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Federated Submodel Optimization for Hot and Cold Data Features | https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html | Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, yanghe feng, Guihai Chen | https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17527-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Supplemental-Conference.pdf | We focus on federated learning in practical recommender systems and natural language processing scenarios. The global model for federated optimization typically contains a large and sparse embedding layer, while each client’s local data tend to interact with part of features, updating only a small submodel with the fea... | null | null |
On Kernelized Multi-Armed Bandits with Constraints | https://papers.nips.cc/paper_files/paper/2022/hash/00295cede6e1600d344b5cd6d9fd4640-Abstract-Conference.html | Xingyu Zhou, Bo Ji | https://papers.nips.cc/paper_files/paper/2022/hash/00295cede6e1600d344b5cd6d9fd4640-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18113-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00295cede6e1600d344b5cd6d9fd4640-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00295cede6e1600d344b5cd6d9fd4640-Supplemental-Conference.pdf | We study a stochastic bandit problem with a general unknown reward function and a general unknown constraint function. Both functions can be non-linear (even non-convex) and are assumed to lie in a reproducing kernel Hilbert space (RKHS) with a bounded norm. This kernelized bandit setup strictly generalizes standard mu... | null | null |
Geometric Order Learning for Rank Estimation | https://papers.nips.cc/paper_files/paper/2022/hash/00358de35a101a372ea0412bed913c86-Abstract-Conference.html | Seon-Ho Lee, Nyeong Ho Shin, Chang-Su Kim | https://papers.nips.cc/paper_files/paper/2022/hash/00358de35a101a372ea0412bed913c86-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17861-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00358de35a101a372ea0412bed913c86-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00358de35a101a372ea0412bed913c86-Supplemental-Conference.zip | A novel approach to rank estimation, called geometric order learning (GOL), is proposed in this paper. First, we construct an embedding space, in which the direction and distance between objects represent order and metric relations between their ranks, by enforcing two geometric constraints: the order constraint compel... | null | null |
Structured Recognition for Generative Models with Explaining Away | https://papers.nips.cc/paper_files/paper/2022/hash/003a96110b7134d678cb675c6aea6c7d-Abstract-Conference.html | Changmin Yu, Hugo Soulat, Neil Burgess, Maneesh Sahani | https://papers.nips.cc/paper_files/paper/2022/hash/003a96110b7134d678cb675c6aea6c7d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17779-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/003a96110b7134d678cb675c6aea6c7d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/003a96110b7134d678cb675c6aea6c7d-Supplemental-Conference.zip | A key goal of unsupervised learning is to go beyond density estimation and sample generation to reveal the structure inherent within observed data. Such structure can be expressed in the pattern of interactions between explanatory latent variables captured through a probabilistic graphical model. Although the learning ... | null | null |
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement | https://papers.nips.cc/paper_files/paper/2022/hash/005413e90d003d13886019607b037f52-Abstract-Conference.html | Cian Naik, Judith Rousseau, Trevor Campbell | https://papers.nips.cc/paper_files/paper/2022/hash/005413e90d003d13886019607b037f52-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19127-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/005413e90d003d13886019607b037f52-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/005413e90d003d13886019607b037f52-Supplemental-Conference.zip | Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensively on the coreset, with results that approximate those on the full data. However, cur... | null | null |
What You See is What You Classify: Black Box Attributions | https://papers.nips.cc/paper_files/paper/2022/hash/0073cc73e1873b35345209b50a3dab66-Abstract-Conference.html | Steven Stalder, Nathanael Perraudin, Radhakrishna Achanta, Fernando Perez-Cruz, Michele Volpi | https://papers.nips.cc/paper_files/paper/2022/hash/0073cc73e1873b35345209b50a3dab66-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19444-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0073cc73e1873b35345209b50a3dab66-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0073cc73e1873b35345209b50a3dab66-Supplemental-Conference.zip | An important step towards explaining deep image classifiers lies in the identification of image regions that contribute to individual class scores in the model's output. However, doing this accurately is a difficult task due to the black-box nature of such networks. Most existing approaches find such attributions eithe... | null | null |
Adaptive Interest for Emphatic Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/008079ec00eec9760ee93af5434ee932-Abstract-Conference.html | Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola | https://papers.nips.cc/paper_files/paper/2022/hash/008079ec00eec9760ee93af5434ee932-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18361-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/008079ec00eec9760ee93af5434ee932-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/008079ec00eec9760ee93af5434ee932-Supplemental-Conference.pdf | Emphatic algorithms have shown great promise in stabilizing and improving reinforcement learning by selectively emphasizing the update rule. Although the emphasis fundamentally depends on an interest function which defines the intrinsic importance of each state, most approaches simply adopt a uniform interest over all ... | null | null |
Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning | https://papers.nips.cc/paper_files/paper/2022/hash/00bb4e415ef117f2dee2fc3b778d806d-Abstract-Conference.html | Dongze Lian, Daquan Zhou, Jiashi Feng, Xinchao Wang | https://papers.nips.cc/paper_files/paper/2022/hash/00bb4e415ef117f2dee2fc3b778d806d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18656-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00bb4e415ef117f2dee2fc3b778d806d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00bb4e415ef117f2dee2fc3b778d806d-Supplemental-Conference.pdf | Existing fine-tuning methods either tune all parameters of the pre-trained model (full fine-tuning), which is not efficient, or only tune the last linear layer (linear probing), which suffers a significant accuracy drop compared to the full fine-tuning. In this paper, we propose a new parameter-efficient fine-tuning me... | null | null |
Zero-Shot Video Question Answering via Frozen Bidirectional Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/00d1f03b87a401b1c7957e0cc785d0bc-Abstract-Conference.html | Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, Cordelia Schmid | https://papers.nips.cc/paper_files/paper/2022/hash/00d1f03b87a401b1c7957e0cc785d0bc-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17847-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/00d1f03b87a401b1c7957e0cc785d0bc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/00d1f03b87a401b1c7957e0cc785d0bc-Supplemental-Conference.zip | Video question answering (VideoQA) is a complex task that requires diverse multi-modal data for training. Manual annotation of question and answers for videos, however, is tedious and prohibits scalability. To tackle this problem, recent methods consider zero-shot settings with no manual annotation of visual question-a... | null | null |
Active Learning with Neural Networks: Insights from Nonparametric Statistics | https://papers.nips.cc/paper_files/paper/2022/hash/01025a4e79355bb37a10ba39605944b5-Abstract-Conference.html | Yinglun Zhu, Robert Nowak | https://papers.nips.cc/paper_files/paper/2022/hash/01025a4e79355bb37a10ba39605944b5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17239-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01025a4e79355bb37a10ba39605944b5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01025a4e79355bb37a10ba39605944b5-Supplemental-Conference.pdf | Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, howeve... | null | null |
IM-Loss: Information Maximization Loss for Spiking Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/010c5ba0cafc743fece8be02e7adb8dd-Abstract-Conference.html | Yufei Guo, Yuanpei Chen, Liwen Zhang, Xiaode Liu, Yinglei Wang, Xuhui Huang, Zhe Ma | https://papers.nips.cc/paper_files/paper/2022/hash/010c5ba0cafc743fece8be02e7adb8dd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17524-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/010c5ba0cafc743fece8be02e7adb8dd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/010c5ba0cafc743fece8be02e7adb8dd-Supplemental-Conference.pdf | Spiking Neural Network (SNN), recognized as a type of biologically plausible architecture, has recently drawn much research attention. It transmits information by $0/1$ spikes. This bio-mimetic mechanism of SNN demonstrates extreme energy efficiency since it avoids any multiplications on neuromorphic hardware. However,... | null | null |
Using natural language and program abstractions to instill human inductive biases in machines | https://papers.nips.cc/paper_files/paper/2022/hash/0113ef4642264adc2e6924a3cbbdf532-Abstract-Conference.html | Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y Hu, Robert Hawkins, Jonathan D Cohen, nathaniel daw, Karthik Narasimhan, Tom Griffiths | https://papers.nips.cc/paper_files/paper/2022/hash/0113ef4642264adc2e6924a3cbbdf532-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17141-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0113ef4642264adc2e6924a3cbbdf532-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0113ef4642264adc2e6924a3cbbdf532-Supplemental-Conference.zip | Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on ... | null | null |
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits | https://papers.nips.cc/paper_files/paper/2022/hash/01c4593d60a020fed5607944330106b1-Abstract-Conference.html | Ruibo Liu, Chenyan Jia, Ge Zhang, Ziyu Zhuang, Tony Liu, Soroush Vosoughi | https://papers.nips.cc/paper_files/paper/2022/hash/01c4593d60a020fed5607944330106b1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16643-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01c4593d60a020fed5607944330106b1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01c4593d60a020fed5607944330106b1-Supplemental-Conference.pdf | We present Second Thoughts, a new learning paradigm that enables language models (LMs) to re-align with human values. By modeling the chain-of-edits between value-unaligned and value-aligned text, with LM fine-tuning and additional refinement through reinforcement learning, Second Thoughts not only achieves superior pe... | null | null |
