Papers
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds
Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins et al.
Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data
Dachao Lin, Ruoyu Sun, Zhihua Zhang
Faster Matchings via Learned Duals
Michael Dinitz, Sungjin Im, Thomas Lavastida et al.
Faster Neural Network Training with Approximate Tensor Operations
Menachem Adelman, Kfir Levy, Ido Hakimi et al.
Faster Non-asymptotic Convergence for Double Q-learning
Lin Zhao, Huaqing Xiong, Yingbin Liang
Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods
Hamza Fawzi, Harry Goulbourne
Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems
Sucheol Lee, Donghwan Kim
Fast Federated Learning in the Presence of Arbitrary Device Unavailability
Xinran Gu, Kaixuan Huang, Jingzhao Zhang et al.
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Maura Pintor, Fabio Roli, Wieland Brendel et al.
Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification
Jiong Zhang, Wei-Cheng Chang, Hsiang-Fu Yu et al.
Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization
Shicong Cen, Yuting Wei, Yuejie Chi
Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics
Man Shun Ang, Jianzhu Ma, Nianjun Liu et al.
Fast Pure Exploration via Frank-Wolfe
Po-An Wang, Ruo-Chun Tzeng, Alexandre Proutiere
Fast rates for prediction with limited expert advice
El Mehdi Saad, Gilles Blanchard
Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights
Dong Quan Vu, Kimon Antonakopoulos, Panayotis Mertikopoulos
Fast Training Method for Stochastic Compositional Optimization Problems
Hongchang Gao, Heng Huang
Fast Training of Neural Lumigraph Representations using Meta Learning
Alexander Bergman, Petr Kellnhofer, Gordon Wetzstein
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
Kazu Ghalamkari, Mahito Sugiyama
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
Xiaofeng Fan, Yining Ma, Zhongxiang Dai et al.
FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization
Quoc Tran Dinh, Nhan H Pham, Dzung Phan et al.
Federated-EM with heterogeneity mitigation and variance reduction
Aymeric Dieuleveut, Gersende Fort, Eric Moulines et al.
Federated Graph Classification over Non-IID Graphs
Han Xie, Jing Ma, Li Xiong et al.
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Mikhail Khodak, Renbo Tu, Tian Li et al.
Federated Linear Contextual Bandits
Ruiquan Huang, Weiqiang Wu, Jing Yang et al.