Papers
Adaptive Gradient Descent without Descent
Yura Malitsky, Konstantin Mishchenko
Adaptive Region-Based Active Learning
Corinna Cortes, Giulia Desalvo, Claudio Gentile et al.
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
Xuezhou Zhang, Yuzhe Ma, Adish Singla et al.
Adaptive Sampling for Estimating Probability Distributions
Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh
Adaptive Sketching for Fast and Convergent Canonical Polyadic Decomposition
Alex Gittens, Kareem Aggour, Bülent Yener
AdaScale SGD: A User-Friendly Algorithm for Distributed Training
Tyler Johnson, Pulkit Agrawal, Haijie Gu et al.
Adding seemingly uninformative labels helps in low data regimes
Christos Matsoukas, Albert Bou Hernandez, Yue Liu et al.
A Distributional Framework For Data Valuation
Amirata Ghorbani, Michael Kim, James Zou
A distributional view on multi-objective policy optimization
Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever et al.
Adversarial Attacks on Copyright Detection Systems
Parsa Saadatpanah, Ali Shafahi, Tom Goldstein
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models
Raphaël Dang-Nhu, Gagandeep Singh, Pavol Bielik et al.
Adversarial Filters of Dataset Biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula et al.
Adversarial Learning Guarantees for Linear Hypotheses and Neural Networks
Pranjal Awasthi, Natalie Frank, Mehryar Mohri
Adversarial Mutual Information for Text Generation
Boyuan Pan, Yazheng Yang, Kaizhao Liang et al.
Adversarial Neural Pruning with Latent Vulnerability Suppression
Divyam Madaan, Jinwoo Shin, Sung Ju Hwang
Adversarial Nonnegative Matrix Factorization
Lei Luo, Yanfu Zhang, Heng Huang
Adversarial Risk via Optimal Transport and Optimal Couplings
Muni Sreenivas Pydi, Varun Jog
Adversarial Robustness Against the Union of Multiple Perturbation Models
Pratyush Maini, Eric Wong, Zico Kolter
Adversarial Robustness for Code
Pavol Bielik, Martin Vechev
Adversarial Robustness via Runtime Masking and Cleansing
Yi-Hsuan Wu, Chia-Hung Yuan, Shan-Hung Wu
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation
Pan Xu, Quanquan Gu
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
Yunhua Xiang, Noah Simon
A Flexible Latent Space Model for Multilayer Networks
Xuefei Zhang, Songkai Xue, Ji Zhu
A Free-Energy Principle for Representation Learning
Yansong Gao, Pratik Chaudhari
A Game Theoretic Framework for Model Based Reinforcement Learning
Aravind Rajeswaran, Igor Mordatch, Vikash Kumar