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery | https://papers.nips.cc/paper_files/paper/2022/hash/01c561df365429f33fcd7a7faa44c985-Abstract-Conference.html | Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David Lobell, Stefano Ermon | https://papers.nips.cc/paper_files/paper/2022/hash/01c561df365429f33fcd7a7faa44c985-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16878-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01c561df365429f33fcd7a7faa44c985-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01c561df365429f33fcd7a7faa44c985-Supplemental-Conference.pdf | Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery presents significant opportunities as unlabelled data is plentiful and the inherent temporal and multi-spectral structure provides avenues to fu... | null | null |
On Sample Optimality in Personalized Collaborative and Federated Learning | https://papers.nips.cc/paper_files/paper/2022/hash/01cea7793f3c68af2e4989fc66bf8fb0-Abstract-Conference.html | Mathieu Even, Laurent Massoulié, Kevin Scaman | https://papers.nips.cc/paper_files/paper/2022/hash/01cea7793f3c68af2e4989fc66bf8fb0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17923-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01cea7793f3c68af2e4989fc66bf8fb0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01cea7793f3c68af2e4989fc66bf8fb0-Supplemental-Conference.pdf | In personalized federated learning, each member of a potentially large set of agents aims to train a model minimizing its loss function averaged over its local data distribution. We study this problem under the lens of stochastic optimization, focusing on a scenario with a large number of agents, that each possess very... | null | null |
Offline Multi-Agent Reinforcement Learning with Knowledge Distillation | https://papers.nips.cc/paper_files/paper/2022/hash/01d78b294d80491fecddea897cf03642-Abstract-Conference.html | Wei-Cheng Tseng, Tsun-Hsuan Johnson Wang, Yen-Chen Lin, Phillip Isola | https://papers.nips.cc/paper_files/paper/2022/hash/01d78b294d80491fecddea897cf03642-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17221-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01d78b294d80491fecddea897cf03642-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01d78b294d80491fecddea897cf03642-Supplemental-Conference.pdf | We introduce an offline multi-agent reinforcement learning ( offline MARL) framework that utilizes previously collected data without additional online data collection. Our method reformulates offline MARL as a sequence modeling problem and thus builds on top of the simplicity and scalability of the Transformer architec... | null | null |
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks | https://papers.nips.cc/paper_files/paper/2022/hash/01db36a646c07c64dd39a92b4eceb417-Abstract-Conference.html | Shuoguang Yang, Xuezhou Zhang, Mengdi Wang | https://papers.nips.cc/paper_files/paper/2022/hash/01db36a646c07c64dd39a92b4eceb417-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18262-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01db36a646c07c64dd39a92b4eceb417-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01db36a646c07c64dd39a92b4eceb417-Supplemental-Conference.pdf | Bilevel optimization have gained growing interests, with numerous applications found in meta learning, minimax games, reinforcement learning, and nested composition optimization. This paper studies the problem of decentralized distributed bilevel optimization over a network where agents can only communicate with neighb... | null | null |
Conditional Meta-Learning of Linear Representations | https://papers.nips.cc/paper_files/paper/2022/hash/01ecd39ca49ddecc5729ca996304781b-Abstract-Conference.html | Giulia Denevi, Massimiliano Pontil, Carlo Ciliberto | https://papers.nips.cc/paper_files/paper/2022/hash/01ecd39ca49ddecc5729ca996304781b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19290-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/01ecd39ca49ddecc5729ca996304781b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/01ecd39ca49ddecc5729ca996304781b-Supplemental-Conference.zip | Standard meta-learning for representation learning aims to find a common representation to be shared across multiple tasks. The effectiveness of these methods is often limited when the nuances of the tasks’ distribution cannot be captured by a single representation. In this work we overcome this issue by inferring a co... | null | null |
Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources | https://papers.nips.cc/paper_files/paper/2022/hash/0206c1c20a18915da23df5e61966fc6a-Abstract-Conference.html | Peter Lippmann, Enrique Fita Sanmartín, Fred A. Hamprecht | https://papers.nips.cc/paper_files/paper/2022/hash/0206c1c20a18915da23df5e61966fc6a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19313-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0206c1c20a18915da23df5e61966fc6a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0206c1c20a18915da23df5e61966fc6a-Supplemental-Conference.pdf | Branched optimal transport (BOT) is a generalization of optimal transport in which transportation costs along an edge are subadditive. This subadditivity models an increase in transport efficiency when shipping mass along the same route, favoring branched transportation networks. We here study the NP-hard optimization ... | null | null |
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis | https://papers.nips.cc/paper_files/paper/2022/hash/0207c9ea9faf66c6e892c3fa3c167b75-Abstract-Conference.html | Shichong Peng, Seyed Alireza Moazenipourasil, Ke Li | https://papers.nips.cc/paper_files/paper/2022/hash/0207c9ea9faf66c6e892c3fa3c167b75-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18700-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0207c9ea9faf66c6e892c3fa3c167b75-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0207c9ea9faf66c6e892c3fa3c167b75-Supplemental-Conference.zip | A persistent challenge in conditional image synthesis has been to generate diverse output images from the same input image despite only one output image being observed per input image. GAN-based methods are prone to mode collapse, which leads to low diversity. To get around this, we leverage Implicit Maximum Likelihood... | null | null |
Active Ranking without Strong Stochastic Transitivity | https://papers.nips.cc/paper_files/paper/2022/hash/020e313d40a7c060ed07a10cef287750-Abstract-Conference.html | Hao Lou, Tao Jin, Yue Wu, Pan Xu, Quanquan Gu, Farzad Farnoud | https://papers.nips.cc/paper_files/paper/2022/hash/020e313d40a7c060ed07a10cef287750-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16985-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/020e313d40a7c060ed07a10cef287750-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/020e313d40a7c060ed07a10cef287750-Supplemental-Conference.pdf | Ranking from noisy comparisons is of great practical interest in machine learning. In this paper, we consider the problem of recovering the exact full ranking for a list of items under ranking models that do *not* assume the Strong Stochastic Transitivity property. We propose a $$\delta$$-correct algorithm, Probe-Rank,... | null | null |
Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression | https://papers.nips.cc/paper_files/paper/2022/hash/022a39052abf9ca467e268923057dfc0-Abstract-Conference.html | Jason Yecheng Ma, Jason Yan, Dinesh Jayaraman, Osbert Bastani | https://papers.nips.cc/paper_files/paper/2022/hash/022a39052abf9ca467e268923057dfc0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16796-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/022a39052abf9ca467e268923057dfc0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/022a39052abf9ca467e268923057dfc0-Supplemental-Conference.pdf | Offline goal-conditioned reinforcement learning (GCRL) promises general-purpose skill learning in the form of reaching diverse goals from purely offline datasets. We propose $\textbf{Go}$al-conditioned $f$-$\textbf{A}$dvantage $\textbf{R}$egression (GoFAR), a novel regression-based offline GCRL algorithm derived from a... | null | null |
Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain | https://papers.nips.cc/paper_files/paper/2022/hash/022abe84083d235f7572ca5cba24c51c-Abstract-Conference.html | Yiting Chen, Qibing Ren, Junchi Yan | https://papers.nips.cc/paper_files/paper/2022/hash/022abe84083d235f7572ca5cba24c51c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19148-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/022abe84083d235f7572ca5cba24c51c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/022abe84083d235f7572ca5cba24c51c-Supplemental-Conference.pdf | The existence of adversarial examples poses concerns for the robustness of convolutional neural networks (CNN), for which a popular hypothesis is about the frequency bias phenomenon: CNNs rely more on high-frequency components (HFC) for classification than humans, which causes the brittleness of CNNs. However, most pre... | null | null |
Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/023d94f44110b9a3c62329beec739772-Abstract-Conference.html | Zhun Zhong, Yuyang Zhao, Gim Hee Lee, Nicu Sebe | https://papers.nips.cc/paper_files/paper/2022/hash/023d94f44110b9a3c62329beec739772-Abstract-Conference.html | NIPS 2022 | null | null | null | null | null | null |
Fully Sparse 3D Object Detection | https://papers.nips.cc/paper_files/paper/2022/hash/0247fa3c511bbc415c8b768ee7b32f9e-Abstract-Conference.html | Lue Fan, Feng Wang, Naiyan Wang, ZHAO-XIANG ZHANG | https://papers.nips.cc/paper_files/paper/2022/hash/0247fa3c511bbc415c8b768ee7b32f9e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17797-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0247fa3c511bbc415c8b768ee7b32f9e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0247fa3c511bbc415c8b768ee7b32f9e-Supplemental-Conference.zip | As the perception range of LiDAR increases, LiDAR-based 3D object detection becomes a dominant task in the long-range perception task of autonomous driving. The mainstream 3D object detectors usually build dense feature maps in the network backbone and prediction head. However, the computational and spatial costs on th... | null | null |
