f3d6bcf9550224-Supplemental-Conference.pdf | Graph classifiers are vulnerable to topological attacks. Although certificates of robustness have been recently developed, their threat model only counts local and global edge perturbations, which effectively ignores important graph structures such as isomorphism. To address this issue, we propose measuring the perturb... | null | null |
Functional Ensemble Distillation | https://papers.nips.cc/paper_files/paper/2022/hash/0b7f639ef28a9035a71f7e0c04c1d681-Abstract-Conference.html | Coby Penso, Idan Achituve, Ethan Fetaya | https://papers.nips.cc/paper_files/paper/2022/hash/0b7f639ef28a9035a71f7e0c04c1d681-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17357-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b7f639ef28a9035a71f7e0c04c1d681-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b7f639ef28a9035a71f7e0c04c1d681-Supplemental-Conference.pdf | Bayesian models have many desirable properties, most notable is their ability to generalize from limited data and to properly estimate the uncertainty in their predictions. However, these benefits come at a steep computational cost as Bayesian inference, in most cases, is computationally intractable. One popular approa... | null | null |
Use-Case-Grounded Simulations for Explanation Evaluation | https://papers.nips.cc/paper_files/paper/2022/hash/0b9536e186a77feff516893a5f393f7a-Abstract-Conference.html | Valerie Chen, Nari Johnson, Nicholay Topin, Gregory Plumb, Ameet Talwalkar | https://papers.nips.cc/paper_files/paper/2022/hash/0b9536e186a77feff516893a5f393f7a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18502-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0b9536e186a77feff516893a5f393f7a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0b9536e186a77feff516893a5f393f7a-Supplemental-Conference.pdf | A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, and consequently each study typically only evaluates a limited number of dif... | null | null |
Lethal Dose Conjecture on Data Poisoning | https://papers.nips.cc/paper_files/paper/2022/hash/0badcb4e95306df76a719409155e46e8-Abstract-Conference.html | Wenxiao Wang, Alexander Levine, Soheil Feizi | https://papers.nips.cc/paper_files/paper/2022/hash/0badcb4e95306df76a719409155e46e8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17704-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0badcb4e95306df76a719409155e46e8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0badcb4e95306df76a719409155e46e8-Supplemental-Conference.zip | Data poisoning considers an adversary that distorts the training set of machine learning algorithms for malicious purposes. In this work, we bring to light one conjecture regarding the fundamentals of data poisoning, which we call the Lethal Dose Conjecture. The conjecture states: If $n$ clean training samples are need... | null | null |
Online Decision Mediation | https://papers.nips.cc/paper_files/paper/2022/hash/0bc795afae289ed465a65a3b4b1f4eb7-Abstract-Conference.html | Daniel Jarrett, Alihan Hüyük, Mihaela van der Schaar | https://papers.nips.cc/paper_files/paper/2022/hash/0bc795afae289ed465a65a3b4b1f4eb7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18331-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0bc795afae289ed465a65a3b4b1f4eb7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0bc795afae289ed465a65a3b4b1f4eb7-Supplemental-Conference.pdf | Consider learning a decision support assistant to serve as an intermediary between (oracle) expert behavior and (imperfect) human behavior: At each time, the algorithm observes an action chosen by a fallible agent, and decides whether to accept that agent's decision, intervene with an alternative, or request the expert... | null | null |
Neural-Symbolic Entangled Framework for Complex Query Answering | https://papers.nips.cc/paper_files/paper/2022/hash/0bcfb525c8f8f07ae10a93d0b2a40e00-Abstract-Conference.html | Zezhong Xu, Wen Zhang, Peng Ye, Hui Chen, Huajun Chen | https://papers.nips.cc/paper_files/paper/2022/hash/0bcfb525c8f8f07ae10a93d0b2a40e00-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19057-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0bcfb525c8f8f07ae10a93d0b2a40e00-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0bcfb525c8f8f07ae10a93d0b2a40e00-Supplemental-Conference.zip | Answering complex queries over knowledge graphs (KG) is an important yet challenging task because of the KG incompleteness issue and cascading errors during reasoning. Recent query embedding (QE) approaches embed the entities and relations in a KG and the first-order logic (FOL) queries into a low dimensional space, ma... | null | null |
Reinforcement Learning with Automated Auxiliary Loss Search | https://papers.nips.cc/paper_files/paper/2022/hash/0be44cc1d459731928501cae5699f57a-Abstract-Conference.html | Tairan He, Yuge Zhang, Kan Ren, Minghuan Liu, Che Wang, Weinan Zhang, Yuqing Yang, Dongsheng Li | https://papers.nips.cc/paper_files/paper/2022/hash/0be44cc1d459731928501cae5699f57a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17043-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0be44cc1d459731928501cae5699f57a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0be44cc1d459731928501cae5699f57a-Supplemental-Conference.zip | A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative representations. Unfortunately, these handcrafted objectives rely heavily on expert knowledge and may be sub-optimal. In this paper, we pro... | null | null |
M$^4$I: Multi-modal Models Membership Inference | https://papers.nips.cc/paper_files/paper/2022/hash/0c79d6ed1788653643a1ac67b6ea32a7-Abstract-Conference.html | Pingyi Hu, Zihan Wang, Ruoxi Sun, Hu Wang, Minhui Xue | https://papers.nips.cc/paper_files/paper/2022/hash/0c79d6ed1788653643a1ac67b6ea32a7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18956-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0c79d6ed1788653643a1ac67b6ea32a7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0c79d6ed1788653643a1ac67b6ea32a7-Supplemental-Conference.pdf | With the development of machine learning techniques, the attention of research has been moved from single-modal learning to multi-modal learning, as real-world data exist in the form of different modalities. However, multi-modal models often carry more information than single-modal models and they are usually applied i... | null | null |
Best of Both Worlds Model Selection | https://papers.nips.cc/paper_files/paper/2022/hash/0c8d3770cbb759430f4f4679abe3ab80-Abstract-Conference.html | Aldo Pacchiano, Christoph Dann, Claudio Gentile | https://papers.nips.cc/paper_files/paper/2022/hash/0c8d3770cbb759430f4f4679abe3ab80-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16665-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0c8d3770cbb759430f4f4679abe3ab80-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0c8d3770cbb759430f4f4679abe3ab80-Supplemental-Conference.pdf | We study the problem of model selection in bandit scenarios in the presence of nested policy classes, with the goal of obtaining simultaneous adversarial and stochastic (``best of both worlds") high-probability regret guarantees. Our approach requires that each base learner comes with a candidate regret bound that may ... | null | null |
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes | https://papers.nips.cc/paper_files/paper/2022/hash/0cc21b418ec126f005c7fe8157432339-Abstract-Conference.html | Peter Kocsis, Peter Súkeník, Guillem Braso, Matthias Niessner, Laura Leal-Taixé, Ismail Elezi | https://papers.nips.cc/paper_files/paper/2022/hash/0cc21b418ec126f005c7fe8157432339-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17429-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0cc21b418ec126f005c7fe8157432339-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0cc21b418ec126f005c7fe8157432339-Supplemental-Conference.zip | Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive performance. These architectures typically have a vast number of weights and need to be trained on massive datasets; hence, they are not su... | null | null |
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative | https://papers.nips.cc/paper_files/paper/2022/hash/0cd1eec0eeaf5ce1bf6d8875a7c1d095-Abstract-Conference.html | Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang | https://papers.nips.cc/paper_files/paper/2022/hash/0cd1eec0eeaf5ce1bf6d8875a7c1d095-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16965-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0cd1eec0eeaf5ce1bf6d8875a7c1d095-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0cd1eec0eeaf5ce1bf6d8875a7c1d095-Supplemental-Conference.pdf | This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL). We focus on the following question: How to construct contrastive views for hypergraphs via augmentations? We pro... | null | null |
