paper_id string | title string | paper_url string | pdf_url string | authors list | abstract large_string | track string | primary_area string | doi string | volume string | issue string | pages string | abstract_source string | arxiv_id string | arxiv_id_source string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
10.1609/aaai.v35i8.16849 | Provably Secure Federated Learning against Malicious Clients | https://ojs.aaai.org/index.php/AAAI/article/view/16849 | https://ojs.aaai.org/index.php/AAAI/article/download/16849/16656 | [
"Xiaoyu Cao",
"Jinyuan Jia",
"Neil Zhenqiang Gong"
] | Federated learning enables clients to collaboratively learn a shared global model without sharing their local training data with a cloud server. However, malicious clients can corrupt the global model to predict incorrect labels for testing examples. Existing defenses against malicious clients leverage Byzantine-robust... | main | Machine Learning | 10.1609/aaai.v35i8.16849 | 35 | 8 | 6885-6893 | official | 2102.01854 | title_snapshot |
10.1609/aaai.v35i8.16850 | Dual Quaternion Knowledge Graph Embeddings | https://ojs.aaai.org/index.php/AAAI/article/view/16850 | https://ojs.aaai.org/index.php/AAAI/article/download/16850/16657 | [
"Zongsheng Cao",
"Qianqian Xu",
"Zhiyong Yang",
"Xiaochun Cao",
"Qingming Huang"
] | In this paper, we study the problem of learning representations of entities and relations in the knowledge graph for the link prediction task. Our idea is based on the observation that the vast majority of the related work only models the relation as a single geometric operation such as translation or rotation, which l... | main | Machine Learning | 10.1609/aaai.v35i8.16850 | 35 | 8 | 6894-6902 | official | null | null |
10.1609/aaai.v35i8.16851 | Counterfactual Explanations for Oblique Decision Trees:Exact, Efficient Algorithms | https://ojs.aaai.org/index.php/AAAI/article/view/16851 | https://ojs.aaai.org/index.php/AAAI/article/download/16851/16658 | [
"Miguel Á. Carreira-Perpiñán",
"Suryabhan Singh Hada"
] | We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier. This has become a topic of recent interest as a way to query a trained model and suggest possible actions to overturn its decision. Mathema... | main | Machine Learning | 10.1609/aaai.v35i8.16851 | 35 | 8 | 6903-6911 | official | 2103.01096 | title_snapshot |
10.1609/aaai.v35i8.16852 | Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16852 | https://ojs.aaai.org/index.php/AAAI/article/download/16852/16659 | [
"Paola Cascante-Bonilla",
"Fuwen Tan",
"Yanjun Qi",
"Vicente Ordonez"
] | In this paper we revisit the idea of pseudo-labeling in the context of semi-supervised learning where a learning algorithm has access to a small set of labeled samples and a large set of unlabeled samples. Pseudo-labeling works by applying pseudo-labels to samples in the unlabeled set by using a model trained on combin... | main | Machine Learning | 10.1609/aaai.v35i8.16852 | 35 | 8 | 6912-6920 | official | 2001.06001 | title_snapshot |
10.1609/aaai.v35i8.16853 | Frivolous Units: Wider Networks Are Not Really That Wide | https://ojs.aaai.org/index.php/AAAI/article/view/16853 | https://ojs.aaai.org/index.php/AAAI/article/download/16853/16660 | [
"Stephen Casper",
"Xavier Boix",
"Vanessa D'Amario",
"Ling Guo",
"Martin Schrimpf",
"Kasper Vinken",
"Gabriel Kreiman"
] | A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, t... | main | Machine Learning | 10.1609/aaai.v35i8.16853 | 35 | 8 | 6921-6929 | official | 1912.04783 | title_snapshot |
10.1609/aaai.v35i8.16854 | Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm | https://ojs.aaai.org/index.php/AAAI/article/view/16854 | https://ojs.aaai.org/index.php/AAAI/article/download/16854/16661 | [
"Saptarshi Chakraborty",
"Debolina Paul",
"Swagatam Das"
] | Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an automated fashion. Howe... | main | Machine Learning | 10.1609/aaai.v35i8.16854 | 35 | 8 | 6930-6938 | official | 2012.10929 | title_snapshot |
10.1609/aaai.v35i8.16855 | High-Confidence Off-Policy (or Counterfactual) Variance Estimation | https://ojs.aaai.org/index.php/AAAI/article/view/16855 | https://ojs.aaai.org/index.php/AAAI/article/download/16855/16662 | [
"Yash Chandak",
"Shiv Shankar",
"Philip S. Thomas"
] | Many sequential decision-making systems leverage data collected using prior policies to propose a new policy. For critical applications, it is important that high-confidence guarantees on the new policy’s behavior are provided before deployment, to ensure that the policy will behave as desired. Prior works have studied... | main | Machine Learning | 10.1609/aaai.v35i8.16855 | 35 | 8 | 6939-6947 | official | 2101.09847 | title_snapshot |
10.1609/aaai.v35i8.16856 | A Multi-step-ahead Markov Conditional Forward Model with Cube Perturbations for Extreme Weather Forecasting | https://ojs.aaai.org/index.php/AAAI/article/view/16856 | https://ojs.aaai.org/index.php/AAAI/article/download/16856/16663 | [
"Chia-Yuan Chang",
"Cheng-Wei Lu",
"Chuan-Ju Wang"
] | Predicting extreme weather events such as tropical and extratropical cyclones is of vital scientific and societal importance. Of late, machine learning methods have found their way to weather analysis and prediction, but mostly, these methods use machine learning merely as a complement to traditional numerical weather ... | main | Machine Learning | 10.1609/aaai.v35i8.16856 | 35 | 8 | 6948-6955 | official | null | null |
10.1609/aaai.v35i8.16857 | Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications | https://ojs.aaai.org/index.php/AAAI/article/view/16857 | https://ojs.aaai.org/index.php/AAAI/article/download/16857/16664 | [
"Haw-Shiuan Chang",
"Amol Agrawal",
"Andrew McCallum"
] | Most unsupervised NLP models represent each word with a single point or single region in semantic space, while the existing multi-sense word embeddings cannot represent longer word sequences like phrases or sentences. We propose a novel embedding method for a text sequence (a phrase or a sentence) where each sequence i... | main | Machine Learning | 10.1609/aaai.v35i8.16857 | 35 | 8 | 6956-6965 | official | 2103.15330 | title_snapshot |
10.1609/aaai.v35i8.16858 | On Online Optimization: Dynamic Regret Analysis of Strongly Convex and Smooth Problems | https://ojs.aaai.org/index.php/AAAI/article/view/16858 | https://ojs.aaai.org/index.php/AAAI/article/download/16858/16665 | [
"Ting-Jui Chang",
"Shahin Shahrampour"
] | The regret bound of dynamic online learning algorithms is often expressed in terms of the variation in the function sequence (V_T) and/or the path-length of the minimizer sequence after T rounds. For strongly convex and smooth functions, Zhang et al. (2017) establish the squared path-length of the minimizer sequence (C... | main | Machine Learning | 10.1609/aaai.v35i8.16858 | 35 | 8 | 6966-6973 | official | 2006.03912 | title_judge |
10.1609/aaai.v35i8.16859 | Provable Benefits of Overparameterization in Model Compression: From Double Descent to Pruning Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16859 | https://ojs.aaai.org/index.php/AAAI/article/download/16859/16666 | [
"Xiangyu Chang",
"Yingcong Li",
"Samet Oymak",
"Christos Thrampoulidis"
] | Deep networks are typically trained with many more parameters than the size of the training dataset. Recent empirical evidence indicates that the practice of overparameterization not only benefits training large models, but also assists – perhaps counterintuitively – building lightweight models. Specifically, it sugges... | main | Machine Learning | 10.1609/aaai.v35i8.16859 | 35 | 8 | 6974-6983 | official | 2012.08749 | title_snapshot |
10.1609/aaai.v35i8.16860 | Differentially Private Decomposable Submodular Maximization | https://ojs.aaai.org/index.php/AAAI/article/view/16860 | https://ojs.aaai.org/index.php/AAAI/article/download/16860/16667 | [
"Anamay Chaturvedi",
"Huy Lê Nguyễn",
"Lydia Zakynthinou"
] | We study the problem of differentially private constrained maximization of decomposable submodular functions. A submodular function is decomposable if it takes the form of a sum of submodular functions. The special case of maximizing a monotone, decomposable submodular function under cardinality constraints is known as... | main | Machine Learning | 10.1609/aaai.v35i8.16860 | 35 | 8 | 6984-6992 | official | 2005.14717 | title_snapshot |
10.1609/aaai.v35i8.16861 | Using Hindsight to Anchor Past Knowledge in Continual Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16861 | https://ojs.aaai.org/index.php/AAAI/article/download/16861/16668 | [
