Papers
8,340 papers found
FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models
Songze Li, Duanyi Yao, Jin Liu
FeDXL: Provable Federated Learning for Deep X-Risk Optimization
Zhishuai Guo, Rong Jin, Jiebo Luo et al.
Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection
Haoyue Bai, Gregory Canal, Xuefeng Du et al.
Few-bit Backward: Quantized Gradients of Activation Functions for Memory Footprint Reduction
Georgii Sergeevich Novikov, Daniel Bershatsky, Julia Gusak et al.
Few-Sample Feature Selection via Feature Manifold Learning
David Cohen, Tal Shnitzer, Yuval Kluger et al.
Fighting Fire with Fire: Contrastive Debiasing without Bias-free Data via Generative Bias-transformation
Yeonsung Jung, Hajin Shim, June Yong Yang et al.
Finding Generalization Measures by Contrasting Signal and Noise
Jiaye Teng, Bohang Zhang, Ruichen Li et al.
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Yizhen Zheng, He Zhang, Vincent Lee et al.
Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
Jingfeng Wu, Difan Zou, Zixiang Chen et al.
Fisher Information Embedding for Node and Graph Learning
Dexiong Chen, Paolo Pellizzoni, Karsten Borgwardt
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.