Papers
A Difference Standardization Method for Mutual Transfer Learning
Haoqing Xu, Meng Wang, Beilun Wang
A Differential Entropy Estimator for Training Neural Networks
Georg Pichler, Pierre Jean A. Colombo, Malik Boudiaf et al.
Adversarial Attack and Defense for Non-Parametric Two-Sample Tests
Xilie Xu, Jingfeng Zhang, Feng Liu et al.
Adversarial Attacks on Gaussian Process Bandits
Eric Han, Jonathan Scarlett
Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization
Xiaojun Xu, Jacky Y Zhang, Evelyn Ma et al.
Adversarially Trained Actor Critic for Offline Reinforcement Learning
Ching-An Cheng, Tengyang Xie, Nan Jiang et al.
Adversarially trained neural representations may already be as robust as corresponding biological neural representations
Chong Guo, Michael Lee, Guillaume Leclerc et al.
Adversarial Masking for Self-Supervised Learning
Yuge Shi, N Siddharth, Philip Torr et al.
Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers
Francesco Croce, Matthias Hein
Adversarial Vulnerability of Randomized Ensembles
Hassan Dbouk, Naresh Shanbhag
A Dynamical System Perspective for Lipschitz Neural Networks
Laurent Meunier, Blaise J Delattre, Alexandre Araujo et al.
A Framework for Learning to Request Rich and Contextually Useful Information from Humans
Khanh X Nguyen, Yonatan Bisk, Hal Daumé Iii
A Functional Information Perspective on Model Interpretation
Itai Gat, Nitay Calderon, Roi Reichart et al.
A General Recipe for Likelihood-free Bayesian Optimization
Jiaming Song, Lantao Yu, Willie Neiswanger et al.
AGNAS: Attention-Guided Micro and Macro-Architecture Search
Zihao Sun, Yu Hu, Shun Lu et al.
Agnostic Learnability of Halfspaces via Logistic Loss
Ziwei Ji, Kwangjun Ahn, Pranjal Awasthi et al.
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
Weichao Zhou, Wenchao Li
A Hierarchical Transitive-Aligned Graph Kernel for Un-attributed Graphs
Lu Bai, Lixin Cui, Hancock Edwin
A Joint Exponential Mechanism For Differentially Private Top-$k$
Jennifer Gillenwater, Matthew Joseph, Andres Munoz et al.
A Langevin-like Sampler for Discrete Distributions
Ruqi Zhang, Xingchao Liu, Qiang Liu
Algorithms for the Communication of Samples
Lucas Theis, Noureldin Y Ahmed
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu et al.
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Luca Carminati, Federico Cacciamani, Marco Ciccone et al.
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
Chengchun Shi, Masatoshi Uehara, Jiawei Huang et al.