Diffusion Visual Counterfactual Explanations | https://papers.nips.cc/paper_files/paper/2022/hash/025f7165a452e7d0b57f1397fed3b0fd-Abstract-Conference.html | Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein | https://papers.nips.cc/paper_files/paper/2022/hash/025f7165a452e7d0b57f1397fed3b0fd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17480-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/025f7165a452e7d0b57f1397fed3b0fd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/025f7165a452e7d0b57f1397fed3b0fd-Supplemental-Conference.zip | Visual Counterfactual Explanations (VCEs) are an important tool to understand the decisions of an image classifier. They are “small” but “realistic” semantic changes of the image changing the classifier decision. Current approaches for the generation of VCEs are restricted to adversarially robust models and often conta... | null | null |
Recurrent Video Restoration Transformer with Guided Deformable Attention | https://papers.nips.cc/paper_files/paper/2022/hash/02687e7b22abc64e651be8da74ec610e-Abstract-Conference.html | Jingyun Liang, Yuchen Fan, Xiaoyu Xiang, Rakesh Ranjan, Eddy Ilg, Simon Green, Jiezhang Cao, Kai Zhang, Radu Timofte, Luc V Gool | https://papers.nips.cc/paper_files/paper/2022/hash/02687e7b22abc64e651be8da74ec610e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17283-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/02687e7b22abc64e651be8da74ec610e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/02687e7b22abc64e651be8da74ec610e-Supplemental-Conference.pdf | Video restoration aims at restoring multiple high-quality frames from multiple low-quality frames. Existing video restoration methods generally fall into two extreme cases, i.e., they either restore all frames in parallel or restore the video frame by frame in a recurrent way, which would result in different merits and... | null | null |
A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction | https://papers.nips.cc/paper_files/paper/2022/hash/026aff87942ce636ada884d934cde0ae-Abstract-Conference.html | Boxiang Wang, Archer Yang | https://papers.nips.cc/paper_files/paper/2022/hash/026aff87942ce636ada884d934cde0ae-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17111-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/026aff87942ce636ada884d934cde0ae-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/026aff87942ce636ada884d934cde0ae-Supplemental-Conference.pdf | We propose a consolidated cross-validation (CV) algorithm for training and tuning the support vector machines (SVM) on reproducing kernel Hilbert spaces. Our consolidated CV algorithm utilizes a recently proposed exact leave-one-out formula for the SVM and accelerates the SVM computation via a data reduction strategy. ... | null | null |
On-Demand Sampling: Learning Optimally from Multiple Distributions | https://papers.nips.cc/paper_files/paper/2022/hash/02917acec264a52a729b99d9bc857909-Abstract-Conference.html | Nika Haghtalab, Michael Jordan, Eric Zhao | https://papers.nips.cc/paper_files/paper/2022/hash/02917acec264a52a729b99d9bc857909-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18509-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/02917acec264a52a729b99d9bc857909-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/02917acec264a52a729b99d9bc857909-Supplemental-Conference.zip | Societal and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collaborative [Blum et al. 2017], group distributionally robust [Sagawa et al. 2019], and fair federated learning [Mohri et al. 2019]. In each o... | null | null |
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays | https://papers.nips.cc/paper_files/paper/2022/hash/029df12a9363313c3e41047844ecad94-Abstract-Conference.html | Konstantin Mishchenko, Francis Bach, Mathieu Even, Blake E. Woodworth | https://papers.nips.cc/paper_files/paper/2022/hash/029df12a9363313c3e41047844ecad94-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16766-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/029df12a9363313c3e41047844ecad94-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/029df12a9363313c3e41047844ecad94-Supplemental-Conference.pdf | The existing analysis of asynchronous stochastic gradient descent (SGD) degrades dramatically when any delay is large, giving the impression that performance depends primarily on the delay. On the contrary, we prove much better guarantees for the same asynchronous SGD algorithm regardless of the delays in the gradients... | null | null |
Coresets for Relational Data and The Applications | https://papers.nips.cc/paper_files/paper/2022/hash/029f82afd78288059dc946b105c451fd-Abstract-Conference.html | Jiaxiang Chen, Qingyuan Yang, Ruomin Huang, Hu Ding | https://papers.nips.cc/paper_files/paper/2022/hash/029f82afd78288059dc946b105c451fd-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18405-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/029f82afd78288059dc946b105c451fd-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/029f82afd78288059dc946b105c451fd-Supplemental-Conference.zip | A coreset is a small set that can approximately preserve the structure of the original input data set. Therefore we can run our algorithm on a coreset so as to reduce the total computational complexity. Conventional coreset techniques assume that the input data set is available to process explicitly. However, this assu... | null | null |
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief | https://papers.nips.cc/paper_files/paper/2022/hash/03469b1a66e351b18272be23baf3b809-Abstract-Conference.html | Kaiyang Guo, Shao Yunfeng, Yanhui Geng | https://papers.nips.cc/paper_files/paper/2022/hash/03469b1a66e351b18272be23baf3b809-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18773-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03469b1a66e351b18272be23baf3b809-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03469b1a66e351b18272be23baf3b809-Supplemental-Conference.pdf | Model-based offline reinforcement learning (RL) aims to find highly rewarding policy, by leveraging a previously collected static dataset and a dynamics model. While the dynamics model learned through reuse of the static dataset, its generalization ability hopefully promotes policy learning if properly utilized. To tha... | null | null |
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding | https://papers.nips.cc/paper_files/paper/2022/hash/0346c148ba1c21c6b4780a961ea141dc-Abstract-Conference.html | Yu Meng, Jiaxin Huang, Yu Zhang, Jiawei Han | https://papers.nips.cc/paper_files/paper/2022/hash/0346c148ba1c21c6b4780a961ea141dc-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18696-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0346c148ba1c21c6b4780a961ea141dc-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0346c148ba1c21c6b4780a961ea141dc-Supplemental-Conference.pdf | Pretrained language models (PLMs) have demonstrated remarkable performance in various natural language processing tasks: Unidirectional PLMs (e.g., GPT) are well known for their superior text generation capabilities; bidirectional PLMs (e.g., BERT) have been the prominent choice for natural language understanding (NLU)... | null | null |
Wavelet Score-Based Generative Modeling | https://papers.nips.cc/paper_files/paper/2022/hash/03474669b759f6d38cdca6fb4eb905f4-Abstract-Conference.html | Florentin Guth, Simon Coste, Valentin De Bortoli, Stephane Mallat | https://papers.nips.cc/paper_files/paper/2022/hash/03474669b759f6d38cdca6fb4eb905f4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17946-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03474669b759f6d38cdca6fb4eb905f4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03474669b759f6d38cdca6fb4eb905f4-Supplemental-Conference.zip | Score-based generative models (SGMs) synthesize new data samples from Gaussian white noise by running a time-reversed Stochastic Differential Equation (SDE) whose drift coefficient depends on some probabilistic score. The discretization of such SDEs typically requires a large number of time steps and hence a high compu... | null | null |
Robust Binary Models by Pruning Randomly-initialized Networks | https://papers.nips.cc/paper_files/paper/2022/hash/035f23c0ac4cf2b73b9365ba5a98ad56-Abstract-Conference.html | Chen Liu, Ziqi Zhao, Sabine Süsstrunk, Mathieu Salzmann | https://papers.nips.cc/paper_files/paper/2022/hash/035f23c0ac4cf2b73b9365ba5a98ad56-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17758-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/035f23c0ac4cf2b73b9365ba5a98ad56-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/035f23c0ac4cf2b73b9365ba5a98ad56-Supplemental-Conference.pdf | Robustness to adversarial attacks was shown to require a larger model capacity, and thus a larger memory footprint. In this paper, we introduce an approach to obtain robust yet compact models by pruning randomly-initialized binary networks. Unlike adversarial training, which learns the model parameters, we initialize t... | null | null |
Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses | https://papers.nips.cc/paper_files/paper/2022/hash/03a90e1bb2ceb2ea165424f2d96aa3a1-Abstract-Conference.html | Yuzhou Cao, Tianchi Cai, Lei Feng, Lihong Gu, Jinjie GU, Bo An, Gang Niu, Masashi Sugiyama | https://papers.nips.cc/paper_files/paper/2022/hash/03a90e1bb2ceb2ea165424f2d96aa3a1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18513-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03a90e1bb2ceb2ea165424f2d96aa3a1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03a90e1bb2ceb2ea165424f2d96aa3a1-Supplemental-Conference.pdf | \emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify. Though previous methods for CwR have been provided with theoretical guarantees, they are only compatible with certain loss functions, making the... | null | null |
Markovian Interference in Experiments | https://papers.nips.cc/paper_files/paper/2022/hash/03a9a9c1e15850439653bb971a4ad4b3-Abstract-Conference.html | Vivek Farias, Andrew Li, Tianyi Peng, Andrew Zheng | https://papers.nips.cc/paper_files/paper/2022/hash/03a9a9c1e15850439653bb971a4ad4b3-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17906-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03a9a9c1e15850439653bb971a4ad4b3-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03a9a9c1e15850439653bb971a4ad4b3-Supplemental-Conference.zip | We consider experiments in dynamical systems where interventions on some experimental units impact other units through a limiting constraint (such as a limited supply of products). Despite outsize practical importance, the best estimators for this `Markovian' interference problem are largely heuristic in nature, and th... | null | null |