On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games | https://papers.nips.cc/paper_files/paper/2022/hash/0cd4c8c7ba098b199242c6634f43f653-Abstract-Conference.html | Runyu Zhang, Jincheng Mei, Bo Dai, Dale Schuurmans, Na Li | https://papers.nips.cc/paper_files/paper/2022/hash/0cd4c8c7ba098b199242c6634f43f653-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17560-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0cd4c8c7ba098b199242c6634f43f653-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0cd4c8c7ba098b199242c6634f43f653-Supplemental-Conference.pdf | Softmax policy gradient is a popular algorithm for policy optimization in single-agent reinforcement learning, particularly since projection is not needed for each gradient update. However, in multi-agent systems, the lack of central coordination introduces significant additional difficulties in the convergence analysi... | null | null |
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization | https://papers.nips.cc/paper_files/paper/2022/hash/0cddb777d3441326544e21b67f41bdc8-Abstract-Conference.html | Minsu Kim, Junyoung Park, Jinkyoo Park | https://papers.nips.cc/paper_files/paper/2022/hash/0cddb777d3441326544e21b67f41bdc8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17562-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0cddb777d3441326544e21b67f41bdc8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0cddb777d3441326544e21b67f41bdc8-Supplemental-Conference.pdf | Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable of learning CO solvers less relying on problem-specific expert domain knowledge (heuristic method) and supervised labeled data (supervised l... | null | null |
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0cde695b83bd186c1fd456302888454c-Abstract-Conference.html | Haokun Liu, Derek Tam, Mohammed Muqeeth, Jay Mohta, Tenghao Huang, Mohit Bansal, Colin A. Raffel | https://papers.nips.cc/paper_files/paper/2022/hash/0cde695b83bd186c1fd456302888454c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17102-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0cde695b83bd186c1fd456302888454c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0cde695b83bd186c1fd456302888454c-Supplemental-Conference.pdf | Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the tr... | null | null |
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details | https://papers.nips.cc/paper_files/paper/2022/hash/0ce8e3434c7b486bbddff9745b2a1722-Abstract-Conference.html | Yiqun Wang, Ivan Skorokhodov, Peter Wonka | https://papers.nips.cc/paper_files/paper/2022/hash/0ce8e3434c7b486bbddff9745b2a1722-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18174-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0ce8e3434c7b486bbddff9745b2a1722-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0ce8e3434c7b486bbddff9745b2a1722-Supplemental-Conference.pdf | Neural rendering can be used to reconstruct implicit representations of shapes without 3D supervision. However, current neural surface reconstruction methods have difficulty learning high-frequency geometry details, so the reconstructed shapes are often over-smoothed. We develop HF-NeuS, a novel method to improve the q... | null | null |
On the Epistemic Limits of Personalized Prediction | https://papers.nips.cc/paper_files/paper/2022/hash/0cfc9404f89400c5ed897035e0d3748c-Abstract-Conference.html | Lucas Monteiro Paes, Carol Long, Berk Ustun, Flavio Calmon | https://papers.nips.cc/paper_files/paper/2022/hash/0cfc9404f89400c5ed897035e0d3748c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19248-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0cfc9404f89400c5ed897035e0d3748c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0cfc9404f89400c5ed897035e0d3748c-Supplemental-Conference.pdf | Machine learning models are often personalized by using group attributes that encode personal characteristics (e.g., sex, age group, HIV status). In such settings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model. We study when we can tell that ... | null | null |
DeepInteraction: 3D Object Detection via Modality Interaction | https://papers.nips.cc/paper_files/paper/2022/hash/0d18ab3b5fabfa6fe47c62e711af02f0-Abstract-Conference.html | Zeyu Yang, Jiaqi Chen, Zhenwei Miao, Wei Li, Xiatian Zhu, Li Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/0d18ab3b5fabfa6fe47c62e711af02f0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16951-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0d18ab3b5fabfa6fe47c62e711af02f0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0d18ab3b5fabfa6fe47c62e711af02f0-Supplemental-Conference.pdf | Existing top-performance 3D object detectors typically rely on the multi-modal fusion strategy. This design is however fundamentally restricted due to overlooking the modality-specific useful information and finally hampering the model performance. To address this limitation, in this work we introduce a novel modality ... | null | null |
Deep Differentiable Logic Gate Networks | https://papers.nips.cc/paper_files/paper/2022/hash/0d3496dd0cec77a999c98d35003203ca-Abstract-Conference.html | Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen | https://papers.nips.cc/paper_files/paper/2022/hash/0d3496dd0cec77a999c98d35003203ca-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19235-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0d3496dd0cec77a999c98d35003203ca-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0d3496dd0cec77a999c98d35003203ca-Supplemental-Conference.pdf | Recently, research has increasingly focused on developing efficient neural network architectures. In this work, we explore logic gate networks for machine learning tasks by learning combinations of logic gates. These networks comprise logic gates such as "AND" and "XOR", which allow for very fast execution. The difficu... | null | null |
Maximizing and Satisficing in Multi-armed Bandits with Graph Information | https://papers.nips.cc/paper_files/paper/2022/hash/0d561979f0f4bc6127cfcfe9c46ee205-Abstract-Conference.html | Parth Thaker, Mohit Malu, Nikhil Rao, Gautam Dasarathy | https://papers.nips.cc/paper_files/paper/2022/hash/0d561979f0f4bc6127cfcfe9c46ee205-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18446-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0d561979f0f4bc6127cfcfe9c46ee205-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0d561979f0f4bc6127cfcfe9c46ee205-Supplemental-Conference.pdf | Pure exploration in multi-armed bandits has emerged as an important framework for modeling decision making and search under uncertainty. In modern applications however, one is often faced with a tremendously large number of options and even obtaining one observation per option may be too costly rendering traditional pu... | null | null |
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation | https://papers.nips.cc/paper_files/paper/2022/hash/0d81e6f2511fc78631ee0315fafeef9e-Abstract-Conference.html | Yunsong Zhou, Quan Liu, Hongzi Zhu, Yunzhe Li, Shan Chang, Minyi Guo | https://papers.nips.cc/paper_files/paper/2022/hash/0d81e6f2511fc78631ee0315fafeef9e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18435-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0d81e6f2511fc78631ee0315fafeef9e-Paper-Conference.pdf | null | Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon of monocular vision and the ever-changing camera pose, it is hard to acquire high detection accuracy, especially for far objects. Inspired by... | null | null |
Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0d9057d84a9fc37523bf826232ea6820-Abstract-Conference.html | Harikrishnan N B, Aditi Kathpalia, Nithin Nagaraj | https://papers.nips.cc/paper_files/paper/2022/hash/0d9057d84a9fc37523bf826232ea6820-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18110-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0d9057d84a9fc37523bf826232ea6820-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0d9057d84a9fc37523bf826232ea6820-Supplemental-Conference.pdf | Discovering cause and effect variables from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (NL) is used for the classification of cause and effect time series generated using cou... | null | null |
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification | https://papers.nips.cc/paper_files/paper/2022/hash/0e0157ce5ea15831072be4744cbd5334-Abstract-Conference.html | Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis, Rohit Babbar | https://papers.nips.cc/paper_files/paper/2022/hash/0e0157ce5ea15831072be4744cbd5334-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17492-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0e0157ce5ea15831072be4744cbd5334-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0e0157ce5ea15831072be4744cbd5334-Supplemental-Conference.pdf | Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent approaches, such as XR-Transformer and LightXML, leverage a transformer instance to achieve state-of-the-art performance. However, in this pr... | null | null |
VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? | https://papers.nips.cc/paper_files/paper/2022/hash/0e1a2388cd2f78069f4d048d935cb218-Abstract-Conference.html | Jiawei Jiang, Lukas Burkhalter, Fangcheng Fu, Bolin Ding, Bo Du, Anwar Hithnawi, Bo Li, Ce Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/0e1a2388cd2f78069f4d048d935cb218-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16842-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0e1a2388cd2f78069f4d048d935cb218-Paper-Conference.pdf | null | Vertical Federated Learning (VFL), that trains federated models over vertically partitioned data, has emerged as an important learning paradigm. However, existing VFL methods are facing two challenges: (1) scalability when # participants grows to even modest scale and (2) diminishing return w.r.t. # participants: not a... | null | null |
PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/0e5cce15e1bfc6b3d7b71f24cc5da821-Abstract-Conference.html | Kyoungseok Jang, Chicheng Zhang, Kwang-Sung Jun | https://papers.nips.cc/paper_files/paper/2022/hash/0e5cce15e1bfc6b3d7b71f24cc5da821-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16893-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0e5cce15e1bfc6b3d7b71f24cc5da821-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0e5cce15e1bfc6b3d7b71f24cc5da821-Supplemental-Conference.zip | In sparse linear bandits, a learning agent sequentially selects an action from a fixed action set and receives reward feedback, and the reward function depends linearly on a few coordinates of the covariates of the actions. This has applications in many real-world sequential decision making problems. In this paper, we ... | null | null |
Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions | https://papers.nips.cc/paper_files/paper/2022/hash/0ea048312aa812b2711fe765a9e9ef05-Abstract-Conference.html | Anupam Gupta, Debmalya Panigrahi, Bernardo Subercaseaux, Kevin Sun | https://papers.nips.cc/paper_files/paper/2022/hash/0ea048312aa812b2711fe765a9e9ef05-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18779-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0ea048312aa812b2711fe765a9e9ef05-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0ea048312aa812b2711fe765a9e9ef05-Supplemental-Conference.pdf | The growing body of work in learning-augmented online algorithms studies how online algorithms can be improved when given access to ML predictions about the future. Motivated by ML models that give a confidence parameter for their predictions, we study online algorithms with predictions that are $\epsilon$-accurate: na... | null | null |
Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns | https://papers.nips.cc/paper_files/paper/2022/hash/0eaf2c04280c7fecc8b26762dd4ab6da-Abstract-Conference.html | Laurynas Karazija, Subhabrata Choudhury, Iro Laina, Christian Rupprecht, Andrea Vedaldi | https://papers.nips.cc/paper_files/paper/2022/hash/0eaf2c04280c7fecc8b26762dd4ab6da-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18385-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0eaf2c04280c7fecc8b26762dd4ab6da-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0eaf2c04280c7fecc8b26762dd4ab6da-Supplemental-Conference.pdf | We propose a new approach to learn to segment multiple image objects without manual supervision. The method can extract objects form still images, but uses videos for supervision. While prior works have considered motion for segmentation, a key insight is that, while motion can be used to identify objects, not all obje... | null | null |
Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards | https://papers.nips.cc/paper_files/paper/2022/hash/0ee633a6ade45eab4276352b3ee79c7a-Abstract-Conference.html | Ashwinkumar Badanidiyuru Varadaraja, Zhe Feng, Tianxi Li, Haifeng Xu | https://papers.nips.cc/paper_files/paper/2022/hash/0ee633a6ade45eab4276352b3ee79c7a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18235-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0ee633a6ade45eab4276352b3ee79c7a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0ee633a6ade45eab4276352b3ee79c7a-Supplemental-Conference.pdf | Incrementality, which measures the causal effect of showing an ad to a potential customer (e.g. a user in an internet platform) versus not, is a central object for advertisers in online advertising platforms. This paper investigates the problem of how an advertiser can learn to optimize the bidding sequence in an onl... | null | null |
Masked Generative Adversarial Networks are Data-Efficient Generation Learners | https://papers.nips.cc/paper_files/paper/2022/hash/0efcb1885b8534109f95ca82a5319d25-Abstract-Conference.html | Jiaxing Huang, Kaiwen Cui, Dayan Guan, Aoran Xiao, Fangneng Zhan, Shijian Lu, Shengcai Liao, Eric Xing | https://papers.nips.cc/paper_files/paper/2022/hash/0efcb1885b8534109f95ca82a5319d25-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19008-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0efcb1885b8534109f95ca82a5319d25-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0efcb1885b8534109f95ca82a5319d25-Supplemental-Conference.pdf | This paper shows that masked generative adversarial network (MaskedGAN) is robust image generation learners with limited training data. The idea of MaskedGAN is simple: it randomly masks out certain image information for effective GAN training with limited data. We develop two masking strategies that work along orthogo... | null | null |
What You See is What You Get: Principled Deep Learning via Distributional Generalization | https://papers.nips.cc/paper_files/paper/2022/hash/0f4bbaaaf1e167f79134dd4cf11e3ed4-Abstract-Conference.html | Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu, Jarosław Błasiok, Preetum Nakkiran | https://papers.nips.cc/paper_files/paper/2022/hash/0f4bbaaaf1e167f79134dd4cf11e3ed4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17090-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f4bbaaaf1e167f79134dd4cf11e3ed4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f4bbaaaf1e167f79134dd4cf11e3ed4-Supplemental-Conference.zip | Having similar behavior at training time and test time—what we call a “What You See Is What You Get” (WYSIWYG) property—is desirable in machine learning. Models trained with standard stochastic gradient descent (SGD), however, do not necessarily have this property, as their complex behaviors such as robustness or subgr... | null | null |
Towards Understanding the Condensation of Neural Networks at Initial Training | https://papers.nips.cc/paper_files/paper/2022/hash/0f4d1fc085b7504c140e66bb26ed8842-Abstract-Conference.html | Hanxu Zhou, Zhou Qixuan, Tao Luo, Yaoyu Zhang, Zhi-Qin Xu | https://papers.nips.cc/paper_files/paper/2022/hash/0f4d1fc085b7504c140e66bb26ed8842-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18508-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f4d1fc085b7504c140e66bb26ed8842-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f4d1fc085b7504c140e66bb26ed8842-Supplemental-Conference.zip | Empirical works show that for ReLU neural networks (NNs) with small initialization, input weights of hidden neurons (the input weight of a hidden neuron consists of the weight from its input layer to the hidden neuron and its bias term) condense onto isolated orientations. The condensation dynamics implies that the tra... | null | null |
CoNT: Contrastive Neural Text Generation | https://papers.nips.cc/paper_files/paper/2022/hash/0f5fcf4bff73a3537e0813a38f0d3f76-Abstract-Conference.html | Chenxin An, Jiangtao Feng, Kai Lv, Lingpeng Kong, Xipeng Qiu, Xuanjing Huang | https://papers.nips.cc/paper_files/paper/2022/hash/0f5fcf4bff73a3537e0813a38f0d3f76-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17850-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f5fcf4bff73a3537e0813a38f0d3f76-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f5fcf4bff73a3537e0813a38f0d3f76-Supplemental-Conference.zip | Recently, contrastive learning attracts increasing interests in neural text generation as a new solution to alleviate the exposure bias problem. It introduces a sequence-level training signal which is crucial to generation tasks that always rely on auto-regressive decoding. However, previous methods using contrastive ... | null | null |
GAPX: Generalized Autoregressive Paraphrase-Identification X | https://papers.nips.cc/paper_files/paper/2022/hash/0f6cc80ad86e553d085842308e0fd2cb-Abstract-Conference.html | Yifei Zhou, Renyu Li, Hayden Housen, Ser Nam Lim | https://papers.nips.cc/paper_files/paper/2022/hash/0f6cc80ad86e553d085842308e0fd2cb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16983-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f6cc80ad86e553d085842308e0fd2cb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f6cc80ad86e553d085842308e0fd2cb-Supplemental-Conference.pdf | Paraphrase Identification is a fundamental task in Natural Language Processing. While much progress has been made in the field, the performance of many state-of- the-art models often suffer from distribution shift during inference time. We verify that a major source of this performance drop comes from biases introduced... | null | null |