"Arslan Chaudhry",
"Albert Gordo",
"Puneet Dokania",
"Philip Torr",
"David Lopez-Paz"
] | In continual learning, the learner faces a stream of data whose distribution changes over time. Modern neural networks are known to suffer under this setting, as they quickly forget previously acquired knowledge. To address such catastrophic forgetting, many continual learning methods implement different types of exper... | main | Machine Learning | 10.1609/aaai.v35i8.16861 | 35 | 8 | 6993-7001 | official | 2002.08165 | title_snapshot |
10.1609/aaai.v35i8.16862 | Deep Verifier Networks: Verification of Deep Discriminative Models with Deep Generative Models | https://ojs.aaai.org/index.php/AAAI/article/view/16862 | https://ojs.aaai.org/index.php/AAAI/article/download/16862/16669 | [
"Tong Che",
"Xiaofeng Liu",
"Site Li",
"Yubin Ge",
"Ruixiang Zhang",
"Caiming Xiong",
"Yoshua Bengio"
] | AI Safety is a major concern in many deep learning applications such as autonomous driving. Given a trained deep learning model, an important natural problem is how to reliably verify the model's prediction. In this paper, we propose a novel framework --- deep verifier networks (DVN) to detect unreliable inputs or pred... | main | Machine Learning | 10.1609/aaai.v35i8.16862 | 35 | 8 | 7002-7010 | official | 1911.07421 | title_snapshot |
10.1609/aaai.v35i8.16823 | An Enhanced Advising Model in Teacher-Student Framework using State Categorization | https://ojs.aaai.org/index.php/AAAI/article/view/16823 | https://ojs.aaai.org/index.php/AAAI/article/download/16823/16630 | [
"Daksh Anand",
"Vaibhav Gupta",
"Praveen Paruchuri",
"Balaraman Ravindran"
] | The teacher-student framework aims to improve the sample efficiency of RL algorithms by deploying an advising mechanism in which a teacher helps a student by guiding its exploration. Prior work in this field has considered an advising mechanism where the teacher advises the student about the optimal action to take in a... | main | Machine Learning | 10.1609/aaai.v35i8.16823 | 35 | 8 | 6653-6660 | official | null | null |
10.1609/aaai.v35i8.16824 | On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory | https://ojs.aaai.org/index.php/AAAI/article/view/16824 | https://ojs.aaai.org/index.php/AAAI/article/download/16824/16631 | [
"Alexandre Araujo",
"Benjamin Negrevergne",
"Yann Chevaleyre",
"Jamal Atif"
] | This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with implications in training stability, generalization, robustness against adversarial examples, etc. However, computing the exact value of the ... | main | Machine Learning | 10.1609/aaai.v35i8.16824 | 35 | 8 | 6661-6669 | official | 2006.08391 | title_snapshot |
10.1609/aaai.v35i8.16825 | The Tractability of SHAP-Score-Based Explanations for Classification over Deterministic and Decomposable Boolean Circuits | https://ojs.aaai.org/index.php/AAAI/article/view/16825 | https://ojs.aaai.org/index.php/AAAI/article/download/16825/16632 | [
"Marcelo Arenas",
"Pablo Barceló",
"Leopoldo Bertossi",
"Mikaël Monet"
] | Scores based on Shapley values are widely used for providing explanations to classification results over machine learning models. A prime example of this is the influential SHAP-score, a version of the Shapley value that can help explain the result of a learned model on a specific entity by assigning a score to every f... | main | Machine Learning | 10.1609/aaai.v35i8.16825 | 35 | 8 | 6670-6678 | official | 2007.14045 | title_judge |
10.1609/aaai.v35i8.16826 | TabNet: Attentive Interpretable Tabular Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16826 | https://ojs.aaai.org/index.php/AAAI/article/download/16826/16633 | [
"Sercan Ö. Arik",
"Tomas Pfister"
] | We propose a novel high-performance and interpretable canonical deep tabular data learning architecture, TabNet. TabNet uses sequential attention to choose which features to reason from at each decision step, enabling interpretability and more efficient learning as the learning capacity is used for the most salient fea... | main | Machine Learning | 10.1609/aaai.v35i8.16826 | 35 | 8 | 6679-6687 | official | 1908.07442 | title_snapshot |
10.1609/aaai.v35i8.16827 | Robust Model Compression Using Deep Hypotheses | https://ojs.aaai.org/index.php/AAAI/article/view/16827 | https://ojs.aaai.org/index.php/AAAI/article/download/16827/16634 | [
"Omri Armstrong",
"Ran Gilad-Bachrach"
] | Machine Learning models should ideally be compact and robust. Compactness provides efficiency and comprehensibility whereas robustness provides stability. Both topics have been studied in recent years but in isolation. Here we present a robust model compression scheme which is independent of model types: it can compres... | main | Machine Learning | 10.1609/aaai.v35i8.16827 | 35 | 8 | 6688-6695 | official | 2103.07668 | title_snapshot |
10.1609/aaai.v35i8.16828 | Deep Radial-Basis Value Functions for Continuous Control | https://ojs.aaai.org/index.php/AAAI/article/view/16828 | https://ojs.aaai.org/index.php/AAAI/article/download/16828/16635 | [
"Kavosh Asadi",
"Neev Parikh",
"Ronald E. Parr",
"George D. Konidaris",
"Michael L. Littman"
] | A core operation in reinforcement learning (RL) is finding an action that is optimal with respect to a learned value function. This operation is often challenging when the learned value function takes continuous actions as input. We introduce deep radial-basis value functions (RBVFs): value functions learned using a de... | main | Machine Learning | 10.1609/aaai.v35i8.16828 | 35 | 8 | 6696-6704 | official | 2002.01883 | title_snapshot |
10.1609/aaai.v35i8.16829 | DecAug: Out-of-Distribution Generalization via Decomposed Feature Representation and Semantic Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16829 | https://ojs.aaai.org/index.php/AAAI/article/download/16829/16636 | [
"Haoyue Bai",
"Rui Sun",
"Lanqing Hong",
"Fengwei Zhou",
"Nanyang Ye",
"Han-Jia Ye",
"S.-H. Gary Chan",
"Zhenguo Li"
] | While deep learning demonstrates its strong ability to handle independent and identically distributed (IID) data, it often suffers from out-of-distribution (OoD) generalization, where the test data come from another distribution (w.r.t. the training one). Designing a general OoD generalization framework for a wide rang... | main | Machine Learning | 10.1609/aaai.v35i8.16829 | 35 | 8 | 6705-6713 | official | 2012.09382 | title_snapshot |
10.1609/aaai.v35i8.16830 | Correlative Channel-Aware Fusion for Multi-View Time Series Classification | https://ojs.aaai.org/index.php/AAAI/article/view/16830 | https://ojs.aaai.org/index.php/AAAI/article/download/16830/16637 | [
"Yue Bai",
"Lichen Wang",
"Zhiqiang Tao",
"Sheng Li",
"Yun Fu"
] | Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views. Existing methods for MVTSC mainly aim to fuse multi-view information at an early stage, e.g., by extracting a common feature subspace among multiple views. However, these app... | main | Machine Learning | 10.1609/aaai.v35i8.16830 | 35 | 8 | 6714-6722 | official | 1911.11561 | title_snapshot |
10.1609/aaai.v35i8.16831 | Deterministic Mini-batch Sequencing for Training Deep Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16831 | https://ojs.aaai.org/index.php/AAAI/article/download/16831/16638 | [
"Subhankar Banerjee",
"Shayok Chakraborty"
] | Recent advancements in the field of deep learning have dramatically improved the performance of machine learning models in a variety of applications, including computer vision, text mining, speech processing and fraud detection among others. Mini-batch gradient descent is the standard algorithm to train deep models, wh... | main | Machine Learning | 10.1609/aaai.v35i8.16831 | 35 | 8 | 6723-6731 | official | null | null |
10.1609/aaai.v35i8.16832 | Relative Variational Intrinsic Control | https://ojs.aaai.org/index.php/AAAI/article/view/16832 | https://ojs.aaai.org/index.php/AAAI/article/download/16832/16639 | [
"Kate Baumli",
"David Warde-Farley",
"Steven Hansen",
"Volodymyr Mnih"
] | In the absence of external rewards, agents can still learn useful behaviors by identifying and mastering a set of diverse skills within their environment. Existing skill learning methods use mutual information objectives to incentivize each skill to be diverse and distinguishable from the rest. However, if care is not ... | main | Machine Learning | 10.1609/aaai.v35i8.16832 | 35 | 8 | 6732-6740 | official | 2012.07827 | title_snapshot |
