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20202026
most citedMultimodal Federated Learning via Contrastive Representation Ensemble

36 citations · 49 across the 18 of their papers we have counts for

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5 papers · 1 filter

cs.LG2024

Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning

Xiangru Jian, Xinjian Zhao, Wei Pang +4

The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. Spectral augmentation, which involves modify…

cs.LG2023★ 1 cited

Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards

Bo Xue, Yimu Wang, Yuanyu Wan +2

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose -th moment is bounded for some . Although there exist methods…

cs.LG2023★ 1 cited

Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks

Yimu Wang, Xiangru Jian, Bo Xue

In this work, we present a post-processing solution to address the hubness problem in cross-modal retrieval, a phenomenon where a small number of gallery data points are frequently…

cs.LG2023★ 36 cited

Multimodal Federated Learning via Contrastive Representation Ensemble

Qiying Yu, Yang Liu, Yimu Wang +2

With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a criti…

cs.LG2020★ 3 cited

Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs

Bo Xue, Guanghui Wang, Yimu Wang +1

In this paper, we study the problem of stochastic linear bandits with finite action sets. Most of existing work assume the payoffs are bounded or sub-Gaussian, which may be violate…