1 citations · 1 across the 4 of their papers we have counts for
5 papers
The Role of Gradient Modification in Heavy-Tailed Nonconvex Stochastic Min-Max Optimization
Tianxi Zhu, Yi Xu, Xiangyang Ji
Stochastic min-max optimization has attracted increasing attention due to its applications in modern machine learning, while existing theoretical studies mainly rely on the bounded…
ConformalSAM: Unlocking the Potential of Foundational Segmentation Models in Semi-Supervised Semantic Segmentation with Conformal Prediction
Danhui Chen, Ziquan Liu, Chuxi Yang +4
Pixel-level vision tasks, such as semantic segmentation, require extensive and high-quality annotated data, which is costly to obtain. Semi-supervised semantic segmentation (SSSS)…
Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
Tianxi Zhu, Yi Xu, Xiangyang Ji
An increasing number of studies have focused on stochastic first-order methods (SFOMs) under heavy-tailed gradient noises, which have been observed in the training of practical dee…
Robust Fast Adaptation from Adversarially Explicit Task Distribution Generation
Cheems Wang, Yiqin Lv, Yixiu Mao +3
Meta-learning is a practical learning paradigm to transfer skills across tasks from a few examples. Nevertheless, the existence of task distribution shifts tends to weaken meta-lea…
Supported Trust Region Optimization for Offline Reinforcement Learning
Yixiu Mao, Hongchang Zhang, Chen Chen +2
Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error. Most policy constraint methods regularize the density of the trained policy towar…