10.1609/aaai.v35i8.16833 | A Theory of Independent Mechanisms for Extrapolation in Generative Models | https://ojs.aaai.org/index.php/AAAI/article/view/16833 | https://ojs.aaai.org/index.php/AAAI/article/download/16833/16640 | [
"Michel Besserve",
"Remy Sun",
"Dominik Janzing",
"Bernhard Schölkopf"
] | Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating proc... | main | Machine Learning | 10.1609/aaai.v35i8.16833 | 35 | 8 | 6741-6749 | official | 2004.00184 | title_snapshot |
10.1609/aaai.v35i8.16834 | ExGAN: Adversarial Generation of Extreme Samples | https://ojs.aaai.org/index.php/AAAI/article/view/16834 | https://ojs.aaai.org/index.php/AAAI/article/download/16834/16641 | [
"Siddharth Bhatia",
"Arjit Jain",
"Bryan Hooi"
] | Mitigating the risk arising from extreme events is a fundamental goal with many applications, such as the modelling of natural disasters, financial crashes, epidemics, and many others. To manage this risk, a vital step is to be able to understand or generate a wide range of extreme scenarios. Existing approaches based ... | main | Machine Learning | 10.1609/aaai.v35i8.16834 | 35 | 8 | 6750-6758 | official | 2009.08454 | title_snapshot |
10.1609/aaai.v35i8.16835 | Ordinal Historical Dependence in Graphical Event Models with Tree Representations | https://ojs.aaai.org/index.php/AAAI/article/view/16835 | https://ojs.aaai.org/index.php/AAAI/article/download/16835/16642 | [
"Debarun Bhattacharjya",
"Tian Gao",
"Dharmashankar Subramanian"
] | Graphical event models are representations that capture process independence between different types of events in multivariate temporal point processes. The literature consists of various parametric models and approaches to learn them from multivariate event stream data. Since these models are interpretable, they are o... | main | Machine Learning | 10.1609/aaai.v35i8.16835 | 35 | 8 | 6759-6767 | official | null | null |
10.1609/aaai.v35i8.16836 | Characterizing the Loss Landscape in Non-Negative Matrix Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/16836 | https://ojs.aaai.org/index.php/AAAI/article/download/16836/16643 | [
"Johan Bjorck",
"Anmol Kabra",
"Kilian Q. Weinberger",
"Carla Gomes"
] | Non-negative matrix factorization (NMF) is a highly celebrated algorithm for matrix decomposition that guarantees non-negative factors. The underlying optimization problem is computationally intractable, yet in practice, gradient-descent-based methods often find good solutions. In this paper, we revisit the NMF optimiz... | main | Machine Learning | 10.1609/aaai.v35i8.16836 | 35 | 8 | 6768-6776 | official | null | null |
10.1609/aaai.v35i8.16837 | Understanding Decoupled and Early Weight Decay | https://ojs.aaai.org/index.php/AAAI/article/view/16837 | https://ojs.aaai.org/index.php/AAAI/article/download/16837/16644 | [
"Johan Bjorck",
"Kilian Q. Weinberger",
"Carla Gomes"
] | Weight decay (WD) is a traditional regularization technique in deep learning, but despite its ubiquity, its behavior is still an area of active research. Golatkar et al. have recently shown that WD only matters at the start of the training in computer vision, upending traditional wisdom. Loshchilov et al. show that for... | main | Machine Learning | 10.1609/aaai.v35i8.16837 | 35 | 8 | 6777-6785 | official | 2012.13841 | title_snapshot |
10.1609/aaai.v35i8.16838 | Communication-Aware Collaborative Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16838 | https://ojs.aaai.org/index.php/AAAI/article/download/16838/16645 | [
"Avrim Blum",
"Shelby Heinecke",
"Lev Reyzin"
] | Algorithms for noiseless collaborative PAC learning have been analyzed and optimized in recent years with respect to sample complexity. In this paper, we study collaborative PAC learning with the goal of reducing communication cost at essentially no penalty to the sample complexity. We develop communication efficient c... | main | Machine Learning | 10.1609/aaai.v35i8.16838 | 35 | 8 | 6786-6793 | official | 2012.10569 | title_snapshot |
10.1609/aaai.v35i8.16839 | Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16839 | https://ojs.aaai.org/index.php/AAAI/article/download/16839/16646 | [
"Yoonho Boo",
"Sungho Shin",
"Jungwook Choi",
"Wonyong Sung"
] | The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge... | main | Machine Learning | 10.1609/aaai.v35i8.16839 | 35 | 8 | 6794-6802 | official | 2009.14502 | title_snapshot |
10.1609/aaai.v35i8.16840 | Fast Training of Provably Robust Neural Networks by SingleProp | https://ojs.aaai.org/index.php/AAAI/article/view/16840 | https://ojs.aaai.org/index.php/AAAI/article/download/16840/16647 | [
"Akhilan Boopathy",
"Lily Weng",
"Sijia Liu",
"Pin-Yu Chen",
"Gaoyuan Zhang",
"Luca Daniel"
] | Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. However, these techniques can be computationally costly due to the use of certification during training. We develop a new regularizer that is both more efficient than existing certified defens... | main | Machine Learning | 10.1609/aaai.v35i8.16840 | 35 | 8 | 6803-6811 | official | 2102.01208 | title_snapshot |
10.1609/aaai.v35i8.16841 | Sample-Specific Output Constraints for Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16841 | https://ojs.aaai.org/index.php/AAAI/article/download/16841/16648 | [
"Mathis Brosowsky",
"Florian Keck",
"Olaf Dünkel",
"Marius Zöllner"
] | It is common practice to constrain the output space of a neural network with the final layer to a problem-specific value range. However, for many tasks it is desired to restrict the output space for each input independently to a different subdomain with a non-trivial geometry, e.g. in safety-critical applications, to e... | main | Machine Learning | 10.1609/aaai.v35i8.16841 | 35 | 8 | 6812-6821 | official | 2003.10258 | title_snapshot |
10.1609/aaai.v35i8.16842 | Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/16842 | https://ojs.aaai.org/index.php/AAAI/article/download/16842/16649 | [
"Brian Brubach",
"Darshan Chakrabarti",
"John P. Dickerson",
"Aravind Srinivasan",
"Leonidas Tsepenekas"
] | Metric clustering is fundamental in areas ranging from Combinatorial Optimization and Data Mining, to Machine Learning and Operations Research. However, in a variety of situations we may have additional requirements or knowledge, distinct from the underlying metric, regarding which pairs of points should be clustered t... | main | Machine Learning | 10.1609/aaai.v35i8.16842 | 35 | 8 | 6822-6830 | official | 2103.02013 | title_snapshot |
10.1609/aaai.v35i8.16811 | SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors | https://ojs.aaai.org/index.php/AAAI/article/view/16811 | https://ojs.aaai.org/index.php/AAAI/article/download/16811/16618 | [
"Ardavan Afshar",
"Kejing Yin",
"Sherry Yan",
"Cheng Qian",
"Joyce Ho",
"Haesun Park",
"Jimeng Sun"
] | Existing tensor factorization methods assume that the input tensor follows some specific distribution (i.e. Poisson, Bernoulli, and Gaussian), and solve the factorization by minimizing some empirical loss functions defined based on the corresponding distribution. However, it suffers from several drawbacks: 1) In realit... | main | Machine Learning | 10.1609/aaai.v35i8.16811 | 35 | 8 | 6548-6556 | official | 2010.04081 | title_snapshot |
10.1609/aaai.v35i8.16812 | DART: Adaptive Accept Reject Algorithm for Non-Linear Combinatorial Bandits | https://ojs.aaai.org/index.php/AAAI/article/view/16812 | https://ojs.aaai.org/index.php/AAAI/article/download/16812/16619 | [
"Mridul Agarwal",
"Vaneet Aggarwal",
"Abhishek Kumar Umrawal",
"Chris Quinn"
] | We consider the bandit problem of selecting K out of N arms at each time step. The joint reward can be a non-linear function of the rewards of the selected individual arms. The direct use of a multi-armed bandit algorithm requires choosing among all possible combinations, making the action space large. To simplify the ... | main | Machine Learning | 10.1609/aaai.v35i8.16812 | 35 | 8 | 6557-6565 | official | null | null |
10.1609/aaai.v35i8.16813 | Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration | https://ojs.aaai.org/index.php/AAAI/article/view/16813 | https://ojs.aaai.org/index.php/AAAI/article/download/16813/16620 | [
"Priyank Agrawal",
"Jinglin Chen",
"Nan Jiang"
] | This paper studies regret minimization with randomized value functions in reinforcement learning. In tabular finite-horizon Markov Decision Processes, we introduce a clipping variant of one classical Thompson Sampling (TS)-like algorithm, randomized least-squares value iteration (RLSVI). Our $\tilde{\mathrm{O}}(H^2S\sq... | main | Machine Learning | 10.1609/aaai.v35i8.16813 | 35 | 8 | 6566-6573 | official | 2010.12163 | title_snapshot |