Scalable Infomin Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0f7e4bb7a35dd4cb426203c91a4bfa10-Abstract-Conference.html | Yanzhi Chen, weihao sun, Yingzhen Li, Adrian Weller | https://papers.nips.cc/paper_files/paper/2022/hash/0f7e4bb7a35dd4cb426203c91a4bfa10-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18094-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f7e4bb7a35dd4cb426203c91a4bfa10-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f7e4bb7a35dd4cb426203c91a4bfa10-Supplemental-Conference.pdf | The task of infomin learning aims to learn a representation with high utility while being uninformative about a specified target, with the latter achieved by minimising the mutual information between the representation and the target. It has broad applications, ranging from training fair prediction models against prote... | null | null |
Learning to Accelerate Partial Differential Equations via Latent Global Evolution | https://papers.nips.cc/paper_files/paper/2022/hash/0f817dcbad81afb21fb695f1b2e55e44-Abstract-Conference.html | Tailin Wu, Takashi Maruyama, Jure Leskovec | https://papers.nips.cc/paper_files/paper/2022/hash/0f817dcbad81afb21fb695f1b2e55e44-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16827-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f817dcbad81afb21fb695f1b2e55e44-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f817dcbad81afb21fb695f1b2e55e44-Supplemental-Conference.pdf | Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimization problems. However, both classical solvers and recent deep learning-based surrogate models are typ... | null | null |
Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations | https://papers.nips.cc/paper_files/paper/2022/hash/0f94c552e5fe82bc152494985e34bd48-Abstract-Conference.html | Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane, Daniel Brown, Ken Goldberg | https://papers.nips.cc/paper_files/paper/2022/hash/0f94c552e5fe82bc152494985e34bd48-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17159-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f94c552e5fe82bc152494985e34bd48-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f94c552e5fe82bc152494985e34bd48-Supplemental-Conference.zip | Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify sparse reward functions. This sparsity poses new exploration challenges. One common way to address this problem is using demonstrations to provi... | null | null |
Not too little, not too much: a theoretical analysis of graph (over)smoothing | https://papers.nips.cc/paper_files/paper/2022/hash/0f956ca6f667c62e0f71511773c86a59-Abstract-Conference.html | Nicolas Keriven | https://papers.nips.cc/paper_files/paper/2022/hash/0f956ca6f667c62e0f71511773c86a59-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19019-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f956ca6f667c62e0f71511773c86a59-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f956ca6f667c62e0f71511773c86a59-Supplemental-Conference.pdf | We analyze graph smoothing with mean aggregation, where each node successively receives the average of the features of its neighbors. Indeed, it has quickly been observed that Graph Neural Networks (GNNs), which generally follow some variant of Message-Passing (MP) with repeated aggregation, may be subject to the overs... | null | null |
Kernel similarity matching with Hebbian networks | https://papers.nips.cc/paper_files/paper/2022/hash/0f98645119923217a245735c2c4d23f4-Abstract-Conference.html | Kyle Luther, Sebastian Seung | https://papers.nips.cc/paper_files/paper/2022/hash/0f98645119923217a245735c2c4d23f4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18580-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0f98645119923217a245735c2c4d23f4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0f98645119923217a245735c2c4d23f4-Supplemental-Conference.pdf | Recent works have derived neural networks with online correlation-based learning rules to perform \textit{kernel similarity matching}. These works applied existing linear similarity matching algorithms to nonlinear features generated with random Fourier methods. In this paper attempt to perform kernel similarity matchi... | null | null |
HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process | https://papers.nips.cc/paper_files/paper/2022/hash/0fb98d483fa580e0354bcdd3a003a3f3-Abstract-Conference.html | Haoran Wei, Ping Guo, Yangguang Zhu, Chenglong Liu, Peng Wang | https://papers.nips.cc/paper_files/paper/2022/hash/0fb98d483fa580e0354bcdd3a003a3f3-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18521-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0fb98d483fa580e0354bcdd3a003a3f3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0fb98d483fa580e0354bcdd3a003a3f3-Supplemental-Conference.zip | Popular object detection models generate bounding boxes in a different way than we humans. As an example, modern detectors yield object box either upon the regression of its center and width/height (center-guided detector), or by grouping paired estimated corners (corner-guided detector). However, that is not the patte... | null | null |
Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees | https://papers.nips.cc/paper_files/paper/2022/hash/0fd489e5e393f61b355be86ed4c24a54-Abstract-Conference.html | Andrea Tirinzoni, Matteo Papini, Ahmed Touati, Alessandro Lazaric, Matteo Pirotta | https://papers.nips.cc/paper_files/paper/2022/hash/0fd489e5e393f61b355be86ed4c24a54-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18664-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0fd489e5e393f61b355be86ed4c24a54-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0fd489e5e393f61b355be86ed4c24a54-Supplemental-Conference.zip | We study the problem of representation learning in stochastic contextual linear bandits. While the primary concern in this domain is usually to find \textit{realizable} representations (i.e., those that allow predicting the reward function at any context-action pair exactly), it has been recently shown that representat... | null | null |
DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes | https://papers.nips.cc/paper_files/paper/2022/hash/0fed4ca757f63257370f456def09d3eb-Abstract-Conference.html | Tianqi Wei, Rana Alkhoury Maroun, Qinghai Guo, Barbara Webb | https://papers.nips.cc/paper_files/paper/2022/hash/0fed4ca757f63257370f456def09d3eb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18501-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0fed4ca757f63257370f456def09d3eb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0fed4ca757f63257370f456def09d3eb-Supplemental-Conference.pdf | Neural circuits undergo developmental processes which can be influenced by experience. Here we explore a bio-inspired development process to form the connections in a network used for locality sensitive hashing. The network is a simplified model of the insect mushroom body, which has sparse connections from the input l... | null | null |
Why neural networks find simple solutions: The many regularizers of geometric complexity | https://papers.nips.cc/paper_files/paper/2022/hash/0ff3502bb29570b219967278db150a50-Abstract-Conference.html | Benoit Dherin, Michael Munn, Mihaela Rosca, David Barrett | https://papers.nips.cc/paper_files/paper/2022/hash/0ff3502bb29570b219967278db150a50-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17557-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0ff3502bb29570b219967278db150a50-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0ff3502bb29570b219967278db150a50-Supplemental-Conference.pdf | In many contexts, simpler models are preferable to more complex models and the control of this model complexity is the goal for many methods in machine learning such as regularization, hyperparameter tuning and architecture design. In deep learning, it has been difficult to understand the underlying mechanisms of compl... | null | null |
Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation | https://papers.nips.cc/paper_files/paper/2022/hash/0ff54b4ec4f70b3ae12c8621ca8a49f4-Abstract-Conference.html | Zhouxing Shi, Yihan Wang, Huan Zhang, J. Zico Kolter, Cho-Jui Hsieh | https://papers.nips.cc/paper_files/paper/2022/hash/0ff54b4ec4f70b3ae12c8621ca8a49f4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19367-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0ff54b4ec4f70b3ae12c8621ca8a49f4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0ff54b4ec4f70b3ae12c8621ca8a49f4-Supplemental-Conference.pdf | Lipschitz constants are connected to many properties of neural networks, such as robustness, fairness, and generalization. Existing methods for computing Lipschitz constants either produce relatively loose upper bounds or are limited to small networks. In this paper, we develop an efficient framework for computing the... | null | null |