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/03bdba50e3741ac5e3eaa0e55423587e-Abstract-Conference.html | Paul Rolland, Luca Viano, Norman Schürhoff, Boris Nikolov, Volkan Cevher | https://papers.nips.cc/paper_files/paper/2022/hash/03bdba50e3741ac5e3eaa0e55423587e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18073-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03bdba50e3741ac5e3eaa0e55423587e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03bdba50e3741ac5e3eaa0e55423587e-Supplemental-Conference.zip | While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior. It is well known that, in general, various reward functions can lead to the same optimal policy, and henc... | null | null |
Parallel Tempering With a Variational Reference | https://papers.nips.cc/paper_files/paper/2022/hash/03cd3cf3f74d4f9ce5958de269960884-Abstract-Conference.html | Nikola Surjanovic, Saifuddin Syed, Alexandre Bouchard-Côté, Trevor Campbell | https://papers.nips.cc/paper_files/paper/2022/hash/03cd3cf3f74d4f9ce5958de269960884-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18401-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03cd3cf3f74d4f9ce5958de269960884-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03cd3cf3f74d4f9ce5958de269960884-Supplemental-Conference.pdf | Sampling from complex target distributions is a challenging task fundamental to Bayesian inference. Parallel tempering (PT) addresses this problem by constructing a Markov chain on the expanded state space of a sequence of distributions interpolating between the posterior distribution and a fixed reference distribution... | null | null |
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems | https://papers.nips.cc/paper_files/paper/2022/hash/03d7e13f0092405804f3a381ade8f3f0-Abstract-Conference.html | Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun | https://papers.nips.cc/paper_files/paper/2022/hash/03d7e13f0092405804f3a381ade8f3f0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18431-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03d7e13f0092405804f3a381ade8f3f0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03d7e13f0092405804f3a381ade8f3f0-Supplemental-Conference.pdf | We study Reinforcement Learning for partially observable systems using function approximation. We propose a new PO-bilinear framework, that is general enough to include models such as undercomplete tabular Partially Observable Markov Decision Processes (POMDPs), Linear Quadratic Gaussian (LQG), Predictive State Represe... | null | null |
Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models | https://papers.nips.cc/paper_files/paper/2022/hash/03dfa2a7755635f756b160e9f4c6b789-Abstract-Conference.html | Rui Miao, Zhengling Qi, Xiaoke Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/03dfa2a7755635f756b160e9f4c6b789-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17323-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03dfa2a7755635f756b160e9f4c6b789-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03dfa2a7755635f756b160e9f4c6b789-Supplemental-Conference.zip | We study the problem of off-policy evaluation (OPE) for episodic Partially Observable Markov Decision Processes (POMDPs) with continuous states. Motivated by the recently proposed proximal causal inference framework, we develop a non-parametric identification result for estimating the policy value via a sequence of so-... | null | null |
Efficient Knowledge Distillation from Model Checkpoints | https://papers.nips.cc/paper_files/paper/2022/hash/03e0712bf85ebe7cec4f1a7fc53216c9-Abstract-Conference.html | Chaofei Wang, Qisen Yang, Rui Huang, Shiji Song, Gao Huang | https://papers.nips.cc/paper_files/paper/2022/hash/03e0712bf85ebe7cec4f1a7fc53216c9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18645-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/03e0712bf85ebe7cec4f1a7fc53216c9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/03e0712bf85ebe7cec4f1a7fc53216c9-Supplemental-Conference.pdf | Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong correlation between the performance of teacher and student models, it is commonly believed that a high performing teacher is preferred. Cons... | null | null |
Decoupled Self-supervised Learning for Graphs | https://papers.nips.cc/paper_files/paper/2022/hash/040c816286b3844fd78f2124eec75f2e-Abstract-Conference.html | Teng Xiao, Zhengyu Chen, Zhimeng Guo, Zeyang Zhuang, Suhang Wang | https://papers.nips.cc/paper_files/paper/2022/hash/040c816286b3844fd78f2124eec75f2e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18228-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/040c816286b3844fd78f2124eec75f2e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/040c816286b3844fd78f2124eec75f2e-Supplemental-Conference.zip | This paper studies the problem of conducting self-supervised learning for node representation learning on graphs. Most existing self-supervised learning methods assume the graph is homophilous, where linked nodes often belong to the same class or have similar features. However, such assumptions of homophily do not alw... | null | null |
Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer | https://papers.nips.cc/paper_files/paper/2022/hash/040d3b6af368bf71f952c18da5713b48-Abstract-Conference.html | Lujun Li, ZHE JIN | https://papers.nips.cc/paper_files/paper/2022/hash/040d3b6af368bf71f952c18da5713b48-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16803-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/040d3b6af368bf71f952c18da5713b48-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/040d3b6af368bf71f952c18da5713b48-Supplemental-Conference.pdf | Knowledge distillation can be generally divided into offline and online categories according to whether teacher model is pre-trained and persistent during the distillation process. Offline distillation can employ existing models yet always demonstrates inferior performance than online ones. In this paper, we first em... | null | null |
ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs | https://papers.nips.cc/paper_files/paper/2022/hash/0418973e545b932939302cb605d06f43-Abstract-Conference.html | Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, Shuiwang Ji | https://papers.nips.cc/paper_files/paper/2022/hash/0418973e545b932939302cb605d06f43-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17348-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0418973e545b932939302cb605d06f43-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0418973e545b932939302cb605d06f43-Supplemental-Conference.zip | Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose... | null | null |
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret | https://papers.nips.cc/paper_files/paper/2022/hash/0463ec87d0ac1e98a6cbe3d95d4e3e35-Abstract-Conference.html | Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu | https://papers.nips.cc/paper_files/paper/2022/hash/0463ec87d0ac1e98a6cbe3d95d4e3e35-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16742-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0463ec87d0ac1e98a6cbe3d95d4e3e35-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0463ec87d0ac1e98a6cbe3d95d4e3e35-Supplemental-Conference.pdf | We propose a new learning framework that captures the tiered structure of many real-world user-interaction applications, where the users can be divided into two groups based on their different tolerance on exploration risks and should be treated separately. In this setting, we simultaneously maintain two policies $\pi^... | null | null |
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness | https://papers.nips.cc/paper_files/paper/2022/hash/047aa59e51e3ac7a2422a55468feefd5-Abstract-Conference.html | Sarah Sachs, Hedi Hadiji, Tim van Erven, Cristóbal Guzmán | https://papers.nips.cc/paper_files/paper/2022/hash/047aa59e51e3ac7a2422a55468feefd5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18709-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/047aa59e51e3ac7a2422a55468feefd5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/047aa59e51e3ac7a2422a55468feefd5-Supplemental-Conference.pdf | Stochastic and adversarial data are two widely studied settings in online learning. But many optimizationtasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a better theoretical understanding of the world between these extremes. In this work we establish novel regret bounds fo... | null | null |
Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) | https://papers.nips.cc/paper_files/paper/2022/hash/04b42392f9a3a16aea012395359b8148-Abstract-Conference.html | Jiayuan Ye, Reza Shokri | https://papers.nips.cc/paper_files/paper/2022/hash/04b42392f9a3a16aea012395359b8148-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16726-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04b42392f9a3a16aea012395359b8148-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04b42392f9a3a16aea012395359b8148-Supplemental-Conference.pdf | Prior work on differential privacy analysis of randomized SGD algorithms relies on composition theorems, where the implicit (unrealistic) assumption is that the internal state of the iterative algorithm is revealed to the adversary. As a result, the R\'enyi DP bounds derived by such composition-based analyses linearly ... | null | null |
BR-SNIS: Bias Reduced Self-Normalized Importance Sampling | https://papers.nips.cc/paper_files/paper/2022/hash/04bd683d5428d91c5fbb5a7d2c27064d-Abstract-Conference.html | Gabriel Cardoso, Sergey Samsonov, Achille Thin, Eric Moulines, Jimmy Olsson | https://papers.nips.cc/paper_files/paper/2022/hash/04bd683d5428d91c5fbb5a7d2c27064d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17244-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04bd683d5428d91c5fbb5a7d2c27064d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04bd683d5428d91c5fbb5a7d2c27064d-Supplemental-Conference.pdf | Importance Sampling (IS) is a method for approximating expectations with respect to a target distribution using independent samples from a proposal distribution and the associated to importance weights. In many cases, the target distribution is known up to a normalization constant and self-normalized IS (SNIS) is then ... | null | null |
Learning to Configure Computer Networks with Neural Algorithmic Reasoning | https://papers.nips.cc/paper_files/paper/2022/hash/04cc90ec6868b97b7423dc38ced1e35c-Abstract-Conference.html | Luca Beurer-Kellner, Martin Vechev, Laurent Vanbever, Petar Veličković | https://papers.nips.cc/paper_files/paper/2022/hash/04cc90ec6868b97b7423dc38ced1e35c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16789-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04cc90ec6868b97b7423dc38ced1e35c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04cc90ec6868b97b7423dc38ced1e35c-Supplemental-Conference.zip | We present a new method for scaling automatic configuration of computer networks. The key idea is to relax the computationally hard search problem of finding a configuration that satisfies a given specification into an approximate objective amenable to learning-based techniques. Based on this idea, we train a neural al... | null | null |
Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/04cda3a5ef307978cb5dbef6ab649380-Abstract-Conference.html | Mingze Wang, Chao Ma | https://papers.nips.cc/paper_files/paper/2022/hash/04cda3a5ef307978cb5dbef6ab649380-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19135-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04cda3a5ef307978cb5dbef6ab649380-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04cda3a5ef307978cb5dbef6ab649380-Supplemental-Conference.zip | The convergence of GD and SGD when training mildly parameterized neural networks starting from random initialization is studied. For a broad range of models and loss functions, including the widely used square loss and cross entropy loss, we prove an ''early stage convergence'' result. We show that the loss is decrease... | null | null |
On Divergence Measures for Bayesian Pseudocoresets | https://papers.nips.cc/paper_files/paper/2022/hash/04f8311e7e22eac15d67fe45c242ead8-Abstract-Conference.html | Balhae Kim, Jungwon Choi, Seanie Lee, Yoonho Lee, Jung-Woo Ha, Juho Lee | https://papers.nips.cc/paper_files/paper/2022/hash/04f8311e7e22eac15d67fe45c242ead8-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17012-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/04f8311e7e22eac15d67fe45c242ead8-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/04f8311e7e22eac15d67fe45c242ead8-Supplemental-Conference.pdf | A Bayesian pseudocoreset is a small synthetic dataset for which the posterior over parameters approximates that of the original dataset. While promising, the scalability of Bayesian pseudocoresets is not yet validated in large-scale problems such as image classification with deep neural networks. On the other hand, dat... | null | null |
Unsupervised Learning of Equivariant Structure from Sequences | https://papers.nips.cc/paper_files/paper/2022/hash/0503f5dce343a1d06d16ba103dd52db1-Abstract-Conference.html | Takeru Miyato, Masanori Koyama, Kenji Fukumizu | https://papers.nips.cc/paper_files/paper/2022/hash/0503f5dce343a1d06d16ba103dd52db1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16982-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0503f5dce343a1d06d16ba103dd52db1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0503f5dce343a1d06d16ba103dd52db1-Supplemental-Conference.pdf | In this study, we present \textit{meta-sequential prediction} (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three. Our method leverages the stationary property~(e.g. constant velocity, constant acceleration) of the time sequence to learn the underlying equivariant stru... | null | null |
Multi-Class $H$-Consistency Bounds | https://papers.nips.cc/paper_files/paper/2022/hash/051f3997af1dd65da8e14397b6a72f8e-Abstract-Conference.html | Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong | https://papers.nips.cc/paper_files/paper/2022/hash/051f3997af1dd65da8e14397b6a72f8e-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17197-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/051f3997af1dd65da8e14397b6a72f8e-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/051f3997af1dd65da8e14397b6a72f8e-Supplemental-Conference.pdf | We present an extensive study of $H$-consistency bounds for multi-class classification. These are upper bounds on the target loss estimation error of a predictor in a hypothesis set $H$, expressed in terms of the surrogate loss estimation error of that predictor. They are stronger and more significant guarantees than B... | null | null |
On the Frequency-bias of Coordinate-MLPs | https://papers.nips.cc/paper_files/paper/2022/hash/0525fa17a8dbea687359116d01732e12-Abstract-Conference.html | Sameera Ramasinghe, Lachlan E. MacDonald, Simon Lucey | https://papers.nips.cc/paper_files/paper/2022/hash/0525fa17a8dbea687359116d01732e12-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17312-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0525fa17a8dbea687359116d01732e12-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0525fa17a8dbea687359116d01732e12-Supplemental-Conference.pdf | We show that typical implicit regularization assumptions for deep neural networks (for regression) do not hold for coordinate-MLPs, a family of MLPs that are now ubiquitous in computer vision for representing high-frequency signals. Lack of such implicit bias disrupts smooth interpolations between training samples, and... | null | null |
Mask Matching Transformer for Few-Shot Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/053a18c03e0844d0c484ba2861f8ae6c-Abstract-Conference.html | siyu jiao, Gengwei Zhang, Shant Navasardyan, Ling Chen, Yao Zhao, Yunchao Wei, Humphrey Shi | https://papers.nips.cc/paper_files/paper/2022/hash/053a18c03e0844d0c484ba2861f8ae6c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16678-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/053a18c03e0844d0c484ba2861f8ae6c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/053a18c03e0844d0c484ba2861f8ae6c-Supplemental-Conference.zip | In this paper, we aim to tackle the challenging few-shot segmentation task from a new perspective. Typical methods follow the paradigm to firstly learn prototypical features from support images and then match query features in pixel-level to obtain segmentation results. However, to obtain satisfactory segments, such a ... | null | null |
Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html | Ilai Bistritz, Nicholas Bambos | https://papers.nips.cc/paper_files/paper/2022/hash/056e8e9c8ca9929cb6cf198952bf1dbb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18952-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/056e8e9c8ca9929cb6cf198952bf1dbb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/056e8e9c8ca9929cb6cf198952bf1dbb-Supplemental-Conference.pdf | Abstract Consider $N$ cooperative agents such that for $T$ turns, each agent n takes an action $a_{n}$ and receives a stochastic reward $r_{n}\left(a_{1},\ldots,a_{N}\right)$. Agents cannot observe the actions of other agents and do not know even their own reward function. The agents can communicate with their neighbor... | null | null |
Differentially Private Covariance Revisited | https://papers.nips.cc/paper_files/paper/2022/hash/057405fd73dd7ba7f32a7cb34fb7c7f5-Abstract-Conference.html | Wei Dong, Yuting Liang, Ke Yi | https://papers.nips.cc/paper_files/paper/2022/hash/057405fd73dd7ba7f32a7cb34fb7c7f5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19211-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/057405fd73dd7ba7f32a7cb34fb7c7f5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/057405fd73dd7ba7f32a7cb34fb7c7f5-Supplemental-Conference.zip | In this paper, we present two new algorithms for covariance estimation under concentrated differential privacy (zCDP). The first algorithm achieves a Frobenius error of $\tilde{O}(d^{1/4}\sqrt{\mathrm{tr}}/\sqrt{n} + \sqrt{d}/n)$, where $\mathrm{tr}$ is the trace of the covariance matrix. By taking $\mathrm{tr}=1$, t... | null | null |
Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model | https://papers.nips.cc/paper_files/paper/2022/hash/05b12f103c9e613efc4c85674cdc9066-Abstract-Conference.html | Pranjal Awasthi, Abhimanyu Das, Weihao Kong, Rajat Sen | https://papers.nips.cc/paper_files/paper/2022/hash/05b12f103c9e613efc4c85674cdc9066-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19284-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/05b12f103c9e613efc4c85674cdc9066-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/05b12f103c9e613efc4c85674cdc9066-Supplemental-Conference.pdf | We study the problem of learning generalized linear models under adversarial corruptions.We analyze a classical heuristic called the \textit{iterative trimmed maximum likelihood estimator} which is known to be effective against \textit{label corruptions} in practice. Under label corruptions, we prove that this simple e... | null | null |
Causal Discovery in Linear Latent Variable Models Subject to Measurement Error | https://papers.nips.cc/paper_files/paper/2022/hash/05b63fa06784b71aab3939004e0f0a0d-Abstract-Conference.html | Yuqin Yang, AmirEmad Ghassami, Mohamed Nafea, Negar Kiyavash, Kun Zhang, Ilya Shpitser | https://papers.nips.cc/paper_files/paper/2022/hash/05b63fa06784b71aab3939004e0f0a0d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17005-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/05b63fa06784b71aab3939004e0f0a0d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/05b63fa06784b71aab3939004e0f0a0d-Supplemental-Conference.pdf | We focus on causal discovery in the presence of measurement error in linear systems where the mixing matrix, i.e., the matrix indicating the independent exogenous noise terms pertaining to the observed variables, is identified up to permutation and scaling of the columns. We demonstrate a somewhat surprising connection... | null | null |
Density-driven Regularization for Out-of-distribution Detection | https://papers.nips.cc/paper_files/paper/2022/hash/05b69cc4c8ff6e24c5de1ecd27223d37-Abstract-Conference.html | Wenjian Huang, Hao Wang, Jiahao Xia, Chengyan Wang, Jianguo Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/05b69cc4c8ff6e24c5de1ecd27223d37-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18816-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/05b69cc4c8ff6e24c5de1ecd27223d37-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/05b69cc4c8ff6e24c5de1ecd27223d37-Supplemental-Conference.pdf | Detecting out-of-distribution (OOD) samples is essential for reliably deploying deep learning classifiers in open-world applications. However, existing detectors relying on discriminative probability suffer from the overconfident posterior estimate for OOD data. Other reported approaches either impose strong unproven p... | null | null |
Sparsity in Continuous-Depth Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/0626822954674a06ccd9c234e3f0d572-Abstract-Conference.html | Hananeh Aliee, Till Richter, Mikhail Solonin, Ignacio Ibarra, Fabian Theis, Niki Kilbertus | https://papers.nips.cc/paper_files/paper/2022/hash/0626822954674a06ccd9c234e3f0d572-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19112-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0626822954674a06ccd9c234e3f0d572-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0626822954674a06ccd9c234e3f0d572-Supplemental-Conference.pdf | Neural Ordinary Differential Equations (NODEs) have proven successful in learning dynamical systems in terms of accurately recovering the observed trajectories. While different types of sparsity have been proposed to improve robustness, the generalization properties of NODEs for dynamical systems beyond the observed da... | null | null |
