3 citations · 7 across the 5 of their papers we have counts for
6 papers
Communication-Efficient Agnostic Federated Learning via Faster Convergence and Compression
Haomin Bai, Junyan Sun, Sifan Yang +2
Agnostic federated learning (AFL) seeks a model that performs reliably across heterogeneous workers, but communication remains a bottleneck. We improve communication efficiency…
DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models
Yimu Wang, Shuai Yuan, Bo Xue +4
Recent progress in video-text retrieval has been driven largely by advancements in model architectures and training strategies. However, the representation learning capabilities of…
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…
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…
A Dataset and Benchmarks for Multimedia Social Analysis
Bofan Xue, David Chan, John Canny
We present a new publicly available dataset with the goal of advancing multi-modality learning by offering vision and language data within the same context. This is achieved by obt…
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…