A Causal Analysis of Harm | https://papers.nips.cc/paper_files/paper/2022/hash/100c1f131893d3b4b34bb8db49bef79f-Abstract-Conference.html | Sander Beckers, Hana Chockler, Joseph Halpern | https://papers.nips.cc/paper_files/paper/2022/hash/100c1f131893d3b4b34bb8db49bef79f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18190-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/100c1f131893d3b4b34bb8db49bef79f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/100c1f131893d3b4b34bb8db49bef79f-Supplemental-Conference.pdf | As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework toaddress when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with with the many examples t... | null | null |
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers | https://papers.nips.cc/paper_files/paper/2022/hash/1022661f3f43406065641f16ce25eafa-Abstract-Conference.html | Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva Dyer | https://papers.nips.cc/paper_files/paper/2022/hash/1022661f3f43406065641f16ce25eafa-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17699-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1022661f3f43406065641f16ce25eafa-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1022661f3f43406065641f16ce25eafa-Supplemental-Conference.pdf | Complex time-varying systems are often studied by abstracting away from the dynamics of individual components to build a model of the population-level dynamics from the start. However, when building a population-level description, it can be easy to lose sight of each individual and how they contribute to the larger pic... | null | null |
Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization | https://papers.nips.cc/paper_files/paper/2022/hash/104f7b25495a0e40e65fb7c7eee37ed9-Abstract-Conference.html | Arman Zharmagambetov, Miguel A. Carreira-Perpinan | https://papers.nips.cc/paper_files/paper/2022/hash/104f7b25495a0e40e65fb7c7eee37ed9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16962-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/104f7b25495a0e40e65fb7c7eee37ed9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/104f7b25495a0e40e65fb7c7eee37ed9-Supplemental-Conference.pdf | Semi-supervised learning seeks to learn a machine learning model when only a small amount of the available data is labeled. The most widespread approach uses a graph prior, which encourages similar instances to have similar predictions. This has been very successful with models ranging from kernel machines to neural ne... | null | null |
Riemannian Score-Based Generative Modelling | https://papers.nips.cc/paper_files/paper/2022/hash/105112d52254f86d5854f3da734a52b4-Abstract-Conference.html | Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet | https://papers.nips.cc/paper_files/paper/2022/hash/105112d52254f86d5854f3da734a52b4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17223-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/105112d52254f86d5854f3da734a52b4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/105112d52254f86d5854f3da734a52b4-Supplemental-Conference.pdf | Score-based generative models (SGMs) are a powerful class of generative models that exhibit remarkable empirical performance.Score-based generative modelling (SGM) consists of a noising'' stage, whereby a diffusion is used to gradually add Gaussian noise to data, and a generative model, which entails adenoising'' proce... | null | null |
Intra-agent speech permits zero-shot task acquisition | https://papers.nips.cc/paper_files/paper/2022/hash/1074541383db5ef12d6ac66d2f8e8d34-Abstract-Conference.html | Chen Yan, Federico Carnevale, Petko I Georgiev, Adam Santoro, Aurelia Guy, Alistair Muldal, Chia-Chun Hung, Joshua Abramson, Timothy Lillicrap, Gregory Wayne | https://papers.nips.cc/paper_files/paper/2022/hash/1074541383db5ef12d6ac66d2f8e8d34-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18473-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1074541383db5ef12d6ac66d2f8e8d34-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1074541383db5ef12d6ac66d2f8e8d34-Supplemental-Conference.zip | Human language learners are exposed to a trickle of informative, context-sensitive language, but a flood of raw sensory data. Through both social language use and internal processes of rehearsal and practice, language learners are able to build high-level, semantic representations that explain their perceptions. Here, ... | null | null |
Free Probability for predicting the performance of feed-forward fully connected neural networks | https://papers.nips.cc/paper_files/paper/2022/hash/10826a1a80f816ea98d559d7c7a97973-Abstract-Conference.html | Reda CHHAIBI, Tariq Daouda, Ezechiel Kahn | https://papers.nips.cc/paper_files/paper/2022/hash/10826a1a80f816ea98d559d7c7a97973-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19123-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/10826a1a80f816ea98d559d7c7a97973-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/10826a1a80f816ea98d559d7c7a97973-Supplemental-Conference.pdf | Gradient descent during the learning process of a neural network can be subject to many instabilities. The spectral density of the Jacobian is a key component for analyzing stability. Following the works of Pennington et al., such Jacobians are modeled using free multiplicative convolutions from Free Probability Theory... | null | null |
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm | https://papers.nips.cc/paper_files/paper/2022/hash/109cf25cbc36037deecdbeabfa199956-Abstract-Conference.html | Shilong Bao, Qianqian Xu, Zhiyong Yang, Yuan He, Xiaochun Cao, Qingming Huang | https://papers.nips.cc/paper_files/paper/2022/hash/109cf25cbc36037deecdbeabfa199956-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17550-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/109cf25cbc36037deecdbeabfa199956-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/109cf25cbc36037deecdbeabfa199956-Supplemental-Conference.pdf | Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging... | null | null |
Open-Ended Reinforcement Learning with Neural Reward Functions | https://papers.nips.cc/paper_files/paper/2022/hash/10a6bdcabbd5a3d36b760daa295f63c1-Abstract-Conference.html | Robert Meier, Asier Mujika | https://papers.nips.cc/paper_files/paper/2022/hash/10a6bdcabbd5a3d36b760daa295f63c1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19441-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/10a6bdcabbd5a3d36b760daa295f63c1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/10a6bdcabbd5a3d36b760daa295f63c1-Supplemental-Conference.zip | Inspired by the great success of unsupervised learning in Computer Vision and Natural Language Processing, the Reinforcement Learning community has recently started to focus more on unsupervised discovery of skills. Most current approaches, like DIAYN or DADS, optimize some form of mutual information objective. We prop... | null | null |
A Reduction to Binary Approach for Debiasing Multiclass Datasets | https://papers.nips.cc/paper_files/paper/2022/hash/10eaa0aae94b34308e9b3fa7b677cbe1-Abstract-Conference.html | Ibrahim M. Alabdulmohsin, Jessica Schrouff, Sanmi Koyejo | https://papers.nips.cc/paper_files/paper/2022/hash/10eaa0aae94b34308e9b3fa7b677cbe1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18391-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/10eaa0aae94b34308e9b3fa7b677cbe1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/10eaa0aae94b34308e9b3fa7b677cbe1-Supplemental-Conference.pdf | We propose a novel reduction-to-binary (R2B) approach that enforces demographic parity for multiclass classification with non-binary sensitive attributes via a reduction to a sequence of binary debiasing tasks. We prove that R2B satisfies optimality and bias guarantees and demonstrate empirically that it can lead to an... | null | null |
Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks | https://papers.nips.cc/paper_files/paper/2022/hash/112bfcff816203efbb986bc178380ef2-Abstract-Conference.html | Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, Ming Tang | https://papers.nips.cc/paper_files/paper/2022/hash/112bfcff816203efbb986bc178380ef2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17246-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/112bfcff816203efbb986bc178380ef2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/112bfcff816203efbb986bc178380ef2-Supplemental-Conference.zip | Visual tasks vary a lot in their output formats and concerned contents, therefore it is hard to process them with an identical structure. One main obstacle lies in the high-dimensional outputs in object-level visual tasks. In this paper, we propose an object-centric vision framework, Obj2Seq. Obj2Seq takes objects as b... | null | null |
