Papers
Flash: Concept Drift Adaptation in Federated Learning
Kunjal Panchal, Sunav Choudhary, Subrata Mitra et al.
FLEX: an Adaptive Exploration Algorithm for Nonlinear Systems
Matthieu Blanke, Marc Lelarge
FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU
Ying Sheng, Lianmin Zheng, Binhang Yuan et al.
Flexible Phase Dynamics for Bio-Plausible Contrastive Learning
Ezekiel Williams, Colin Bredenberg, Guillaume Lajoie
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization
Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon et al.
Flipping Coins to Estimate Pseudocounts for Exploration in Reinforcement Learning
Sam Lobel, Akhil Bagaria, George Konidaris
Forget Unlearning: Towards True Data-Deletion in Machine Learning
Rishav Chourasia, Neil Shah
Formalizing Preferences Over Runtime Distributions
Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal
Yingdong Hu, Renhao Wang, Li Erran Li et al.
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space
Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi et al.
Fourmer: An Efficient Global Modeling Paradigm for Image Restoration
Man Zhou, Jie Huang, Chun-Le Guo et al.
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Chieh-Hsin Lai, Yuhta Takida, Naoki Murata et al.
Fractional Denoising for 3D Molecular Pre-training
Shikun Feng, Yuyan Ni, Yanyan Lan et al.
FREDIS: A Fusion Framework of Refinement and Disambiguation for Unreliable Partial Label Learning
Congyu Qiao, Ning Xu, Jiaqi Lv et al.
Free-Form Variational Inference for Gaussian Process State-Space Models
Xuhui Fan, Edwin V. Bonilla, Terence O’Kane et al.
From Adaptive Query Release to Machine Unlearning
Enayat Ullah, Raman Arora
From Hypergraph Energy Functions to Hypergraph Neural Networks
Yuxin Wang, Quan Gan, Xipeng Qiu et al.
From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning
Edwige Cyffers, Aurélien Bellet, Debabrota Basu
From Perception to Programs: Regularize, Overparameterize, and Amortize
Hao Tang, Kevin Ellis
From Relational Pooling to Subgraph GNNs: A Universal Framework for More Expressive Graph Neural Networks
Cai Zhou, Xiyuan Wang, Muhan Zhang
From Robustness to Privacy and Back
Hilal Asi, Jonathan Ullman, Lydia Zakynthinou
From Temporal to Contemporaneous Iterative Causal Discovery in the Presence of Latent Confounders
Raanan Yehezkel Rohekar, Shami Nisimov, Yaniv Gurwicz et al.
Fully-Adaptive Composition in Differential Privacy
Justin Whitehouse, Aaditya Ramdas, Ryan Rogers et al.
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
Ba-Hien Tran, Babak Shahbaba, Stephan Mandt et al.
Fully Dynamic Submodular Maximization over Matroids
Paul Duetting, Federico Fusco, Silvio Lattanzi et al.