2 citations · 2 across the 4 of their papers we have counts for
7 papers
Softmax Routing Dynamics in Mixture-of-Experts Models: Collapse and Recovery
Shengze Xu, Maolin Deng, Tieyong Zeng
Softmax routing in mixture-of-experts models can concentrate probability on a subset of experts, suppressing access to the others. We study how this concentration forms, why it per…
Composite and Stochastic Smoothed Decentralized Bregman Proximal-Gradient Methods
Shengze Xu, Chenhan Jin, Qingsong Wang +1
Smoothed decentralized Bregman proximal-gradient (SDBPG) methods combine relative-smoothness geometry with edge-dual consensus. We extend SDBPG to node-specific convex nonsmooth re…
More Programs or More Rolls? Separating Coverage from Specialization in LLM Harnesses
Ziyang Xu, Haitian Zhong, Hao Zhou +5
Automated generation of LLM harnesses promises to improve inference through task specialization. Yet additional answer coverage can arise from repeated execution of the same progra…
Adaptive Bregman Proximal Stochastic Gradient with a Stabilized Barzilai--Borwein Step Size
Chenhan Jin, Shengze Xu, Binghui Xie +4
Bregman proximal stochastic gradient (BPSG) methods bring variance-reduced composite optimization to objectives whose geometry is poorly captured by Euclidean smoothness. Their per…
Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models
Junkai Lin, Junkai Chen, Siqi Hou +6
Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowledge while preserving model utilit…
EndoVLA: Dual-Phase Vision-Language-Action Model for Autonomous Tracking in Endoscopy
Chi Kit Ng, Long Bai, Guankun Wang +6
In endoscopic procedures, autonomous tracking of abnormal regions and following circumferential cutting markers can significantly reduce the cognitive burden on endoscopists. Howev…
Efficient Private SCO for Heavy-Tailed Data via Averaged Clipping
Chenhan Jin, Kaiwen Zhou, Bo Han +2
We consider stochastic convex optimization for heavy-tailed data with the guarantee of being differentially private (DP). Most prior works on differentially private stochastic conv…