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering | https://papers.nips.cc/paper_files/paper/2022/hash/11332b6b6cf4485b84afadb1352d3a9a-Abstract-Conference.html | Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, Ashwin Kalyan | https://papers.nips.cc/paper_files/paper/2022/hash/11332b6b6cf4485b84afadb1352d3a9a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19254-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11332b6b6cf4485b84afadb1352d3a9a-Supplemental-Conference.zip | When answering a question, humans utilize the information available across different modalities to synthesize a consistent and complete chain of thought (CoT). This process is normally a black box in the case of deep learning models like large-scale language models. Recently, science question benchmarks have been used ... | null | null |
Few-Shot Audio-Visual Learning of Environment Acoustics | https://papers.nips.cc/paper_files/paper/2022/hash/113ae3a9762ca2168f860a8501d6ae25-Abstract-Conference.html | Sagnik Majumder, Changan Chen, Ziad Al-Halah, Kristen Grauman | https://papers.nips.cc/paper_files/paper/2022/hash/113ae3a9762ca2168f860a8501d6ae25-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18512-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/113ae3a9762ca2168f860a8501d6ae25-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/113ae3a9762ca2168f860a8501d6ae25-Supplemental-Conference.pdf | Room impulse response (RIR) functions capture how the surrounding physical environment transforms the sounds heard by a listener, with implications for various applications in AR, VR, and robotics. Whereas traditional methods to estimate RIRs assume dense geometry and/or sound measurements throughout the environment, w... | null | null |
The Phenomenon of Policy Churn | https://papers.nips.cc/paper_files/paper/2022/hash/114292cf3f930ba157ed33f66997fee2-Abstract-Conference.html | Tom Schaul, Andre Barreto, John Quan, Georg Ostrovski | https://papers.nips.cc/paper_files/paper/2022/hash/114292cf3f930ba157ed33f66997fee2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18072-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/114292cf3f930ba157ed33f66997fee2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/114292cf3f930ba157ed33f66997fee2-Supplemental-Conference.pdf | We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly rapid pace, changing the greedy action in a large fraction of states within a handful of learning updates (in a typical deep RL set-up such ... | null | null |
Molecule Generation by Principal Subgraph Mining and Assembling | https://papers.nips.cc/paper_files/paper/2022/hash/1160792eab11de2bbaf9e71fce191e8c-Abstract-Conference.html | Xiangzhe Kong, Wenbing Huang, Zhixing Tan, Yang Liu | https://papers.nips.cc/paper_files/paper/2022/hash/1160792eab11de2bbaf9e71fce191e8c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17603-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1160792eab11de2bbaf9e71fce191e8c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1160792eab11de2bbaf9e71fce191e8c-Supplemental-Conference.pdf | Molecule generation is central to a variety of applications. Current attention has been paid to approaching the generation task as subgraph prediction and assembling. Nevertheless, these methods usually rely on hand-crafted or external subgraph construction, and the subgraph assembling depends solely on local arrangeme... | null | null |
Implicit Neural Representations with Levels-of-Experts | https://papers.nips.cc/paper_files/paper/2022/hash/1165af8b913fb836c6280b42d6e0084f-Abstract-Conference.html | Zekun Hao, Arun Mallya, Serge Belongie, Ming-Yu Liu | https://papers.nips.cc/paper_files/paper/2022/hash/1165af8b913fb836c6280b42d6e0084f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18716-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1165af8b913fb836c6280b42d6e0084f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1165af8b913fb836c6280b42d6e0084f-Supplemental-Conference.pdf | Coordinate-based networks, usually in the forms of MLPs, have been successfully applied to the task of predicting high-frequency but low-dimensional signals using coordinate inputs. To scale them to model large-scale signals, previous works resort to hybrid representations, combining a coordinate-based network with a g... | null | null |
Planning for Sample Efficient Imitation Learning | https://papers.nips.cc/paper_files/paper/2022/hash/11715d433f6f8b9106baae0df023deb3-Abstract-Conference.html | Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao | https://papers.nips.cc/paper_files/paper/2022/hash/11715d433f6f8b9106baae0df023deb3-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17917-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11715d433f6f8b9106baae0df023deb3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11715d433f6f8b9106baae0df023deb3-Supplemental-Conference.zip | Imitation learning is a class of promising policy learning algorithms that is free from many practical issues with reinforcement learning, such as the reward design issue and the exploration hardness. However, the current imitation algorithm struggles to achieve both high performance and high in-environment sample effi... | null | null |
Concept Activation Regions: A Generalized Framework For Concept-Based Explanations | https://papers.nips.cc/paper_files/paper/2022/hash/11a7f429d75f9f8c6e9c630aeb6524b5-Abstract-Conference.html | Jonathan Crabbé, Mihaela van der Schaar | https://papers.nips.cc/paper_files/paper/2022/hash/11a7f429d75f9f8c6e9c630aeb6524b5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18246-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11a7f429d75f9f8c6e9c630aeb6524b5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11a7f429d75f9f8c6e9c630aeb6524b5-Supplemental-Conference.zip | Concept-based explanations permit to understand the predictions of a deep neural network (DNN) through the lens of concepts specified by users. Existing methods assume that the examples illustrating a concept are mapped in a fixed direction of the DNN's latent space. When this holds true, the concept can be represented... | null | null |
Towards Safe Reinforcement Learning with a Safety Editor Policy | https://papers.nips.cc/paper_files/paper/2022/hash/11afefdd848d1bc9ac9f1604d9f45817-Abstract-Conference.html | Haonan Yu, Wei Xu, Haichao Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/11afefdd848d1bc9ac9f1604d9f45817-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16850-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11afefdd848d1bc9ac9f1604d9f45817-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11afefdd848d1bc9ac9f1604d9f45817-Supplemental-Conference.pdf | We consider the safe reinforcement learning (RL) problem of maximizing utility with extremely low constraint violation rates. Assuming no prior knowledge or pre-training of the environment safety model given a task, an agent has to learn, via exploration, which states and actions are safe. A popular approach in this li... | null | null |
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty | https://papers.nips.cc/paper_files/paper/2022/hash/11b3ae28275461741026c46c0c786711-Abstract-Conference.html | Jaehoon Oh, Sungnyun Kim, Namgyu Ho, Jin-Hwa Kim, Hwanjun Song, Se-Young Yun | https://papers.nips.cc/paper_files/paper/2022/hash/11b3ae28275461741026c46c0c786711-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18966-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11b3ae28275461741026c46c0c786711-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11b3ae28275461741026c46c0c786711-Supplemental-Conference.pdf | Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for handling large differences between the source and target domains--an important concern in real-world scenarios. To overcome these large differences, recent works have considered exploiting small-scale unlabeled data from the target domain during... | null | null |
Sustainable Online Reinforcement Learning for Auto-bidding | https://papers.nips.cc/paper_files/paper/2022/hash/11faf17bf7e5412d9cded369f97db23d-Abstract-Conference.html | Zhiyu Mou, Yusen Huo, Rongquan Bai, Mingzhou Xie, Chuan Yu, Jian Xu, Bo Zheng | https://papers.nips.cc/paper_files/paper/2022/hash/11faf17bf7e5412d9cded369f97db23d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17544-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11faf17bf7e5412d9cded369f97db23d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11faf17bf7e5412d9cded369f97db23d-Supplemental-Conference.pdf | Recently, auto-bidding technique has become an essential tool to increase the revenue of advertisers. Facing the complex and ever-changing bidding environments in the real-world advertising system (RAS), state-of-the-art auto-bidding policies usually leverage reinforcement learning (RL) algorithms to generate real-time... | null | null |
Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs | https://papers.nips.cc/paper_files/paper/2022/hash/11fc8c98b46d4cbdfe8157267228f7d7-Abstract-Conference.html | Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, Jifeng Dai | https://papers.nips.cc/paper_files/paper/2022/hash/11fc8c98b46d4cbdfe8157267228f7d7-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18730-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/11fc8c98b46d4cbdfe8157267228f7d7-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/11fc8c98b46d4cbdfe8157267228f7d7-Supplemental-Conference.pdf | To build an artificial neural network like the biological intelligence system, recent works have unified numerous tasks into a generalist model, which can process various tasks with shared parameters and do not have any task-specific modules. While generalist models achieve promising results on various benchmarks, they... | null | null |
