Papers
Accelerated, Optimal and Parallel: Some results on model-based stochastic optimization
Karan Chadha, Gary Cheng, John Duchi
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver et al.
Accelerating Shapley Explanation via Contributive Cooperator Selection
Guanchu Wang, Yu-Neng Chuang, Mengnan Du et al.
Accurate Quantization of Measures via Interacting Particle-based Optimization
Lantian Xu, Anna Korba, Dejan Slepcev
Achieving Fairness at No Utility Cost via Data Reweighing with Influence
Peizhao Li, Hongfu Liu
Achieving Minimax Rates in Pool-Based Batch Active Learning
Claudio Gentile, Zhilei Wang, Tong Zhang
A Closer Look at Smoothness in Domain Adversarial Training
Harsh Rangwani, Sumukh K Aithal, Mayank Mishra et al.
A Consistent and Efficient Evaluation Strategy for Attribution Methods
Yao Rong, Tobias Leemann, Vadim Borisov et al.
A Context-Integrated Transformer-Based Neural Network for Auction Design
Zhijian Duan, Jingwu Tang, Yutong Yin et al.
A Convergence Theory for SVGD in the Population Limit under Talagrand’s Inequality T1
Adil Salim, Lukang Sun, Peter Richtarik
A Convergent and Dimension-Independent Min-Max Optimization Algorithm
Vijay Keswani, Oren Mangoubi, Sushant Sachdeva et al.
Action-Sufficient State Representation Learning for Control with Structural Constraints
Biwei Huang, Chaochao Lu, Liu Leqi et al.
Active fairness auditing
Tom Yan, Chicheng Zhang
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