3 papers
math.OC2026
Stochastic Auto-conditioned Fast Gradient Methods with Optimal Rates
Yao Ji, Guanghui Lan
Achieving optimal rates for stochastic composite convex optimization without prior knowledge of problem parameters remains a central challenge. In the deterministic setting, the au…
math.OC2025
High-order Accumulative Regularization for Gradient Minimization in Convex Programming
Yao Ji, Guanghui Lan
This paper develops a unified high-order accumulative regularization (AR) framework for convex and uniformly convex gradient norm minimization. Existing high-order methods often ex…
cs.LG2025
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual Matching
Ruqi Bai, Yao Ji, Zeyu Zhou +1
Models that learn spurious correlations from training data often fail when deployed in new environments. While many methods aim to learn invariant representations to address this,…