Improved Coresets for Euclidean $k$-Means | https://papers.nips.cc/paper_files/paper/2022/hash/120c9ab5c58ba0fa9dd3a22ace1de245-Abstract-Conference.html | Vincent Cohen-Addad, Kasper Green Larsen, David Saulpic, Chris Schwiegelshohn, Omar Ali Sheikh-Omar | https://papers.nips.cc/paper_files/paper/2022/hash/120c9ab5c58ba0fa9dd3a22ace1de245-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17930-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/120c9ab5c58ba0fa9dd3a22ace1de245-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/120c9ab5c58ba0fa9dd3a22ace1de245-Supplemental-Conference.pdf | Given a set of $n$ points in $d$ dimensions, the Euclidean $k$-means problem (resp. Euclidean $k$-median) consists of finding $k$ centers such that the sum of squared distances (resp. sum of distances) from every point to its closest center is minimized. The arguably most popular way of dealing with this problem in the... | null | null |
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning | https://papers.nips.cc/paper_files/paper/2022/hash/12143893d9d37c3569dda800b95cabd9-Abstract-Conference.html | Zhijie Deng, Feng Zhou, Jun Zhu | https://papers.nips.cc/paper_files/paper/2022/hash/12143893d9d37c3569dda800b95cabd9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18624-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/12143893d9d37c3569dda800b95cabd9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/12143893d9d37c3569dda800b95cabd9-Supplemental-Conference.pdf | Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks. The generalized Gauss-Newton (GGN) approximation is typically introduced to improve their tractability. However, LA and LLA are still confronted with non-trivial ineff... | null | null |
Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures | https://papers.nips.cc/paper_files/paper/2022/hash/12202970782399ee67981dc5269c3b8a-Abstract-Conference.html | Emmanuel Abbe, Samy Bengio, Elisabetta Cornacchia, Jon Kleinberg, Aryo Lotfi, Maithra Raghu, Chiyuan Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/12202970782399ee67981dc5269c3b8a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17019-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/12202970782399ee67981dc5269c3b8a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/12202970782399ee67981dc5269c3b8a-Supplemental-Conference.pdf | This paper considers the Pointer Value Retrieval (PVR) benchmark introduced in [ZRKB21], where a `reasoning' function acts on a string of digits to produce the label. More generally, the paper considers the learning of logical functions with gradient descent (GD) on neural networks. It is first shown that in order to l... | null | null |
Using Partial Monotonicity in Submodular Maximization | https://papers.nips.cc/paper_files/paper/2022/hash/1227a7a80529ecfe033065b9fcc5a042-Abstract-Conference.html | Loay Mualem, Moran Feldman | https://papers.nips.cc/paper_files/paper/2022/hash/1227a7a80529ecfe033065b9fcc5a042-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18201-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1227a7a80529ecfe033065b9fcc5a042-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1227a7a80529ecfe033065b9fcc5a042-Supplemental-Conference.pdf | Over the last two decades, submodular function maximization has been the workhorse of many discrete optimization problems in machine learning applications. Traditionally, the study of submodular functions was based on binary function properties, but recent works began to consider continuous function properties such as ... | null | null |
Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments | https://papers.nips.cc/paper_files/paper/2022/hash/122f45f4d451617ac87adf7024ee14cd-Abstract-Conference.html | Desik Rengarajan, Sapana Chaudhary, Jaewon Kim, Dileep Kalathil, Srinivas Shakkottai | https://papers.nips.cc/paper_files/paper/2022/hash/122f45f4d451617ac87adf7024ee14cd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17632-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/122f45f4d451617ac87adf7024ee14cd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/122f45f4d451617ac87adf7024ee14cd-Supplemental-Conference.pdf | Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy. The meta-policy, when adapted over only a small (or just a single) number of steps, is able to perform near-optimally on a new, related task. However, a major challenge to... | null | null |
Riemannian Diffusion Models | https://papers.nips.cc/paper_files/paper/2022/hash/123d3e814e257e0781e5d328232ead9b-Abstract-Conference.html | Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, Aaron C. Courville | https://papers.nips.cc/paper_files/paper/2022/hash/123d3e814e257e0781e5d328232ead9b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18400-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/123d3e814e257e0781e5d328232ead9b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/123d3e814e257e0781e5d328232ead9b-Supplemental-Conference.pdf | Diffusion models are recent state-of-the-art methods for image generation and likelihood estimation. In this work, we generalize continuous-time diffusion models to arbitrary Riemannian manifolds and derive a variational framework for likelihood estimation. Computationally, we propose new methods for computing the Riem... | null | null |
Training and Inference on Any-Order Autoregressive Models the Right Way | https://papers.nips.cc/paper_files/paper/2022/hash/123fd8a56501194823c8e0dca00733df-Abstract-Conference.html | Andy Shih, Dorsa Sadigh, Stefano Ermon | https://papers.nips.cc/paper_files/paper/2022/hash/123fd8a56501194823c8e0dca00733df-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17110-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/123fd8a56501194823c8e0dca00733df-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/123fd8a56501194823c8e0dca00733df-Supplemental-Conference.pdf | Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting. In recent years, the family of Any-Order Autoregressive Models (AO-ARMs) -- closely related to popular models such as BERT and XLNet -- ... | null | null |
Lazy and Fast Greedy MAP Inference for Determinantal Point Process | https://papers.nips.cc/paper_files/paper/2022/hash/127179162bfe4c422325ee7d05ad9cd8-Abstract-Conference.html | Shinichi Hemmi, Taihei Oki, Shinsaku Sakaue, Kaito Fujii, Satoru Iwata | https://papers.nips.cc/paper_files/paper/2022/hash/127179162bfe4c422325ee7d05ad9cd8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19391-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/127179162bfe4c422325ee7d05ad9cd8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/127179162bfe4c422325ee7d05ad9cd8-Supplemental-Conference.zip | The maximum a posteriori (MAP) inference for determinantal point processes (DPPs) is crucial for selecting diverse items in many machine learning applications. Although DPP MAP inference is NP-hard, the greedy algorithm often finds high-quality solutions, and many researchers have studied its efficient implementation. ... | null | null |
Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers | https://papers.nips.cc/paper_files/paper/2022/hash/129033c7c08be683059559e8d6bfd460-Abstract-Conference.html | Sejun Park, Umut Simsekli, Murat A. Erdogdu | https://papers.nips.cc/paper_files/paper/2022/hash/129033c7c08be683059559e8d6bfd460-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19210-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/129033c7c08be683059559e8d6bfd460-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/129033c7c08be683059559e8d6bfd460-Supplemental-Conference.pdf | In this paper, we propose a new covering technique localized for the trajectories of SGD. This localization provides an algorithm-specific complexity measured by the covering number, which can have dimension-independent cardinality in contrast to standard uniform covering arguments that result in exponential dimension ... | null | null |
Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant | https://papers.nips.cc/paper_files/paper/2022/hash/12d286282e1be5431ea05262a21f415c-Abstract-Conference.html | Ying Jin, Jiaqi Wang, Dahua Lin | https://papers.nips.cc/paper_files/paper/2022/hash/12d286282e1be5431ea05262a21f415c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18102-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/12d286282e1be5431ea05262a21f415c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/12d286282e1be5431ea05262a21f415c-Supplemental-Conference.zip | Semi-Supervised Semantic Segmentation aims at training the segmentation model with limited labeled data and a large amount of unlabeled data. To effectively leverage the unlabeled data, pseudo labeling, along with the teacher-student framework, is widely adopted in semi-supervised semantic segmentation. Though proved t... | null | null |