10.1609/aaai.v35i8.16814 | Semi-supervised Sequence Classification through Change Point Detection | https://ojs.aaai.org/index.php/AAAI/article/view/16814 | https://ojs.aaai.org/index.php/AAAI/article/download/16814/16621 | [
"Nauman Ahad",
"Mark A. Davenport"
] | Sequential sensor data is generated in a wide variety of real-world applications. A fundamental machine learning challenge involves learning effective classifiers for such sequential data. While deep learning has led to impressive performance gains in recent years within domains such as speech, this has relied on the a... | main | Machine Learning | 10.1609/aaai.v35i8.16814 | 35 | 8 | 6574-6581 | official | 2009.11829 | title_snapshot |
10.1609/aaai.v35i8.16815 | Learning Invariant Representations using Inverse Contrastive Loss | https://ojs.aaai.org/index.php/AAAI/article/view/16815 | https://ojs.aaai.org/index.php/AAAI/article/download/16815/16622 | [
"Aditya Kumar Akash",
"Vishnu Suresh Lokhande",
"Sathya N. Ravi",
"Vikas Singh"
] | Learning invariant representations is a critical first step in a number of machine learning tasks. A common approach is given by the so-called information bottleneck principle in which an application dependent function of mutual information is carefully chosen and optimized. Unfortunately, in practice, these functions ... | main | Machine Learning | 10.1609/aaai.v35i8.16815 | 35 | 8 | 6582-6591 | official | 2102.08343 | title_snapshot |
10.1609/aaai.v35i8.16816 | Learned Bi-Resolution Image Coding using Generalized Octave Convolutions | https://ojs.aaai.org/index.php/AAAI/article/view/16816 | https://ojs.aaai.org/index.php/AAAI/article/download/16816/16623 | [
"Mohammad Akbari",
"Jie Liang",
"Jingning Han",
"Chengjie Tu"
] | Learned image compression has recently shown the potential to outperform the standard codecs. State-of-the-art rate-distortion (R-D) performance has been achieved by context-adaptive entropy coding approaches in which hyperprior and autoregressive models are jointly utilized to effectively capture the spatial dependenc... | main | Machine Learning | 10.1609/aaai.v35i8.16816 | 35 | 8 | 6592-6599 | official | null | null |
10.1609/aaai.v35i8.16817 | Deep Bayesian Quadrature Policy Optimization | https://ojs.aaai.org/index.php/AAAI/article/view/16817 | https://ojs.aaai.org/index.php/AAAI/article/download/16817/16624 | [
"Ravi Tej Akella",
"Kamyar Azizzadenesheli",
"Mohammad Ghavamzadeh",
"Animashree Anandkumar",
"Yisong Yue"
] | We study the problem of obtaining accurate policy gradient estimates using a finite number of samples. Monte-Carlo methods have been the default choice for policy gradient estimation, despite suffering from high variance in the gradient estimates. On the other hand, more sample efficient alternatives like Bayesian quad... | main | Machine Learning | 10.1609/aaai.v35i8.16817 | 35 | 8 | 6600-6608 | official | 2006.15637 | title_snapshot |
10.1609/aaai.v35i8.16818 | eTREE: Learning Tree-structured Embeddings | https://ojs.aaai.org/index.php/AAAI/article/view/16818 | https://ojs.aaai.org/index.php/AAAI/article/download/16818/16625 | [
"Faisal M. Almutairi",
"Yunlong Wang",
"Dong Wang",
"Emily Zhao",
"Nicholas D. Sidiropoulos"
] | Matrix factorization (MF) plays an important role in a wide range of machine learning and data mining models. MF is commonly used to obtain item embeddings and feature representations due to its ability to capture correlations and higher-order statistical dependencies across dimensions. In many applications, the catego... | main | Machine Learning | 10.1609/aaai.v35i8.16818 | 35 | 8 | 6609-6617 | official | 2012.10853 | title_snapshot |
10.1609/aaai.v35i8.16819 | Does Explainable Artificial Intelligence Improve Human Decision-Making? | https://ojs.aaai.org/index.php/AAAI/article/view/16819 | https://ojs.aaai.org/index.php/AAAI/article/download/16819/16626 | [
"Yasmeen Alufaisan",
"Laura R. Marusich",
"Jonathan Z. Bakdash",
"Yan Zhou",
"Murat Kantarcioglu"
] | Explainable AI provides insights to users into the why for model predictions, offering potential for users to better understand and trust a model, and to recognize and correct AI predictions that are incorrect. Prior research on human and explainable AI interactions has focused on measures such as interpretability, tru... | main | Machine Learning | 10.1609/aaai.v35i8.16819 | 35 | 8 | 6618-6626 | official | 2006.11194 | title_snapshot |
10.1609/aaai.v35i8.16820 | Decentralized Multi-Agent Linear Bandits with Safety Constraints | https://ojs.aaai.org/index.php/AAAI/article/view/16820 | https://ojs.aaai.org/index.php/AAAI/article/download/16820/16627 | [
"Sanae Amani",
"Christos Thrampoulidis"
] | We study decentralized stochastic linear bandits, where a network of N agents acts cooperatively to efficiently solve a linear bandit-optimization problem over a d-dimensional space. For this problem, we propose DLUCB: a fully decentralized algorithm that minimizes the cumulative regret over the entire network. At each... | main | Machine Learning | 10.1609/aaai.v35i8.16820 | 35 | 8 | 6627-6635 | official | 2012.00314 | title_snapshot |
10.1609/aaai.v35i8.16821 | Computing an Efficient Exploration Basis for Learning with Univariate Polynomial Features | https://ojs.aaai.org/index.php/AAAI/article/view/16821 | https://ojs.aaai.org/index.php/AAAI/article/download/16821/16628 | [
"Chaitanya Amballa",
"Manu K. Gupta",
"Sanjay P. Bhat"
] | Barycentric spanners have been used as an efficient exploration basis in online linear optimization problems in a bandit framework. We characterise the barycentric spanner for decision problems in which the cost (or reward) is a polynomial in a single decision variable. Our characterisation of the barycentric spanner i... | main | Machine Learning | 10.1609/aaai.v35i8.16821 | 35 | 8 | 6636-6643 | official | null | null |
10.1609/aaai.v35i8.16822 | Noise Estimation Using Density Estimation for Self-Supervised Multimodal Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16822 | https://ojs.aaai.org/index.php/AAAI/article/download/16822/16629 | [
"Elad Amrani",
"Rami Ben-Ari",
"Daniel Rotman",
"Alex Bronstein"
] | One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data. Unfortunately, annotation of multimodal data is challenging and expensive. Recently, self-supervised multimodal methods that combine vision and language were proposed to learn multimodal r... | main | Machine Learning | 10.1609/aaai.v35i8.16822 | 35 | 8 | 6644-6652 | official | 2003.03186 | title_snapshot |
10.1609/aaai.v35i9.17003 | Hypothesis Disparity Regularized Mutual Information Maximization | https://ojs.aaai.org/index.php/AAAI/article/view/17003 | https://ojs.aaai.org/index.php/AAAI/article/download/17003/16810 | [
"Qicheng Lao",
"Xiang Jiang",
"Mohammad Havaei"
] | We propose a hypothesis disparity regularized mutual information maximization (HDMI) approach to tackle unsupervised hypothesis transfer---as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA)---where the knowledge from a source domain is transferred solely through hy... | main | Machine Learning | 10.1609/aaai.v35i9.17003 | 35 | 9 | 8243-8251 | official | 2012.08072 | title_snapshot |
10.1609/aaai.v35i9.17004 | Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables | https://ojs.aaai.org/index.php/AAAI/article/view/17004 | https://ojs.aaai.org/index.php/AAAI/article/download/17004/16811 | [
"Miguel Lázaro-Gredilla",
"Wolfgang Lehrach",
"Nishad Gothoskar",
"Guangyao Zhou",
"Antoine Dedieu",
"Dileep George"
] | Probabilistic graphical models (PGMs) provide a compact representation of knowledge that can be queried in a flexible way: after learning the parameters of a graphical model once, new probabilistic queries can be answered at test time without retraining. However, when using undirected PGMS with hidden variables, two so... | main | Machine Learning | 10.1609/aaai.v35i9.17004 | 35 | 9 | 8252-8260 | official | 2006.06803 | title_snapshot |
10.1609/aaai.v35i9.17005 | Metrics and Continuity in Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17005 | https://ojs.aaai.org/index.php/AAAI/article/download/17005/16812 | [
"Charline Le Lan",
"Marc G. Bellemare",
"Pablo Samuel Castro"
] | In most practical applications of reinforcement learning, it is untenable to maintain direct estimates for individual states; in continuous-state systems, it is impossible. Instead, researchers often leverage {\em state similarity} (whether explicitly or implicitly) to build models that can generalize well from a limit... | main | Machine Learning | 10.1609/aaai.v35i9.17005 | 35 | 9 | 8261-8269 | official | 2102.01514 | title_snapshot |
