Papers
Faster Privacy Accounting via Evolving Discretization
Badih Ghazi, Pritish Kamath, Ravi Kumar et al.
Fast Finite Width Neural Tangent Kernel
Roman Novak, Jascha Sohl-Dickstein, Samuel S Schoenholz
Fast Lossless Neural Compression with Integer-Only Discrete Flows
Siyu Wang, Jianfei Chen, Chongxuan Li et al.
Fast Population-Based Reinforcement Learning on a Single Machine
Arthur Flajolet, Claire Bizon Monroc, Karim Beguir et al.
Fast Provably Robust Decision Trees and Boosting
Jun-Qi Guo, Ming-Zhuo Teng, Wei Gao et al.
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning
Jiechao Guan, Zhiwu Lu
Fast rates for noisy interpolation require rethinking the effect of inductive bias
Konstantin Donhauser, Nicolò Ruggeri, Stefan Stojanovic et al.
Fast Relative Entropy Coding with A* coding
Gergely Flamich, Stratis Markou, Jose Miguel Hernandez-Lobato
Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows
Feynman Liang, Michael Mahoney, Liam Hodgkinson
Feature and Parameter Selection in Stochastic Linear Bandits
Ahmadreza Moradipari, Berkay Turan, Yasin Abbasi-Yadkori et al.
Feature Learning and Signal Propagation in Deep Neural Networks
Yizhang Lou, Chris E Mingard, Soufiane Hayou
Feature selection using e-values
Subhabrata Majumdar, Snigdhansu Chatterjee
Feature Space Particle Inference for Neural Network Ensembles
Shingo Yashima, Teppei Suzuki, Kohta Ishikawa et al.
Federated Learning with Label Distribution Skew via Logits Calibration
Jie Zhang, Zhiqi Li, Bo Li et al.
Federated Learning with Partial Model Personalization
Krishna Pillutla, Kshitiz Malik, Abdel-Rahman Mohamed et al.
Federated Learning with Positive and Unlabeled Data
Xinyang Lin, Hanting Chen, Yixing Xu et al.
Federated Minimax Optimization: Improved Convergence Analyses and Algorithms
Pranay Sharma, Rohan Panda, Gauri Joshi et al.
Federated Reinforcement Learning: Linear Speedup Under Markovian Sampling
Sajad Khodadadian, Pranay Sharma, Gauri Joshi et al.
FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen et al.
FedNest: Federated Bilevel, Minimax, and Compositional Optimization
Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis et al.
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi et al.
FedNL: Making Newton-Type Methods Applicable to Federated Learning
Mher Safaryan, Rustem Islamov, Xun Qian et al.
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
Fan Lai, Yinwei Dai, Sanjay Singapuram et al.
Fenrir: Physics-Enhanced Regression for Initial Value Problems
Filip Tronarp, Nathanael Bosch, Philipp Hennig
Fictitious Play and Best-Response Dynamics in Identical Interest and Zero-Sum Stochastic Games
Lucas Baudin, Rida Laraki