Anytime-Valid Inference For Multinomial Count Data | https://papers.nips.cc/paper_files/paper/2022/hash/12f3bd5d2b7d93eadc1bf508a0872dc2-Abstract-Conference.html | Michael Lindon, Alan Malek | https://papers.nips.cc/paper_files/paper/2022/hash/12f3bd5d2b7d93eadc1bf508a0872dc2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18191-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/12f3bd5d2b7d93eadc1bf508a0872dc2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/12f3bd5d2b7d93eadc1bf508a0872dc2-Supplemental-Conference.pdf | Many experiments compare count outcomes among treatment groups. Examples include the number of successful signups in conversion rate experiments or the number of errors produced by software versions in canary tests. Observations typically arrive in a sequence and practitioners wish to continuously monitor their experim... | null | null |
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods | https://papers.nips.cc/paper_files/paper/2022/hash/13113e938f2957891c0c5e8df811dd01-Abstract-Conference.html | Julien Colin, Thomas FEL, Remi Cadene, Thomas Serre | https://papers.nips.cc/paper_files/paper/2022/hash/13113e938f2957891c0c5e8df811dd01-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17827-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/13113e938f2957891c0c5e8df811dd01-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/13113e938f2957891c0c5e8df811dd01-Supplemental-Conference.pdf | A multitude of explainability methods has been described to try to help users better understand how modern AI systems make decisions. However, most performance metrics developed to evaluate these methods have remained largely theoretical -- without much consideration for the human end-user. In particular, it is not yet... | null | null |
S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces | https://papers.nips.cc/paper_files/paper/2022/hash/13388efc819c09564c66ab2dc8463809-Abstract-Conference.html | Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, Christopher Ré | https://papers.nips.cc/paper_files/paper/2022/hash/13388efc819c09564c66ab2dc8463809-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17382-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/13388efc819c09564c66ab2dc8463809-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/13388efc819c09564c66ab2dc8463809-Supplemental-Conference.pdf | Visual data such as images and videos are typically modeled as discretizations of inherently continuous, multidimensional signals. Existing continuous-signal models attempt to exploit this fact by modeling the underlying signals of visual (e.g., image) data directly. However, these models have not yet been able to ach... | null | null |
Understanding Programmatic Weak Supervision via Source-aware Influence Function | https://papers.nips.cc/paper_files/paper/2022/hash/1343edb2739a61a6e20bd8764e814b50-Abstract-Conference.html | Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander J. Ratner | https://papers.nips.cc/paper_files/paper/2022/hash/1343edb2739a61a6e20bd8764e814b50-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19270-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1343edb2739a61a6e20bd8764e814b50-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1343edb2739a61a6e20bd8764e814b50-Supplemental-Conference.pdf | Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model. With its increasing popularity, it is critical to have some tool for users to understand the influence of each component (\eg, the source... | null | null |
Approximation with CNNs in Sobolev Space: with Applications to Classification | https://papers.nips.cc/paper_files/paper/2022/hash/136302ea7874e2ff96d517f9a8eb0a35-Abstract-Conference.html | Guohao Shen, Yuling Jiao, Yuanyuan Lin, Jian Huang | https://papers.nips.cc/paper_files/paper/2022/hash/136302ea7874e2ff96d517f9a8eb0a35-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18293-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/136302ea7874e2ff96d517f9a8eb0a35-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/136302ea7874e2ff96d517f9a8eb0a35-Supplemental-Conference.pdf | We derive a novel approximation error bound with explicit prefactor for Sobolev-regular functions using deep convolutional neural networks (CNNs). The bound is non-asymptotic in terms of the network depth and filter lengths, in a rather flexible way. For Sobolev-regular functions which can be embedded into the H\"older... | null | null |
Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search | https://papers.nips.cc/paper_files/paper/2022/hash/136b9a13861308c8948cd308ccd02658-Abstract-Conference.html | Shinsaku Sakaue, Taihei Oki | https://papers.nips.cc/paper_files/paper/2022/hash/136b9a13861308c8948cd308ccd02658-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18550-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/136b9a13861308c8948cd308ccd02658-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/136b9a13861308c8948cd308ccd02658-Supplemental-Conference.pdf | Greedy best-first search (GBFS) and A* search (A*) are popular algorithms for path-finding on large graphs. Both use so-called heuristic functions, which estimate how close a vertex is to the goal. While heuristic functions have been handcrafted using domain knowledge, recent studies demonstrate that learning heuristic... | null | null |
TransTab: Learning Transferable Tabular Transformers Across Tables | https://papers.nips.cc/paper_files/paper/2022/hash/1377f76686d56439a2bd7a91859972f5-Abstract-Conference.html | Zifeng Wang, Jimeng Sun | https://papers.nips.cc/paper_files/paper/2022/hash/1377f76686d56439a2bd7a91859972f5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18211-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1377f76686d56439a2bd7a91859972f5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1377f76686d56439a2bd7a91859972f5-Supplemental-Conference.zip | Tabular data (or tables) are the most widely used data format in machine learning (ML). However, ML models often assume the table structure keeps fixed in training and testing. Before ML modeling, heavy data cleaning is required to merge disparate tables with different columns. This preprocessing often incurs significa... | null | null |
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop | https://papers.nips.cc/paper_files/paper/2022/hash/137cb5dd61b2685bd2623967daee6860-Abstract-Conference.html | Weixia Zhang, Dingquan Li, Xiongkuo Min, Guangtao Zhai, Guodong Guo, Xiaokang Yang, Kede Ma | https://papers.nips.cc/paper_files/paper/2022/hash/137cb5dd61b2685bd2623967daee6860-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17821-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/137cb5dd61b2685bd2623967daee6860-Paper-Conference.pdf | null | No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references. NR-IQA models are extensively studied in computational vision, and are widely used for performance evaluation and perceptual optimization of man-made v... | null | null |
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity | https://papers.nips.cc/paper_files/paper/2022/hash/1385753b9661cd2d9f2cb8958dec985b-Abstract-Conference.html | Mucong Ding, Tahseen Rabbani, Bang An, Evan Wang, Furong Huang | https://papers.nips.cc/paper_files/paper/2022/hash/1385753b9661cd2d9f2cb8958dec985b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17100-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/1385753b9661cd2d9f2cb8958dec985b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/1385753b9661cd2d9f2cb8958dec985b-Supplemental-Conference.pdf | Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification. When scaling up the underlying graphs of GNNs to a larger size, we are forced to either train on the complete graph and keep the full graph adjacency and node embeddings in memory (which is often infeasible) or mini-... | null | null |
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/139ae969f49abd9a113981c1f7fce5ce-Abstract-Conference.html | Ching-Yao Chuang, Stefanie Jegelka | https://papers.nips.cc/paper_files/paper/2022/hash/139ae969f49abd9a113981c1f7fce5ce-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17605-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/139ae969f49abd9a113981c1f7fce5ce-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/139ae969f49abd9a113981c1f7fce5ce-Supplemental-Conference.pdf | Understanding generalization and robustness of machine learning models fundamentally relies on assuming an appropriate metric on the data space. Identifying such a metric is particularly challenging for non-Euclidean data such as graphs. Here, we propose a pseudometric for attributed graphs, the Tree Mover's Distance (... | null | null |
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