10.1609/aaai.v35i9.17006 | Lipschitz Lifelong Reinforcement Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17006 | https://ojs.aaai.org/index.php/AAAI/article/download/17006/16813 | [
"Erwan Lecarpentier",
"David Abel",
"Kavosh Asadi",
"Yuu Jinnai",
"Emmanuel Rachelson",
"Michael L. Littman"
] | We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes and establish that close MDPs have close optimal value functions. Formally, the optimal value functions are Lipschitz continuous with respect... | main | Machine Learning | 10.1609/aaai.v35i9.17006 | 35 | 9 | 8270-8278 | official | 2001.05411 | title_snapshot |
10.1609/aaai.v35i9.17007 | Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/17007 | https://ojs.aaai.org/index.php/AAAI/article/download/17007/16814 | [
"Antoine Ledent",
"Waleed Mustafa",
"Yunwen Lei",
"Marius Kloft"
] | We show generalisation error bounds for deep learning with two main improvements over the state of the art. (1) Our bounds have no explicit dependence on the number of classes except for logarithmic factors. This holds even when formulating the bounds in terms of the Frobenius-norm of the weight matrices, where previou... | main | Machine Learning | 10.1609/aaai.v35i9.17007 | 35 | 9 | 8279-8287 | official | 1905.12430 | title_judge |
10.1609/aaai.v35i9.17008 | Learnable Dynamic Temporal Pooling for Time Series Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17008 | https://ojs.aaai.org/index.php/AAAI/article/download/17008/16815 | [
"Dongha Lee",
"Seonghyeon Lee",
"Hwanjo Yu"
] | With the increase of available time series data, predicting their class labels has been one of the most important challenges in a wide range of disciplines. Recent studies on time series classification show that convolutional neural networks (CNN) achieved the state-of-the-art performance as a single classifier. In thi... | main | Machine Learning | 10.1609/aaai.v35i9.17008 | 35 | 9 | 8288-8296 | official | 2104.02577 | title_snapshot |
10.1609/aaai.v35i9.17009 | Interpretable Embedding Procedure Knowledge Transfer via Stacked Principal Component Analysis and Graph Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/17009 | https://ojs.aaai.org/index.php/AAAI/article/download/17009/16816 | [
"Seunghyun Lee",
"Byung Cheol Song"
] | Knowledge distillation (KD) is one of the most useful techniques for light-weight neural networks. Although neural networks have a clear purpose of embedding datasets into the low-dimensional space, the existing knowledge was quite far from this purpose and provided only limited information. We argue that good knowledg... | main | Machine Learning | 10.1609/aaai.v35i9.17009 | 35 | 9 | 8297-8305 | official | 2104.13561 | title_snapshot |
10.1609/aaai.v35i9.17010 | Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer | https://ojs.aaai.org/index.php/AAAI/article/view/17010 | https://ojs.aaai.org/index.php/AAAI/article/download/17010/16817 | [
"Suhyeon Lee",
"Junhyuk Hyun",
"Hongje Seong",
"Euntai Kim"
] | In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main problem of UDA for semantic segmentation relies on reducing the domain gap between the real image and synthetic image. To solve this problem, ... | main | Machine Learning | 10.1609/aaai.v35i9.17010 | 35 | 9 | 8306-8315 | official | 2012.12545 | title_snapshot |
10.1609/aaai.v35i9.17011 | Memory and Computation-Efficient Kernel SVM via Binary Embedding and Ternary Model Coefficients | https://ojs.aaai.org/index.php/AAAI/article/view/17011 | https://ojs.aaai.org/index.php/AAAI/article/download/17011/16818 | [
"Zijian Lei",
"Liang Lan"
] | Kernel approximation is widely used to scale up kernel SVM training and prediction. However, the memory and computation costs of kernel approximation models are still too large if we want to deploy them on memory-limited devices such as mobile phones, smart watches and IoT devices. To address this challenge, we propose... | main | Machine Learning | 10.1609/aaai.v35i9.17011 | 35 | 9 | 8316-8323 | official | 2010.02577 | title_snapshot |
10.1609/aaai.v35i9.17012 | Enhancing Parameter-Free Frank Wolfe with an Extra Subproblem | https://ojs.aaai.org/index.php/AAAI/article/view/17012 | https://ojs.aaai.org/index.php/AAAI/article/download/17012/16819 | [
"Bingcong Li",
"Lingda Wang",
"Georgios B. Giannakis",
"Zhizhen Zhao"
] | Aiming at convex optimization under structural constraints, this work introduces and analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of ExtraFW is the pair of gradients leveraged per iteration, thanks to which the decision variable is updated in a prediction-correction (PC) for... | main | Machine Learning | 10.1609/aaai.v35i9.17012 | 35 | 9 | 8324-8331 | official | 2012.05284 | title_snapshot |
10.1609/aaai.v35i9.17013 | Unsupervised Active Learning via Subspace Learning | https://ojs.aaai.org/index.php/AAAI/article/view/17013 | https://ojs.aaai.org/index.php/AAAI/article/download/17013/16820 | [
"Changsheng Li",
"Kaihang Mao",
"Lingyan Liang",
"Dongchun Ren",
"Wei Zhang",
"Ye Yuan",
"Guoren Wang"
] | Unsupervised active learning has been an active research topic in machine learning community, with the purpose of choosing representative samples to be labelled in an unsupervised manner. Previous works usually take the minimization of data reconstruction loss as the criterion to select representative samples which can... | main | Machine Learning | 10.1609/aaai.v35i9.17013 | 35 | 9 | 8332-8339 | official | null | null |
10.1609/aaai.v35i9.17014 | LRSC: Learning Representations for Subspace Clustering | https://ojs.aaai.org/index.php/AAAI/article/view/17014 | https://ojs.aaai.org/index.php/AAAI/article/download/17014/16821 | [
"Changsheng Li",
"Chen Yang",
"Bo Liu",
"Ye Yuan",
"Guoren Wang"
] | Deep learning based subspace clustering methods have attracted increasing attention in recent years, where a basic theme is to non-linearly map data into a latent space, and then uncover subspace structures based upon the data self-expressiveness property. However, almost all existing deep subspace clustering methods o... | main | Machine Learning | 10.1609/aaai.v35i9.17014 | 35 | 9 | 8340-8348 | official | null | null |
10.1609/aaai.v35i9.17015 | GoT: a Growing Tree Model for Clustering Ensemble | https://ojs.aaai.org/index.php/AAAI/article/view/17015 | https://ojs.aaai.org/index.php/AAAI/article/download/17015/16822 | [
"Feijiang Li",
"Yuhua Qian",
"Jieting Wang"
] | The clustering ensemble technique that integrates multiple clustering results can improve the accuracy and robustness of the final clustering. In many clustering ensemble algorithms, the co-association matrix (CA matrix), which reflects the frequency of any two samples being partitioned into the same cluster, plays an ... | main | Machine Learning | 10.1609/aaai.v35i9.17015 | 35 | 9 | 8349-8356 | official | null | null |
10.1609/aaai.v35i9.17016 | VSQL: Variational Shadow Quantum Learning for Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17016 | https://ojs.aaai.org/index.php/AAAI/article/download/17016/16823 | [
"Guangxi Li",
"Zhixin Song",
"Xin Wang"
] | Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shado... | main | Machine Learning | 10.1609/aaai.v35i9.17016 | 35 | 9 | 8357-8365 | official | 2012.08288 | title_snapshot |
10.1609/aaai.v35i9.17017 | High Fidelity GAN Inversion via Prior Multi-Subspace Feature Composition | https://ojs.aaai.org/index.php/AAAI/article/view/17017 | https://ojs.aaai.org/index.php/AAAI/article/download/17017/16824 | [
"Guanyue Li",
"Qianfen Jiao",
"Sheng Qian",
"Si Wu",
"Hau-San Wong"
] | Generative Adversarial Networks (GANs) have shown impressive gains in image synthesis. GAN inversion was recently studied to understand and utilize the knowledge it learns, where a real image is inverted back to a latent code and can thus be reconstructed by the generator. Although increasing the number of latent codes... | main | Machine Learning | 10.1609/aaai.v35i9.17017 | 35 | 9 | 8366-8374 | official | null | null |
10.1609/aaai.v35i9.17018 | ShapeNet: A Shapelet-Neural Network Approach for Multivariate Time Series Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17018 | https://ojs.aaai.org/index.php/AAAI/article/download/17018/16825 | [
"Guozhong Li",
"Byron Choi",
"Jianliang Xu",
"Sourav S Bhowmick",
"Kwok-Pan Chun",
"Grace Lai-Hung Wong"
