Tag
This paper proposes MS-WDRO, a multi-source Wasserstein distributionally robust graph learning framework that fuses heterogeneous sources via Wasserstein barycenter and minimizes worst-case risk for robust graph topology inference, outperforming baselines in graph recovery and diagnostic utility.
This paper proposes a unified imitation learning framework using Taylor Series Imitation Learning and distributionally robust adaptive control to address both policy-induced and uncertainty-induced distribution shifts, with a UAV case study demonstrating safety under uncertainty.
This paper proposes a distributionally robust listwise preference optimization method for LLM alignment that handles ranking-label uncertainty, with a tractable objective and strong convergence guarantees.
This paper presents an algorithm for group distributionally robust least squares regression using block Lewis weights, achieving improved complexity over interior point methods. It also provides interpolating algorithms between average and robust losses.