Environment Diversification with Multi-head Neural Network for Invariant Learning | https://papers.nips.cc/paper_files/paper/2022/hash/062d711fb777322e2152435459e6e9d9-Abstract-Conference.html | Bo-Wei Huang, Keng-Te Liao, Chang-Sheng Kao, Shou-De Lin | https://papers.nips.cc/paper_files/paper/2022/hash/062d711fb777322e2152435459e6e9d9-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18268-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/062d711fb777322e2152435459e6e9d9-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/062d711fb777322e2152435459e6e9d9-Supplemental-Conference.pdf | Neural networks are often trained with empirical risk minimization; however, it has been shown that a shift between training and testing distributions can cause unpredictable performance degradation. On this issue, a research direction, invariant learning, has been proposed to extract causal features insensitive to the... | null | null |
Learning Probabilistic Models from Generator Latent Spaces with Hat EBM | https://papers.nips.cc/paper_files/paper/2022/hash/062f9525a7476942f61a6c3b42d0a63f-Abstract-Conference.html | Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang, Song-Chun Zhu | https://papers.nips.cc/paper_files/paper/2022/hash/062f9525a7476942f61a6c3b42d0a63f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17073-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/062f9525a7476942f61a6c3b42d0a63f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/062f9525a7476942f61a6c3b42d0a63f-Supplemental-Conference.zip | This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM). Our formulation posits that observed images are the sum of unobserved latent variables passed through the generator network and a residual random variable that spans the gap between the generator output and the... | null | null |
Learning Best Combination for Efficient N:M Sparsity | https://papers.nips.cc/paper_files/paper/2022/hash/06589ec9d86876508600a678f9c8f51d-Abstract-Conference.html | Yuxin Zhang, Mingbao Lin, ZhiHang Lin, Yiting Luo, Ke Li, Fei Chao, Yongjian Wu, Rongrong Ji | https://papers.nips.cc/paper_files/paper/2022/hash/06589ec9d86876508600a678f9c8f51d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17822-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06589ec9d86876508600a678f9c8f51d-Paper-Conference.pdf | null | By forcing N out of M consecutive weights to be non-zero, the recent N:M fine-grained network sparsity has received increasing attention with its two attractive advantages over traditional irregular network sparsity methods: 1) Promising performance at a high sparsity. 2) Significant speedups when performed on NVIDIA A... | null | null |
Why Do Artificially Generated Data Help Adversarial Robustness | https://papers.nips.cc/paper_files/paper/2022/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html | Yue Xing, Qifan Song, Guang Cheng | https://papers.nips.cc/paper_files/paper/2022/hash/065e259a1d2d955e63b99aac6a3a3081-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18927-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/065e259a1d2d955e63b99aac6a3a3081-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/065e259a1d2d955e63b99aac6a3a3081-Supplemental-Conference.zip | In the adversarial training framework of \cite{carmon2019unlabeled,gowal2021improving}, people use generated/real unlabeled data with pseudolabels to improve adversarial robustness. We provide statistical insights to explain why the artificially generated data improve adversarial training. In particular, we study how t... | null | null |
Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera | https://papers.nips.cc/paper_files/paper/2022/hash/06a52a54c8ee03cd86771136bc91eb1f-Abstract-Conference.html | Hongrui Cai, Wanquan Feng, Xuetao Feng, Yan Wang, Juyong Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/06a52a54c8ee03cd86771136bc91eb1f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18667-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06a52a54c8ee03cd86771136bc91eb1f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/06a52a54c8ee03cd86771136bc91eb1f-Supplemental-Conference.zip | We propose Neural-DynamicReconstruction (NDR), a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. In NDR, we adopt the neural implicit function for surface representation and rendering such that the captured color and depth can be fully utilized to joi... | null | null |
Global Optimal K-Medoids Clustering of One Million Samples | https://papers.nips.cc/paper_files/paper/2022/hash/06abed94583030dd50abe6767bd643b1-Abstract-Conference.html | Jiayang Ren, Kaixun Hua, Yankai Cao | https://papers.nips.cc/paper_files/paper/2022/hash/06abed94583030dd50abe6767bd643b1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18839-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06abed94583030dd50abe6767bd643b1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/06abed94583030dd50abe6767bd643b1-Supplemental-Conference.pdf | We study the deterministic global optimization of the K-Medoids clustering problem. This work proposes a branch and bound (BB) scheme, in which a tailored Lagrangian relaxation method proposed in the 1970s is used to provide a lower bound at each BB node. The lower bounding method already guarantees the maximum gap at ... | null | null |
Batch Multi-Fidelity Active Learning with Budget Constraints | https://papers.nips.cc/paper_files/paper/2022/hash/06ea400b9b7cfce6428ec27a371632eb-Abstract-Conference.html | Shibo Li, Jeff M Phillips, Xin Yu, Robert Kirby, Shandian Zhe | https://papers.nips.cc/paper_files/paper/2022/hash/06ea400b9b7cfce6428ec27a371632eb-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16874-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/06ea400b9b7cfce6428ec27a371632eb-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/06ea400b9b7cfce6428ec27a371632eb-Supplemental-Conference.pdf | Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for these applications is often costly, e.g., by running numerical solvers. The recent work (Li et al., 2022) proposes the first multi-fidelity act... | null | null |
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training | https://papers.nips.cc/paper_files/paper/2022/hash/072fd0525592b43da661e254bbaadc27-Abstract-Conference.html | Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, Junmo Kim | https://papers.nips.cc/paper_files/paper/2022/hash/072fd0525592b43da661e254bbaadc27-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18402-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/072fd0525592b43da661e254bbaadc27-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/072fd0525592b43da661e254bbaadc27-Supplemental-Conference.pdf | Pre-training vision-language models with contrastive objectives has shown promising results that are both scalable to large uncurated datasets and transferable to many downstream applications. Some following works have targeted to improve data efficiency by adding self-supervision terms, but inter-domain (image-text) c... | null | null |
Efficient Multi-agent Communication via Self-supervised Information Aggregation | https://papers.nips.cc/paper_files/paper/2022/hash/075b2875e2b671ddd74aeec0ac9f0357-Abstract-Conference.html | Cong Guan, Feng Chen, Lei Yuan, Chenghe Wang, Hao Yin, Zongzhang Zhang, Yang Yu | https://papers.nips.cc/paper_files/paper/2022/hash/075b2875e2b671ddd74aeec0ac9f0357-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17163-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/075b2875e2b671ddd74aeec0ac9f0357-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/075b2875e2b671ddd74aeec0ac9f0357-Supplemental-Conference.zip | Utilizing messages from teammates can improve coordination in cooperative Multi-agent Reinforcement Learning (MARL). To obtain meaningful information for decision-making, previous works typically combine raw messages generated by teammates with local information as inputs for policy. However, neglecting the aggregation... | null | null |
Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations | https://papers.nips.cc/paper_files/paper/2022/hash/0764db1151b936aca59249e2c1386101-Abstract-Conference.html | Ramansh Sharma, Varun Shankar | https://papers.nips.cc/paper_files/paper/2022/hash/0764db1151b936aca59249e2c1386101-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17755-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0764db1151b936aca59249e2c1386101-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0764db1151b936aca59249e2c1386101-Supplemental-Conference.zip | Physics-informed neural networks (PINNs) are neural networks trained by using physical laws in the form of partial differential equations (PDEs) as soft constraints. We present a new technique for the accelerated training of PINNs that combines modern scientific computing techniques with machine learning: discretely-tr... | null | null |
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning | https://papers.nips.cc/paper_files/paper/2022/hash/076a93fd42aa85f5ccee921a01d77dd5-Abstract-Conference.html | Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil, Srinivas Shakkottai, Jean-Francois Chamberland | https://papers.nips.cc/paper_files/paper/2022/hash/076a93fd42aa85f5ccee921a01d77dd5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16817-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/076a93fd42aa85f5ccee921a01d77dd5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/076a93fd42aa85f5ccee921a01d77dd5-Supplemental-Conference.pdf | Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations. We formulate this safe reinforcement learning (RL) problem using the framework of a finite-horizon Constrained Markov Decision Process (CMDP) with ... | null | null |
Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs | https://papers.nips.cc/paper_files/paper/2022/hash/078fa8f77ce55ef6e9cf79275b88acb0-Abstract-Conference.html | Yeoneung Kim, Insoon Yang, Kwang-Sung Jun | https://papers.nips.cc/paper_files/paper/2022/hash/078fa8f77ce55ef6e9cf79275b88acb0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17935-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/078fa8f77ce55ef6e9cf79275b88acb0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/078fa8f77ce55ef6e9cf79275b88acb0-Supplemental-Conference.zip | In online learning problems, exploiting low variance plays an important role in obtaining tight performance guarantees yet is challenging because variances are often not known a priori. Recently, considerable progress has been made by Zhang et al. (2021) where they obtain a variance-adaptive regret bound for linear ... | null | null |
Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate | https://papers.nips.cc/paper_files/paper/2022/hash/0790ef700dd0072f4940abda9b7d0005-Abstract-Conference.html | Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi, Ming Yan, Wotao Yin, Kun Yuan | https://papers.nips.cc/paper_files/paper/2022/hash/0790ef700dd0072f4940abda9b7d0005-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17245-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0790ef700dd0072f4940abda9b7d0005-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0790ef700dd0072f4940abda9b7d0005-Supplemental-Conference.pdf | Decentralized optimization is an emerging paradigm in distributed learning in which agents achieve network-wide solutions by peer-to-peer communication without the central server. Since communication tends to be slower than computation, when each agent communicates with only a few neighboring agents per iteration, the... | null | null |
Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models | https://papers.nips.cc/paper_files/paper/2022/hash/0799492e7be38b66d10ead5e8809616d-Abstract-Conference.html | Biru Zhu, Yujia Qin, Ganqu Cui, Yangyi Chen, Weilin Zhao, Chong Fu, Yangdong Deng, Zhiyuan Liu, Jingang Wang, Wei Wu, Maosong Sun, Ming Gu | https://papers.nips.cc/paper_files/paper/2022/hash/0799492e7be38b66d10ead5e8809616d-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18724-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0799492e7be38b66d10ead5e8809616d-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0799492e7be38b66d10ead5e8809616d-Supplemental-Conference.zip | Despite the great success of pre-trained language models (PLMs) in a large set of natural language processing (NLP) tasks, there has been a growing concern about their security in real-world applications. Backdoor attack, which poisons a small number of training samples by inserting backdoor triggers, is a typical thre... | null | null |
Dataset Distillation via Factorization | https://papers.nips.cc/paper_files/paper/2022/hash/07bc722f08f096e6ea7ee99349ff0a86-Abstract-Conference.html | Songhua Liu, Kai Wang, Xingyi Yang, Jingwen Ye, Xinchao Wang | https://papers.nips.cc/paper_files/paper/2022/hash/07bc722f08f096e6ea7ee99349ff0a86-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19005-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/07bc722f08f096e6ea7ee99349ff0a86-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/07bc722f08f096e6ea7ee99349ff0a86-Supplemental-Conference.zip | In this paper, we study dataset distillation (DD), from a novel perspective and introduce a \emph{dataset factorization} approach, termed \emph{HaBa}, which is a plug-and-play strategy portable to any existing DD baseline. Unlike conventional DD approaches that aim to produce distilled and representative samples, \emph... | null | null |
Adaptive Sampling for Discovery | https://papers.nips.cc/paper_files/paper/2022/hash/07bc8125400bf4b140c332010756bd9b-Abstract-Conference.html | Ziping Xu, Eunjae Shim, Ambuj Tewari, Paul Zimmerman | https://papers.nips.cc/paper_files/paper/2022/hash/07bc8125400bf4b140c332010756bd9b-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17042-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/07bc8125400bf4b140c332010756bd9b-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/07bc8125400bf4b140c332010756bd9b-Supplemental-Conference.zip | In this paper, we study a sequential decision-making problem, called Adaptive Sampling for Discovery (ASD). Starting with a large unlabeled dataset, algorithms for ASD adaptively label the points with the goal to maximize the sum of responses.This problem has wide applications to real-world discovery problems, for exam... | null | null |
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation | https://papers.nips.cc/paper_files/paper/2022/hash/08050f40fff41616ccfc3080e60a301a-Abstract-Conference.html | Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou, Zhengning Liu, Ming-Ming Cheng, Shi-min Hu | https://papers.nips.cc/paper_files/paper/2022/hash/08050f40fff41616ccfc3080e60a301a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18721-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08050f40fff41616ccfc3080e60a301a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08050f40fff41616ccfc3080e60a301a-Supplemental-Conference.pdf | We present SegNeXt, a simple convolutional network architecture for semantic segmentation. Recent transformer-based models have dominated the field of se- mantic segmentation due to the efficiency of self-attention in encoding spatial information. In this paper, we show that convolutional attention is a more efficient ... | null | null |
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning | https://papers.nips.cc/paper_files/paper/2022/hash/080be5eb7e887319ff30c792c2cbc28c-Abstract-Conference.html | Tao Yu, Yichi Zhang, Zhiru Zhang, Christopher M. De Sa | https://papers.nips.cc/paper_files/paper/2022/hash/080be5eb7e887319ff30c792c2cbc28c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17895-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/080be5eb7e887319ff30c792c2cbc28c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/080be5eb7e887319ff30c792c2cbc28c-Supplemental-Conference.pdf | Hyperdimensional computing (HDC) is an emerging learning paradigm that computes with high dimensional binary vectors. There is an active line of research on HDC in the community of emerging hardware because of its energy efficiency and ultra-low latency---but HDC suffers from low model accuracy, with little theoretical... | null | null |
Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms | https://papers.nips.cc/paper_files/paper/2022/hash/082e82cae0232f45f27fdd2612c31f8a-Abstract-Conference.html | QIN DING, Yue Kang, Yi-Wei Liu, Thomas Chun Man Lee, Cho-Jui Hsieh, James Sharpnack | https://papers.nips.cc/paper_files/paper/2022/hash/082e82cae0232f45f27fdd2612c31f8a-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17454-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/082e82cae0232f45f27fdd2612c31f8a-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/082e82cae0232f45f27fdd2612c31f8a-Supplemental-Conference.zip | The stochastic contextual bandit problem, which models the trade-off between exploration and exploitation, has many real applications, including recommender systems, online advertising and clinical trials. As many other machine learning algorithms, contextual bandit algorithms often have one or more hyper-parameters. A... | null | null |
Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting | https://papers.nips.cc/paper_files/paper/2022/hash/08342dc6ab69f23167b4123086ad4d38-Abstract-Conference.html | Neil Mallinar, James Simon, Amirhesam Abedsoltan, Parthe Pandit, Misha Belkin, Preetum Nakkiran | https://papers.nips.cc/paper_files/paper/2022/hash/08342dc6ab69f23167b4123086ad4d38-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18289-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08342dc6ab69f23167b4123086ad4d38-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08342dc6ab69f23167b4123086ad4d38-Supplemental-Conference.zip | The practical success of overparameterized neural networks has motivated the recent scientific study of \emph{interpolating methods}-- learning methods which are able fit their training data perfectly. Empirically, certain interpolating methods can fit noisy training data without catastrophically bad test performance, ... | null | null |
Pre-trained Adversarial Perturbations | https://papers.nips.cc/paper_files/paper/2022/hash/084727e8abf90a8365b940036329cb6f-Abstract-Conference.html | Yuanhao Ban, Yinpeng Dong | https://papers.nips.cc/paper_files/paper/2022/hash/084727e8abf90a8365b940036329cb6f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19219-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/084727e8abf90a8365b940036329cb6f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/084727e8abf90a8365b940036329cb6f-Supplemental-Conference.pdf | Self-supervised pre-training has drawn increasing attention in recent years due to its superior performance on numerous downstream tasks after fine-tuning. However, it is well-known that deep learning models lack the robustness to adversarial examples, which can also invoke security issues to pre-trained models, despit... | null | null |
An Empirical Study on Disentanglement of Negative-free Contrastive Learning | https://papers.nips.cc/paper_files/paper/2022/hash/0850e04a62e0f3407780852581c5fcf4-Abstract-Conference.html | Jinkun Cao, Ruiqian Nai, Qing Yang, Jialei Huang, Yang Gao | https://papers.nips.cc/paper_files/paper/2022/hash/0850e04a62e0f3407780852581c5fcf4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17595-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0850e04a62e0f3407780852581c5fcf4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0850e04a62e0f3407780852581c5fcf4-Supplemental-Conference.zip | Negative-free contrastive learning methods have attracted a lot of attention with simplicity and impressive performances for large-scale pretraining. However, its disentanglement property remains unexplored. In this paper, we examine negative-free contrastive learning methods to study the disentanglement property empir... | null | null |
MABSplit: Faster Forest Training Using Multi-Armed Bandits | https://papers.nips.cc/paper_files/paper/2022/hash/08857467641ad82f635023d530605b4c-Abstract-Conference.html | Mo Tiwari, Ryan Kang, Jaeyong Lee, Chris Piech, Ilan Shomorony, Sebastian Thrun, Martin J. Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/08857467641ad82f635023d530605b4c-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16915-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08857467641ad82f635023d530605b4c-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08857467641ad82f635023d530605b4c-Supplemental-Conference.pdf | Random forests are some of the most widely used machine learning models today, especially in domains that necessitate interpretability. We present an algorithm that accelerates the training of random forests and other popular tree-based learning methods. At the core of our algorithm is a novel node-splitting subroutine... | null | null |
Counterfactual Fairness with Partially Known Causal Graph | https://papers.nips.cc/paper_files/paper/2022/hash/08887999616116910fccec17a63584b5-Abstract-Conference.html | Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han, Kun Zhang, Mingming Gong | https://papers.nips.cc/paper_files/paper/2022/hash/08887999616116910fccec17a63584b5-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17089-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08887999616116910fccec17a63584b5-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08887999616116910fccec17a63584b5-Supplemental-Conference.pdf | Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learni... | null | null |
Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints | https://papers.nips.cc/paper_files/paper/2022/hash/089b592cccfafdca8e0178e85b609f19-Abstract-Conference.html | Jose Gallego-Posada, Juan Ramirez, Akram Erraqabi, Yoshua Bengio, Simon Lacoste-Julien | https://papers.nips.cc/paper_files/paper/2022/hash/089b592cccfafdca8e0178e85b609f19-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17807-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/089b592cccfafdca8e0178e85b609f19-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/089b592cccfafdca8e0178e85b609f19-Supplemental-Conference.pdf | The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learnin... | null | null |
Algorithms and Hardness for Learning Linear Thresholds from Label Proportions | https://papers.nips.cc/paper_files/paper/2022/hash/08a9e28c96d016dd63903ab51cd085b0-Abstract-Conference.html | Rishi Saket | https://papers.nips.cc/paper_files/paper/2022/hash/08a9e28c96d016dd63903ab51cd085b0-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17810-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08a9e28c96d016dd63903ab51cd085b0-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08a9e28c96d016dd63903ab51cd085b0-Supplemental-Conference.pdf | We study the learnability of linear threshold functions (LTFs) in the learning from label proportions (LLP) framework. In this, the feature-vector classifier is learnt from bags of feature-vectors and their corresponding observed label proportions which are satisfied by (i.e., consistent with) some unknown LTF. This pr... | null | null |
Predictive Coding beyond Gaussian Distributions | https://papers.nips.cc/paper_files/paper/2022/hash/08f9de0232c0b485110237f6e6cf88f1-Abstract-Conference.html | Luca Pinchetti, Tommaso Salvatori, Yordan Yordanov, Beren Millidge, Yuhang Song, Thomas Lukasiewicz | https://papers.nips.cc/paper_files/paper/2022/hash/08f9de0232c0b485110237f6e6cf88f1-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17495-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08f9de0232c0b485110237f6e6cf88f1-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08f9de0232c0b485110237f6e6cf88f1-Supplemental-Conference.pdf | A large amount of recent research has the far-reaching goal of finding training methods for deep neural networks that can serve as alternatives to backpropagation~(BP). A prominent example is predictive coding (PC), which is a neuroscience-inspired method that performs inference on hierarchical Gaussian generative mode... | null | null |
Semi-supervised Active Linear Regression | https://papers.nips.cc/paper_files/paper/2022/hash/08fe4b20d554296e503f5a43795c78d6-Abstract-Conference.html | Nived Rajaraman, Fnu Devvrit, Pranjal Awasthi | https://papers.nips.cc/paper_files/paper/2022/hash/08fe4b20d554296e503f5a43795c78d6-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/16896-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08fe4b20d554296e503f5a43795c78d6-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08fe4b20d554296e503f5a43795c78d6-Supplemental-Conference.pdf | Labeled data often comes at a high cost as it may require recruiting human labelers or running costly experiments. At the same time, in many practical scenarios, one already has access to a partially labeled, potentially biased dataset that can help with the learning task at hand. Motivated by such settings, we formall... | null | null |
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness | https://papers.nips.cc/paper_files/paper/2022/hash/08fe50bf209c57eecf0804f9f9ed639f-Abstract-Conference.html | Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang | https://papers.nips.cc/paper_files/paper/2022/hash/08fe50bf209c57eecf0804f9f9ed639f-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/18884-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/08fe50bf209c57eecf0804f9f9ed639f-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/08fe50bf209c57eecf0804f9f9ed639f-Supplemental-Conference.pdf | We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversarially robust only on a small fraction $\beta \ll 1$ of the data distribution. Our proposed notion of barely robust learning requires robustness ... | null | null |
Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses | https://papers.nips.cc/paper_files/paper/2022/hash/0904c7edde20d7134a77fc7f9cd86ea2-Abstract-Conference.html | Sanket Shah, Kai Wang, Bryan Wilder, Andrew Perrault, Milind Tambe | https://papers.nips.cc/paper_files/paper/2022/hash/0904c7edde20d7134a77fc7f9cd86ea2-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17149-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0904c7edde20d7134a77fc7f9cd86ea2-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0904c7edde20d7134a77fc7f9cd86ea2-Supplemental-Conference.pdf | Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better \textit{on that specific task}. The main technical challenge associated with DFL is that it requires being able to differentiate through the optimization ... | null | null |
Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics | https://papers.nips.cc/paper_files/paper/2022/hash/091166620a04a289c555f411d8899049-Abstract-Conference.html | Cristopher Salvi, Maud Lemercier, Andris Gerasimovics | https://papers.nips.cc/paper_files/paper/2022/hash/091166620a04a289c555f411d8899049-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17640-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/091166620a04a289c555f411d8899049-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/091166620a04a289c555f411d8899049-Supplemental-Conference.pdf | Stochastic partial differential equations (SPDEs) are the mathematical tool of choice for modelling spatiotemporal PDE-dynamics under the influence of randomness. Based on the notion of mild solution of an SPDE, we introduce a novel neural architecture to learn solution operators of PDEs with (possibly stochastic) forc... | null | null |
Okapi: Generalising Better by Making Statistical Matches Match | https://papers.nips.cc/paper_files/paper/2022/hash/0918183ced31affb7ce0345e45ac1943-Abstract-Conference.html | Myles Bartlett, Sara Romiti, Viktoriia Sharmanska, Novi Quadrianto | https://papers.nips.cc/paper_files/paper/2022/hash/0918183ced31affb7ce0345e45ac1943-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/17728-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/0918183ced31affb7ce0345e45ac1943-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/0918183ced31affb7ce0345e45ac1943-Supplemental-Conference.pdf | We propose Okapi, a simple, efficient, and general method for robust semi-supervised learning based on online statistical matching. Our method uses a nearest-neighbours-based matching procedure to generate cross-domain views for a consistency loss, while eliminating statistical outliers. In order to perform the online ... | null | null |
Revisiting Heterophily For Graph Neural Networks | https://papers.nips.cc/paper_files/paper/2022/hash/092359ce5cf60a80e882378944bf1be4-Abstract-Conference.html | Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, Doina Precup | https://papers.nips.cc/paper_files/paper/2022/hash/092359ce5cf60a80e882378944bf1be4-Abstract-Conference.html | NIPS 2022 | https://papers.nips.cc/paper_files/paper/19041-/bibtex | https://papers.nips.cc/paper_files/paper/2022/file/092359ce5cf60a80e882378944bf1be4-Paper-Conference.pdf | https://papers.nips.cc/paper_files/paper/2022/file/092359ce5cf60a80e882378944bf1be4-Supplemental-Conference.pdf | Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by using graph structures based on the relational inductive bias (homophily assumption). While GNNs have been commonly believed to outperform NNs in real-world tasks, recent work has identified a non-trivial set of datasets where their performance compared... | null | null |
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NeurIPS 2022 Accepted Paper Meta Info Dataset
This dataset is collected from the NeurIPS 2022 Advances in Neural Information Processing Systems 35 conference accepted paper (https://papers.nips.cc/paper_files/paper/2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/nips2022). For researchers who are interested in doing analysis of NIPS 2022 accepted papers and potential research trends, you can use the already cleaned up json file in the dataset. Each row contains the meta information of a paper in the NIPS 2022 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Meta Information of Json File
{
"title": "Federated Submodel Optimization for Hot and Cold Data Features",
"url": "https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html",
"authors": "Yucheng Ding, Chaoyue Niu, Fan Wu, Shaojie Tang, Chengfei Lyu, yanghe feng, Guihai Chen",
"detail_url": "https://papers.nips.cc/paper_files/paper/2022/hash/002262941c9edfd472a79298b2ac5e17-Abstract-Conference.html",
"tags": "NIPS 2022",
"Bibtex": "https://papers.nips.cc/paper_files/paper/17527-/bibtex",
"Paper": "https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Paper-Conference.pdf",
"Supplemental": "https://papers.nips.cc/paper_files/paper/2022/file/002262941c9edfd472a79298b2ac5e17-Supplemental-Conference.pdf",
"abstract": "We focus on federated learning in practical recommender systems and natural language processing scenarios. The global model for federated optimization typically contains a large and sparse embedding layer, while each client\u2019s local data tend to interact with part of features, updating only a small submodel with the feature-related embedding vectors. We identify a new and important issue that distinct data features normally involve different numbers of clients, generating the differentiation of hot and cold features. We further reveal that the classical federated averaging algorithm (FedAvg) or its variants, which randomly selects clients to participate and uniformly averages their submodel updates, will be severely slowed down, because different parameters of the global model are optimized at different speeds. More specifically, the model parameters related to hot (resp., cold) features will be updated quickly (resp., slowly). We thus propose federated submodel averaging (FedSubAvg), which introduces the number of feature-related clients as the metric of feature heat to correct the aggregation of submodel updates. We prove that due to the dispersion of feature heat, the global objective is ill-conditioned, and FedSubAvg works as a suitable diagonal preconditioner. We also rigorously analyze FedSubAvg\u2019s convergence rate to stationary points. We finally evaluate FedSubAvg over several public and industrial datasets. The evaluation results demonstrate that FedSubAvg significantly outperforms FedAvg and its variants."
}
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