] | Time series shapelets are short discriminative subsequences that recently have been found not only to be accurate but also interpretable for the classification problem of univariate time series (UTS). However, existing work on shapelets selection cannot be applied to multivariate time series classification (MTSC) since... | main | Machine Learning | 10.1609/aaai.v35i9.17018 | 35 | 9 | 8375-8383 | official | null | null |
10.1609/aaai.v35i9.17019 | A Bayesian Approach for Subset Selection in Contextual Bandits | https://ojs.aaai.org/index.php/AAAI/article/view/17019 | https://ojs.aaai.org/index.php/AAAI/article/download/17019/16826 | [
"Jialian Li",
"Chao Du",
"Jun Zhu"
] | Subset selection in Contextual Bandits (CB) is an important task in various applications such as advertisement recommendation. In CB, arms are attached with contexts and thus correlated in the context space. Proper exploration for subset selection in CB should carefully consider the contexts. Previous works mainly conc... | main | Machine Learning | 10.1609/aaai.v35i9.17019 | 35 | 9 | 8384-8391 | official | null | null |
10.1609/aaai.v35i9.17020 | Self-Paced Two-dimensional PCA | https://ojs.aaai.org/index.php/AAAI/article/view/17020 | https://ojs.aaai.org/index.php/AAAI/article/download/17020/16827 | [
"Jiangxin Li",
"Zhao Kang",
"Chong Peng",
"Wenyu Chen"
] | Two-dimensional PCA (2DPCA) is an effective approach to reduce dimension and extract features in the image domain. Most recently developed techniques use different error measures to improve their robustness to outliers. When certain data points are overly contaminated, the existing methods are frequently incapable of f... | main | Machine Learning | 10.1609/aaai.v35i9.17020 | 35 | 9 | 8392-8400 | official | null | null |
10.1609/aaai.v35i9.17021 | Learning Intact Features by Erasing-Inpainting for Few-shot Classification | https://ojs.aaai.org/index.php/AAAI/article/view/17021 | https://ojs.aaai.org/index.php/AAAI/article/download/17021/16828 | [
"Junjie Li",
"Zilei Wang",
"Xiaoming Hu"
] | Few-shot classification aims to categorize the samples from unseen classes with only few labeled samples. To address such a challenge, many methods exploit a base set consisting of massive labeled samples to learn an instance embedding function, i.e., image feature extractor, and it is expected to possess good transfer... | main | Machine Learning | 10.1609/aaai.v35i9.17021 | 35 | 9 | 8401-8409 | official | null | null |
10.1609/aaai.v35i9.17022 | Token-Aware Virtual Adversarial Training in Natural Language Understanding | https://ojs.aaai.org/index.php/AAAI/article/view/17022 | https://ojs.aaai.org/index.php/AAAI/article/download/17022/16829 | [
"Linyang Li",
"Xipeng Qiu"
] | Gradient-based adversarial training is widely used in improving the robustness of neural networks, while it cannot be easily adapted to natural language processing tasks since the embedding space is discrete. In natural language processing fields, virtual adversarial training is introduced since texts are discrete and ... | main | Machine Learning | 10.1609/aaai.v35i9.17022 | 35 | 9 | 8410-8418 | official | 2004.14543 | title_judge |
10.1609/aaai.v35i9.16983 | Learning Generalized Relational Heuristic Networks for Model-Agnostic Planning | https://ojs.aaai.org/index.php/AAAI/article/view/16983 | https://ojs.aaai.org/index.php/AAAI/article/download/16983/16790 | [
"Rushang Karia",
"Siddharth Srivastava"
] | Computing goal-directed behavior is essential to designing efficient AI systems. Due to the computational complexity of planning, current approaches rely primarily upon hand-coded symbolic action models and hand-coded heuristic function generators for efficiency. Learned heuristics for such problems have been of limite... | main | Machine Learning | 10.1609/aaai.v35i9.16983 | 35 | 9 | 8064-8073 | official | 2007.06702 | title_snapshot |
10.1609/aaai.v35i9.16984 | A Recipe for Global Convergence Guarantee in Deep Neural Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16984 | https://ojs.aaai.org/index.php/AAAI/article/download/16984/16791 | [
"Kenji Kawaguchi",
"Qingyun Sun"
] | Existing global convergence guarantees of (stochastic) gradient descent do not apply to practical deep networks in the practical regime of deep learning beyond the neural tangent kernel (NTK) regime. This paper proposes an algorithm, which is ensured to have global convergence guarantees in the practical regime beyond ... | main | Machine Learning | 10.1609/aaai.v35i9.16984 | 35 | 9 | 8074-8082 | official | 2104.05785 | title_snapshot |
10.1609/aaai.v35i9.16985 | Bayesian Dynamic Mode Decomposition with Variational Matrix Factorization | https://ojs.aaai.org/index.php/AAAI/article/view/16985 | https://ojs.aaai.org/index.php/AAAI/article/download/16985/16792 | [
"Takahiro Kawashima",
"Hayaru Shouno",
"Hideitsu Hino"
] | Dynamic mode decomposition (DMD) and its extensions are data-driven methods that have substantially contributed to our understanding of dynamical systems. However, because DMD and most of its extensions are deterministic, it is difficult to treat probabilistic representations of parameters and predictions. In this work... | main | Machine Learning | 10.1609/aaai.v35i9.16985 | 35 | 9 | 8083-8091 | official | null | null |
10.1609/aaai.v35i9.16986 | Improving Fairness and Privacy in Selection Problems | https://ojs.aaai.org/index.php/AAAI/article/view/16986 | https://ojs.aaai.org/index.php/AAAI/article/download/16986/16793 | [
"Mohammad Mahdi Khalili",
"Xueru Zhang",
"Mahed Abroshan",
"Somayeh Sojoudi"
] | Supervised learning models have been increasingly used for making decisions about individuals in applications such as hiring, lending, and college admission. These models may inherit pre-existing biases from training datasets and discriminate against protected attributes (e.g., race or gender). In addition to unfairnes... | main | Machine Learning | 10.1609/aaai.v35i9.16986 | 35 | 9 | 8092-8100 | official | 2012.03812 | title_snapshot |
10.1609/aaai.v35i9.16987 | A Flexible Framework for Communication-Efficient Machine Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16987 | https://ojs.aaai.org/index.php/AAAI/article/download/16987/16794 | [
"Sarit Khirirat",
"Sindri Magnússon",
"Arda Aytekin",
"Mikael Johansson"
] | With the increasing scale of machine learning tasks, it has become essential to reduce the communication between computing nodes. Early work on gradient compression focused on the bottleneck between CPUs and GPUs, but communication-efficiency is now needed in a variety of different system architectures, from high-perfo... | main | Machine Learning | 10.1609/aaai.v35i9.16987 | 35 | 9 | 8101-8109 | official | null | null |
10.1609/aaai.v35i9.16988 | GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning | https://ojs.aaai.org/index.php/AAAI/article/view/16988 | https://ojs.aaai.org/index.php/AAAI/article/download/16988/16795 | [
"Krishnateja Killamsetty",
"Durga Sivasubramanian",
"Ganesh Ramakrishnan",
"Rishabh Iyer"
] | Large scale machine learning and deep models are extremely data-hungry. Unfortunately, obtaining large amounts of labeled data is expensive, and training state-of-the-art models (with hyperparameter tuning) requires significant computing resources and time. Secondly, real-world data is noisy and imbalanced. As a result... | main | Machine Learning | 10.1609/aaai.v35i9.16988 | 35 | 9 | 8110-8118 | official | 2012.10630 | title_snapshot |
10.1609/aaai.v35i9.16989 | Understanding Catastrophic Overfitting in Single-step Adversarial Training | https://ojs.aaai.org/index.php/AAAI/article/view/16989 | https://ojs.aaai.org/index.php/AAAI/article/download/16989/16796 | [
"Hoki Kim",
"Woojin Lee",
"Jaewook Lee"
] | Although fast adversarial training has demonstrated both robustness and efficiency, the problem of "catastrophic overfitting" has been observed. This is a phenomenon in which, during single-step adversarial training, the robust accuracy against projected gradient descent (PGD) suddenly decreases to 0% after a few epoch... | main | Machine Learning | 10.1609/aaai.v35i9.16989 | 35 | 9 | 8119-8127 | official | 2010.01799 | title_snapshot |
10.1609/aaai.v35i9.16990 | Counterfactual Fairness with Disentangled Causal Effect Variational Autoencoder | https://ojs.aaai.org/index.php/AAAI/article/view/16990 | https://ojs.aaai.org/index.php/AAAI/article/download/16990/16797 | [
"Hyemi Kim",
"Seungjae Shin",
"JoonHo Jang",
"Kyungwoo Song",
"Weonyoung Joo",
"Wanmo Kang",
"Il-Chul Moon"
