Papers
Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization
Jin Liu, Xiaokang Pan, Junwen Duan et al.
Fast & Fair: A Collaborative Platform for Fair Division Applications
Jiatong Han, Warut Suksompong
Fast Inter-frame Motion Prediction for Compressed Dynamic Point Cloud Attribute Enhancement
Wang Liu, Wei Gao, Xingming Mu
Fast Machine Unlearning without Retraining through Selective Synaptic Dampening
Jack Foster, Stefan Schoepf, Alexandra Brintrup
FAVOR: Full-Body AR-Driven Virtual Object Rearrangement Guided by Instruction Text
Kailin Li, Lixin Yang, Zenan Lin et al.
FD3D: Exploiting Foreground Depth Map for Feature-Supervised Monocular 3D Object Detection
Zizhang Wu, Yuanzhu Gan, Yunzhe Wu et al.
Feature Distribution Matching by Optimal Transport for Effective and Robust Coreset Selection
Weiwei Xiao, Yongyong Chen, Qiben Shan et al.
Feature Fusion from Head to Tail for Long-Tailed Visual Recognition
Mengke Li, Zhikai HU, Yang Lu et al.
Feature Transportation Improves Graph Neural Networks
Moshe Eliasof, Eldad Haber, Eran Treister
Feature Unlearning for Pre-trained GANs and VAEs
Saemi Moon, Seunghyuk Cho, Dongwoo Kim
FeatWalk: Enhancing Few-Shot Classification through Local View Leveraging
Dalong Chen, Jianjia Zhang, Wei-Shi Zheng et al.
FedA3I: Annotation Quality-Aware Aggregation for Federated Medical Image Segmentation against Heterogeneous Annotation Noise
Nannan Wu, Zhaobin Sun, Zengqiang Yan et al.
FedASMU: Efficient Asynchronous Federated Learning with Dynamic Staleness-Aware Model Update
Ji Liu, Juncheng Jia, Tianshi Che et al.
FedCD: Federated Semi-Supervised Learning with Class Awareness Balance via Dual Teachers
Yuzhi Liu, Huisi Wu, Jing Qin
FedCompetitors: Harmonious Collaboration in Federated Learning with Competing Participants
Shanli Tan, Hao Cheng, Xiaohu Wu et al.
FedCSL: A Scalable and Accurate Approach to Federated Causal Structure Learning
Xianjie Guo, Kui Yu, Lin Liu et al.
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Haokun Chen, Yao Zhang, Denis Krompass et al.
FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels
Jichang Li, Guanbin Li, Hui Cheng et al.
Federated Adaptive Prompt Tuning for Multi-Domain Collaborative Learning
Shangchao Su, Mingzhao Yang, Bin Li et al.
Federated Causality Learning with Explainable Adaptive Optimization
Dezhi Yang, Xintong He, Jun Wang et al.
Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users
Hantao Yang, Xutong Liu, Zhiyong Wang et al.
Federated Graph Learning under Domain Shift with Generalizable Prototypes
Guancheng Wan, Wenke Huang, Mang Ye
Federated Label-Noise Learning with Local Diversity Product Regularization
Xiaochen Zhou, Xudong Wang
Federated Learning via Input-Output Collaborative Distillation
Xuan Gong, Shanglin Li, Yuxiang Bao et al.
Federated Learning with Extremely Noisy Clients via Negative Distillation
Yang Lu, Lin Chen, Yonggang Zhang et al.