Papers
FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning
Dipam Goswami, Yuyang Liu, Bartłomiej Twardowski et al.
Fed-CO$_{2}$: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
Zhongyi Cai, Ye Shi, Wei Huang et al.
Federated Compositional Deep AUC Maximization
Xinwen Zhang, Yihan Zhang, Tianbao Yang et al.
Federated Conditional Stochastic Optimization
Xidong Wu, Jianhui Sun, Zhengmian Hu et al.
Federated Learning via Meta-Variational Dropout
Insu Jeon, Minui Hong, Junhyeog Yun et al.
Federated Learning with Bilateral Curation for Partially Class-Disjoint Data
Ziqing Fan, ruipeng zhang, Jiangchao Yao et al.
Federated Learning with Client Subsampling, Data Heterogeneity, and Unbounded Smoothness: A New Algorithm and Lower Bounds
Michael Crawshaw, Yajie Bao, Mingrui Liu
Federated Learning with Manifold Regularization and Normalized Update Reaggregation
Xuming An, Li Shen, Han Hu et al.
Federated Linear Bandits with Finite Adversarial Actions
Li Fan, Ruida Zhou, Chao Tian et al.
Federated Multi-Objective Learning
Haibo Yang, Zhuqing Liu, Jia Liu et al.
Federated Spectral Clustering via Secure Similarity Reconstruction
Dong Qiao, Chris Ding, Jicong Fan
Fed-FA: Theoretically Modeling Client Data Divergence for Federated Language Backdoor Defense
Zhiyuan Zhang, Deli Chen, Hao Zhou et al.
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning
Zhiqin Yang, Yonggang Zhang, Yu Zheng et al.
FedGame: A Game-Theoretic Defense against Backdoor Attacks in Federated Learning
Jinyuan Jia, Zhuowen Yuan, Dinuka Sahabandu et al.
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Yuhang Yao, Weizhao Jin, Srivatsan Ravi et al.
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
Zikai Xiao, Zihan Chen, Songshang Liu et al.
FedL2P: Federated Learning to Personalize
Royson Lee, Minyoung Kim, Da Li et al.
FedNAR: Federated Optimization with Normalized Annealing Regularization
Junbo Li, Ang Li, Chong Tian et al.
FELM: Benchmarking Factuality Evaluation of Large Language Models
shiqi chen, Yiran Zhao, Jinghan Zhang et al.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
Yuanxin Liu, Lei Li, Shuhuai Ren et al.
Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
Qi-Wei Wang, Da-Wei Zhou, Yi-Kai Zhang et al.
Few-shot Generation via Recalling Brain-Inspired Episodic-Semantic Memory
Zhibin Duan, Zhiyi Lv, Chaojie Wang et al.
FGPrompt: Fine-grained Goal Prompting for Image-goal Navigation
Xinyu Sun, Peihao Chen, Jugang Fan et al.
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations
Chanakya Ekbote, Ajinkya Deshpande, Arun Iyer et al.
FIND: A Function Description Benchmark for Evaluating Interpretability Methods
Sarah Schwettmann, Tamar Shaham, Joanna Materzynska et al.