] | The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contra... | main | Machine Learning | 10.1609/aaai.v35i9.16990 | 35 | 9 | 8128-8136 | official | 2011.11878 | title_snapshot |
10.1609/aaai.v35i9.16991 | Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network | https://ojs.aaai.org/index.php/AAAI/article/view/16991 | https://ojs.aaai.org/index.php/AAAI/article/download/16991/16798 | [
"Jong-Yeong Kim",
"Dong-Wan Choi"
] | Continual learning has been a major problem in the deep learning community, where the main challenge is how to effectively learn a series of newly arriving tasks without forgetting the knowledge of previous tasks. Initiated by Learning without Forgetting (LwF), many of the existing works report that knowledge distillat... | main | Machine Learning | 10.1609/aaai.v35i9.16991 | 35 | 9 | 8137-8145 | official | 2107.01349 | title_snapshot |
10.1609/aaai.v35i9.16992 | DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation | https://ojs.aaai.org/index.php/AAAI/article/view/16992 | https://ojs.aaai.org/index.php/AAAI/article/download/16992/16799 | [
"Jungeun Kim",
"Kookjin Lee",
"Dongeun Lee",
"Sheo Yon Jhin",
"Noseong Park"
] | We present a method for learning dynamics of complex physical processes described by time-dependent nonlinear partial differential equations (PDEs). Our particular interest lies in extrapolating solutions in time beyond the range of temporal domain used in training. Our choice for a baseline method is physics-informed ... | main | Machine Learning | 10.1609/aaai.v35i9.16992 | 35 | 9 | 8146-8154 | official | 2012.02681 | title_snapshot |
10.1609/aaai.v35i9.16993 | Kernel-convoluted Deep Neural Networks with Data Augmentation | https://ojs.aaai.org/index.php/AAAI/article/view/16993 | https://ojs.aaai.org/index.php/AAAI/article/download/16993/16800 | [
"Minjin Kim",
"Young-geun Kim",
"Dongha Kim",
"Yongdai Kim",
"Myunghee Cho Paik"
] | The Mixup method, which uses linearly interpolated data, has emerged as an effective data augmentation tool to improve generalization performance and the robustness to adversarial examples. The motivation is to curtail undesirable oscillations by its implicit model constraint to behave linearly at in-between observed d... | main | Machine Learning | 10.1609/aaai.v35i9.16993 | 35 | 9 | 8155-8162 | official | 2012.02521 | title_snapshot |
10.1609/aaai.v35i9.16994 | Neural Sequence-to-grid Module for Learning Symbolic Rules | https://ojs.aaai.org/index.php/AAAI/article/view/16994 | https://ojs.aaai.org/index.php/AAAI/article/download/16994/16801 | [
"Segwang Kim",
"Hyoungwook Nam",
"Joonyoung Kim",
"Kyomin Jung"
] | Logical reasoning tasks over symbols, such as learning arithmetic operations and computer program evaluations, have become challenges to deep learning. In particular, even state-of-the-art neural networks fail to achieve \textit{out-of-distribution} (OOD) generalization of symbolic reasoning tasks, whereas humans can e... | main | Machine Learning | 10.1609/aaai.v35i9.16994 | 35 | 9 | 8163-8171 | official | 2101.04921 | title_snapshot |
10.1609/aaai.v35i9.16995 | Visual Concept Reasoning Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16995 | https://ojs.aaai.org/index.php/AAAI/article/download/16995/16802 | [
"Taesup Kim",
"Sungwoong Kim",
"Yoshua Bengio"
] | A split-transform-merge strategy has been broadly used as an architectural constraint in convolutional neural networks for visual recognition tasks. It approximates sparsely connected networks by explicitly defining multiple branches to simultaneously learn representations with different visual concepts or properties. ... | main | Machine Learning | 10.1609/aaai.v35i9.16995 | 35 | 9 | 8172-8180 | official | 2008.11783 | title_snapshot |
10.1609/aaai.v35i9.16996 | Sparsity Aware Normalization for GANs | https://ojs.aaai.org/index.php/AAAI/article/view/16996 | https://ojs.aaai.org/index.php/AAAI/article/download/16996/16803 | [
"Idan Kligvasser",
"Tomer Michaeli"
] | Generative adversarial networks (GANs) are known to benefit from regularization or normalization of their critic (discriminator) network during training. In this paper, we analyze the popular spectral normalization scheme, find a significant drawback and introduce sparsity aware normalization (SAN), a new alternative a... | main | Machine Learning | 10.1609/aaai.v35i9.16996 | 35 | 9 | 8181-8190 | official | 2103.02458 | title_snapshot |
10.1609/aaai.v35i9.16997 | HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference | https://ojs.aaai.org/index.php/AAAI/article/view/16997 | https://ojs.aaai.org/index.php/AAAI/article/download/16997/16804 | [
"Jakob Kruse",
"Gianluca Detommaso",
"Ullrich Köthe",
"Robert Scheichl"
] | Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation. While such a transformation is invertible, its Jacobian is very sparse and thus may lack expressiveness. Th... | main | Machine Learning | 10.1609/aaai.v35i9.16997 | 35 | 9 | 8191-8199 | official | 1905.10687 | title_snapshot |
10.1609/aaai.v35i9.16998 | Nearly Linear-Time, Parallelizable Algorithms for Non-Monotone Submodular Maximization | https://ojs.aaai.org/index.php/AAAI/article/view/16998 | https://ojs.aaai.org/index.php/AAAI/article/download/16998/16805 | [
"Alan Kuhnle"
] | We study combinatorial, parallelizable algorithms for maximization of a submodular function, not necessarily monotone, with respect to a cardinality constraint k. We improve the best approximation factor achieved by an algorithm that has optimal adaptivity and query complexity, up to logarithmic factors in the size of ... | main | Machine Learning | 10.1609/aaai.v35i9.16998 | 35 | 9 | 8200-8208 | official | 2009.01947 | title_judge |
10.1609/aaai.v35i9.16999 | Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees | https://ojs.aaai.org/index.php/AAAI/article/view/16999 | https://ojs.aaai.org/index.php/AAAI/article/download/16999/16806 | [
"Vyacheslav Kungurtsev",
"Malcolm Egan",
"Bapi Chatterjee",
"Dan Alistarh"
] | Asynchronous distributed algorithms are a popular way to reduce synchronization costs in large-scale optimization, and in particular for neural network training. However, for nonsmooth and nonconvex objectives, few convergence guarantees exist beyond cases where closed-form proximal operator solutions are available. As... | main | Machine Learning | 10.1609/aaai.v35i9.16999 | 35 | 9 | 8209-8216 | official | 1905.11845 | title_snapshot |
10.1609/aaai.v35i9.17000 | Positions, Channels, and Layers: Fully Generalized Non-Local Network for Singer Identification | https://ojs.aaai.org/index.php/AAAI/article/view/17000 | https://ojs.aaai.org/index.php/AAAI/article/download/17000/16807 | [
"I-Yuan Kuo",
"Wen-Li Wei",
"Jen-Chun Lin"
] | Recently, a non-local (NL) operation has been designed as the central building block for deep-net models to capture long-range dependencies (Wang et al. 2018). Despite its excellent performance, it does not consider the interaction between positions across channels and layers, which is crucial in fine-grained classific... | main | Machine Learning | 10.1609/aaai.v35i9.17000 | 35 | 9 | 8217-8225 | official | null | null |
10.1609/aaai.v35i9.17001 | MolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation | https://ojs.aaai.org/index.php/AAAI/article/view/17001 | https://ojs.aaai.org/index.php/AAAI/article/download/17001/16808 | [
"Maksim Kuznetsov",
"Daniil Polykovskiy"
] | We propose a hierarchical normalizing flow model for generating molecular graphs. The model produces new molecular structures from a single-node graph by recursively splitting every node into two. All operations are invertible and can be used as plug-and-play modules. The hierarchical nature of the latent codes allows ... | main | Machine Learning | 10.1609/aaai.v35i9.17001 | 35 | 9 | 8226-8234 | official | 2106.05856 | title_snapshot |
10.1609/aaai.v35i9.17002 | Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters | https://ojs.aaai.org/index.php/AAAI/article/view/17002 | https://ojs.aaai.org/index.php/AAAI/article/download/17002/16809 | [
"Weichao Lan",
"Liang Lan"
] | Deep Convolutional Neural Networks (CNN) have been successfully applied to many real-life problems. However, the huge memory cost of deep CNN models poses a great challenge of deploying them on memory-constrained devices (e.g., mobile phones). One popular way to reduce the memory cost of deep CNN model is to train bina... | main | Machine Learning | 10.1609/aaai.v35i9.17002 | 35 | 9 | 8235-8242 | official | 2010.02778 | title_snapshot |
