Papers
Fair Division of a Graph into Compact Bundles
Jayakrishnan Madathil
Fair Division with Two-Sided Preferences
Ayumi Igarashi, Yasushi Kawase, Warut Suksompong et al.
Fairly Allocating Goods and (Terrible) Chores
Hadi Hosseini, Aghaheybat Mammadov, Tomasz Wąs
Fairness and Representation in Satellite-Based Poverty Maps: Evidence of Urban-Rural Disparities and Their Impacts on Downstream Policy
Emily Aiken, Esther Rolf, Joshua Blumenstock
Fairness and Stability in Complex Domains
Julian Chingoma
Fairness via Group Contribution Matching
Tianlin Li, Zhiming Li, Anran Li et al.
Fast Algorithms for SAT with Bounded Occurrences of Variables
Junqiang Peng, Mingyu Xiao
Fast and Differentially Private Fair Clustering
Junyoung Byun, Jaewook Lee
FastGR: Global Routing on CPU-GPU with Heterogeneous Task Graph Scheduler (Extended Abstract)
Siting Liu, Yuan Pu, Peiyu Liao et al.
Fast-StrucTexT: An Efficient Hourglass Transformer with Modality-guided Dynamic Token Merge for Document Understanding
Mingliang Zhai, Yulin Li, Xiameng Qin et al.
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts
Shubham Sharma, Jette Henderson, Joydeep Ghosh
Feature Staleness Aware Incremental Learning for CTR Prediction
Zhikai Wang, Yanyan Shen, Zibin Zhang et al.
FedBFPT: An Efficient Federated Learning Framework for Bert Further Pre-training
Xin'ao Wang, Huan Li, Ke Chen et al.
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
Jiahao Liu, Jiang Wu, Jinyu Chen et al.
Federated Graph Semantic and Structural Learning
Wenke Huang, Guancheng Wan, Mang Ye et al.
Federated Probabilistic Preference Distribution Modelling with Compactness Co-Clustering for Privacy-Preserving Multi-Domain Recommendation
Weiming Liu, Chaochao Chen, Xinting Liao et al.
FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer
Chenghao Liu, Xiaoyang Qu, Jianzong Wang et al.
FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks
Xinyu Fu, Irwin King
FedNoRo: Towards Noise-Robust Federated Learning by Addressing Class Imbalance and Label Noise Heterogeneity
Nannan Wu, Li Yu, Xuefeng Jiang et al.
FedOBD: Opportunistic Block Dropout for Efficiently Training Large-scale Neural Networks through Federated Learning
Yuanyuan Chen, Zichen Chen, Pengcheng Wu et al.
FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Hanlin Gu, Jiahuan Luo, Yan Kang et al.
FedSampling: A Better Sampling Strategy for Federated Learning
Tao Qi, Fangzhao Wu, Lingjuan Lyu et al.
Fedstellar: A Platform for Training Models in a Privacy-preserving and Decentralized Fashion
Enrique Tomás Martínez Beltrán, Pedro Miguel Sánchez Sánchez, Sergio López Bernal et al.
Few-shot Classification via Ensemble Learning with Multi-Order Statistics
Sai Yang, Fan Liu, Delong Chen et al.