5 papers
Last-Iterate Convergence of Adaptive Riemannian Gradient Descent for Equilibrium Computation
Yang Cai, Michael I. Jordan, Tianyi Lin +2
Equilibrium computation on Riemannian manifolds provides a unifying framework for numerous problems in machine learning and data analytics. One of the simplest yet most fundamental…
Prediction-Augmented Trees for Reliable Statistical Inference
Vikram Kher, Argyris Oikonomou, Manolis Zampetakis
The remarkable success of machine learning (ML) in predictive tasks has led scientists to incorporate ML predictions as a core component of the scientific discovery pipeline. This…
COMAL: A Convergent Meta-Algorithm for Aligning LLMs with General Preferences
Yixin Liu, Argyris Oikonomou, Weiqiang Zheng +2
Many alignment methods, including reinforcement learning from human feedback (RLHF), rely on the Bradley-Terry reward assumption, which is not always sufficient to capture the full…
Provable Partially Observable Reinforcement Learning with Privileged Information
Yang Cai, Xiangyu Liu, Argyris Oikonomou +1
Partial observability of the underlying states generally presents significant challenges for reinforcement learning (RL). In practice, certain \emph{privileged information}, e.g.,…
Accelerated Algorithms for Constrained Nonconvex-Nonconcave Min-Max Optimization and Comonotone Inclusion
Yang Cai, Argyris Oikonomou, Weiqiang Zheng
We study constrained comonotone min-max optimization, a structured class of nonconvex-nonconcave min-max optimization problems, and their generalization to comonotone inclusion. In…