3 papers
stat.ME2026
Prior-Informed Generalizable Low-Rank Representation Learning via Regularized Adversarial Reweighting
Keyao Zhan, Junwei Lu, Tianxi Cai
Modern learning problems increasingly leverage data from heterogeneous sources to improve robustness and generalizability under distribution shift, often with high-dimensional para…
stat.ML2025
Analyzing the Role of Permutation Invariance in Linear Mode Connectivity
Keyao Zhan, Puheng Li, Lei Wu
It was empirically observed in Entezari et al. (2021) that when accounting for the permutation invariance of neural networks, there is likely no loss barrier along the linear inter…
stat.ME2024
Domain Adaptation Optimized for Robustness in Mixture Populations
Keyao Zhan, Xin Xiong, Zijian Guo +2
While domain adaptation methods address data shifts, most assume target populations align with at least one source population, neglecting mixtures that combine sources influenced b…