Papers
Adaptive Accelerated (Extra-)Gradient Methods with Variance Reduction
Zijian Liu, Ta Duy Nguyen, Alina Ene et al.
Adaptive Best-of-Both-Worlds Algorithm for Heavy-Tailed Multi-Armed Bandits
Jiatai Huang, Yan Dai, Longbo Huang
Adaptive Conformal Predictions for Time Series
Margaux Zaffran, Olivier Feron, Yannig Goude et al.
Adaptive Data Analysis with Correlated Observations
Aryeh Kontorovich, Menachem Sadigurschi, Uri Stemmer
Adaptive Gaussian Process Change Point Detection
Edoardo Caldarelli, Philippe Wenk, Stefan Bauer et al.
Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum
Zeke Xie, Xinrui Wang, Huishuai Zhang et al.
Adaptive Model Design for Markov Decision Process
Siyu Chen, Donglin Yang, Jiayang Li et al.
Adaptive Random Walk Gradient Descent for Decentralized Optimization
Tao Sun, Dongsheng Li, Bao Wang
Adaptive Second Order Coresets for Data-efficient Machine Learning
Omead Pooladzandi, David Davini, Baharan Mirzasoleiman
A data-driven approach for learning to control computers
Peter C Humphreys, David Raposo, Tobias Pohlen et al.
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Wenzheng Hou, Qianqian Xu, Zhiyong Yang et al.
Additive Gaussian Processes Revisited
Xiaoyu Lu, Alexis Boukouvalas, James Hensman
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning
Adam R Villaflor, Zhe Huang, Swapnil Pande et al.
A deep convolutional neural network that is invariant to time rescaling
Brandon G Jacques, Zoran Tiganj, Aakash Sarkar et al.
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications
Lukas Wolf, Ard Kastrati, Martyna B Plomecka et al.
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