10.1609/aaai.v35i9.16963 | Accurate and Robust Feature Importance Estimation under Distribution Shifts | https://ojs.aaai.org/index.php/AAAI/article/view/16963 | https://ojs.aaai.org/index.php/AAAI/article/download/16963/16770 | [
"Jayaraman J. Thiagarajan",
"Vivek Narayanaswamy",
"Rushil Anirudh",
"Peer-Timo Bremer",
"Andreas Spanias"
] | With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often used to enable humans understand and trust these models. In particular, we focus on the class of methods that can reveal the influence of input... | main | Machine Learning | 10.1609/aaai.v35i9.16963 | 35 | 9 | 7891-7898 | official | 2009.14454 | title_snapshot |
10.1609/aaai.v35i9.16964 | Variance Penalized On-Policy and Off-Policy Actor-Critic | https://ojs.aaai.org/index.php/AAAI/article/view/16964 | https://ojs.aaai.org/index.php/AAAI/article/download/16964/16771 | [
"Arushi Jain",
"Gandharv Patil",
"Ayush Jain",
"Khimya Khetarpal",
"Doina Precup"
] | Reinforcement learning algorithms are typically geared towards optimizing the expected return of an agent. However, in many practical applications, low variance in the return is desired to ensure the reliability of an algorithm. In this paper, we propose on-policy and off-policy actor-critic algorithms that optimize a ... | main | Machine Learning | 10.1609/aaai.v35i9.16964 | 35 | 9 | 7899-7907 | official | 2102.01985 | title_snapshot |
10.1609/aaai.v35i9.16965 | Constructing a Fair Classifier with Generated Fair Data | https://ojs.aaai.org/index.php/AAAI/article/view/16965 | https://ojs.aaai.org/index.php/AAAI/article/download/16965/16772 | [
"Taeuk Jang",
"Feng Zheng",
"Xiaoqian Wang"
] | Fairness in machine learning is getting rising attention as it is directly related to real-world applications and social problems. Recent methods have been explored to alleviate the discrimination between certain demographic groups that are characterized by sensitive attributes (such as race, age, or gender). Some stud... | main | Machine Learning | 10.1609/aaai.v35i9.16965 | 35 | 9 | 7908-7916 | official | null | null |
10.1609/aaai.v35i9.16966 | Neural Utility Functions | https://ojs.aaai.org/index.php/AAAI/article/view/16966 | https://ojs.aaai.org/index.php/AAAI/article/download/16966/16773 | [
"Porter Jenkins",
"Ahmad Farag",
"J. Stockton Jenkins",
"Huaxiu Yao",
"Suhang Wang",
"Zhenhui Li"
] | Current neural network architectures have no mechanism for explicitly reasoning about item trade-offs. Such trade-offs are important for popular tasks such as recommendation. The main idea of this work is to give neural networks inductive biases that are inspired by economic theories. To this end, we propose Neural Uti... | main | Machine Learning | 10.1609/aaai.v35i9.16966 | 35 | 9 | 7917-7925 | official | null | null |
10.1609/aaai.v35i9.16967 | IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks | https://ojs.aaai.org/index.php/AAAI/article/view/16967 | https://ojs.aaai.org/index.php/AAAI/article/download/16967/16774 | [
"Insu Jeon",
"Wonkwang Lee",
"Myeongjang Pyeon",
"Gunhee Kim"
] | We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framework to the optimization of GAN, thereby named IB-GAN. The architecture of IB-GAN is partially similar to that of InfoGAN but has a critical d... | main | Machine Learning | 10.1609/aaai.v35i9.16967 | 35 | 9 | 7926-7934 | official | 2510.20165 | title_snapshot |
10.1609/aaai.v35i9.16968 | Active Bayesian Assessment of Black-Box Classifiers | https://ojs.aaai.org/index.php/AAAI/article/view/16968 | https://ojs.aaai.org/index.php/AAAI/article/download/16968/16775 | [
"Disi Ji",
"Robert L. Logan",
"Padhraic Smyth",
"Mark Steyvers"
] | Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that... | main | Machine Learning | 10.1609/aaai.v35i9.16968 | 35 | 9 | 7935-7944 | official | 2002.06532 | title_judge |
10.1609/aaai.v35i9.16969 | Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching | https://ojs.aaai.org/index.php/AAAI/article/view/16969 | https://ojs.aaai.org/index.php/AAAI/article/download/16969/16776 | [
"Mingi Ji",
"Byeongho Heo",
"Sungrae Park"
] | Knowledge distillation extracts general knowledge from a pretrained teacher network and provides guidance to a target student network. Most studies manually tie intermediate features of the teacher and student, and transfer knowledge through predefined links. However, manual selection often constructs ineffective links... | main | Machine Learning | 10.1609/aaai.v35i9.16969 | 35 | 9 | 7945-7952 | official | 2102.02973 | title_snapshot |
10.1609/aaai.v35i9.16970 | Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications | https://ojs.aaai.org/index.php/AAAI/article/view/16970 | https://ojs.aaai.org/index.php/AAAI/article/download/16970/16777 | [
"Taoran Ji",
"Nathan Self",
"Kaiqun Fu",
"Zhiqian Chen",
"Naren Ramakrishnan",
"Chang-Tien Lu"
] | Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing ... | main | Machine Learning | 10.1609/aaai.v35i9.16970 | 35 | 9 | 7953-7960 | official | null | null |
10.1609/aaai.v35i9.16971 | Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks | https://ojs.aaai.org/index.php/AAAI/article/view/16971 | https://ojs.aaai.org/index.php/AAAI/article/download/16971/16778 | [
"Jinyuan Jia",
"Xiaoyu Cao",
"Neil Zhenqiang Gong"
] | In a data poisoning attack, an attacker modifies, deletes, and/or inserts some training examples to corrupt the learnt machine learning model. Bootstrap Aggregating (bagging) is a well known ensemble learning method, which trains multiple base models on random subsamples of a training dataset using a base learning algo... | main | Machine Learning | 10.1609/aaai.v35i9.16971 | 35 | 9 | 7961-7969 | official | 2008.04495 | title_snapshot |
10.1609/aaai.v35i9.16972 | Clustering Ensemble Meets Low-rank Tensor Approximation | https://ojs.aaai.org/index.php/AAAI/article/view/16972 | https://ojs.aaai.org/index.php/AAAI/article/download/16972/16779 | [
"Yuheng Jia",
"Hui Liu",
"Junhui Hou",
"Qingfu Zhang"
] | This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the wei... | main | Machine Learning | 10.1609/aaai.v35i9.16972 | 35 | 9 | 7970-7978 | official | 2012.08916 | title_snapshot |
10.1609/aaai.v35i9.16973 | Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/16973 | https://ojs.aaai.org/index.php/AAAI/article/download/16973/16780 | [
"Haobo Jiang",
"Jin Xie",
"Jian Yang"
] | Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning, employs the clipped double estimator to approximate the maximum expected action value. Due to the underestimation bias of the clipped double... | main | Machine Learning | 10.1609/aaai.v35i9.16973 | 35 | 9 | 7979-7986 | official | 2105.00704 | title_snapshot |
10.1609/aaai.v35i9.16974 | LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification | https://ojs.aaai.org/index.php/AAAI/article/view/16974 | https://ojs.aaai.org/index.php/AAAI/article/download/16974/16781 | [
"Ting Jiang",
"Deqing Wang",
"Leilei Sun",
"Huayi Yang",
"Zhengyang Zhao",
"Fuzhen Zhuang"
] | Extreme multi-label text classification(XMC) is a task for finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train ... | main | Machine Learning | 10.1609/aaai.v35i9.16974 | 35 | 9 | 7987-7994 | official | 2101.03305 | title_snapshot |
10.1609/aaai.v35i9.16975 | Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks | https://ojs.aaai.org/index.php/AAAI/article/view/16975 | https://ojs.aaai.org/index.php/AAAI/article/download/16975/16782 | [
"Yuqian Jiang",
"Suda Bharadwaj",
"Bo Wu",
"Rishi Shah",
"Ufuk Topcu",
"Peter Stone"
] | In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an optimal policy in this setting typically requires a large amount of training experiences. Reward shaping is a common approach for incorporati... | main | Machine Learning | 10.1609/aaai.v35i9.16975 | 35 | 9 | 7995-8003 | official | 2007.01498 | title_snapshot |
10.1609/aaai.v35i9.16976 | Power up! Robust Graph Convolutional Network via Graph Powering | https://ojs.aaai.org/index.php/AAAI/article/view/16976 | https://ojs.aaai.org/index.php/AAAI/article/download/16976/16783 | [
"Ming Jin",
"Heng Chang",
"Wenwu Zhu",
"Somayeh Sojoudi"
] | Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be vulnerable to topological attacks. To enhance adversarial robustness, we go beyond spectral graph theory to robust graph theory. By challenging the classical graph Laplacian, we propose a new c... | main | Machine Learning | 10.1609/aaai.v35i9.16976 | 35 | 9 | 8004-8012 | official | 1905.10029 | title_snapshot |
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