Papers
ActiveHedge: Hedge meets Active Learning
Bhuvesh Kumar, Jacob D Abernethy, Venkatesh Saligrama
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Guy Hacohen, Avihu Dekel, Daphna Weinshall
Active Multi-Task Representation Learning
Yifang Chen, Kevin Jamieson, Simon Du
Active Nearest Neighbor Regression Through Delaunay Refinement
Alexander Kravberg, Giovanni Luca Marchetti, Vladislav Polianskii et al.
Active Sampling for Min-Max Fairness
Jacob D Abernethy, Pranjal Awasthi, Matthäus Kleindessner et al.
Actor-Critic based Improper Reinforcement Learning
Mohammadi Zaki, Avi Mohan, Aditya Gopalan et al.
AdaGrad Avoids Saddle Points
Kimon Antonakopoulos, Panayotis Mertikopoulos, Georgios Piliouras et al.
Adapting k-means Algorithms for Outliers
Christoph Grunau, Václav Rozhoň
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Javier Antoran, David Janz, James U Allingham et al.
Adapting to Mixing Time in Stochastic Optimization with Markovian Data
Ron Dorfman, Kfir Yehuda Levy
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.