arXiv Machine Learning August 12, 2026 By Arnab Maiti, Claire Jie Zhang, Kevin Jamieson, Jamie Heather Morgenstern, Ioannis Panageas, Lillian J. Ratliff

Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback

Read the original on arXiv Machine Learning →

arXiv:2602. 21436v2 Announce Type: replace-cross Abstract: In this paper, we study last-iterate convergence of learning algorithms in bilinear saddle-point problems, a preferable notion of convergence that captures the day-to-day behavior of learning dynamics.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

reinforcement-learning

arXiv Machine Learning

Aug 11

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agents reinforcement-learning safety

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reinforcement-learning

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arXiv Machine Learning

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reinforcement-learning safety

arXiv Machine Learning

Jul 16

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reinforcement-learning benchmarks

arXiv Machine Learning

Jul 9

Nonlinear Bandit

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By Tianshuo Zheng, Ting Wu, Zhi-Hua Zhou, Keqin Liu

reinforcement-learning

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