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regret analysis

topic1 events
papersSEP 10 04:00 UTC

Exact-form regret analysis for gradient descent, mirror descent, and follow-the-regularized-leader

A newly posted arXiv paper investigates how online learning methods such as gradient descent, mirror descent, and follow-the-regularized-leader behave when measured against more demanding, action-dependent benchmarks rather than fixed comparison points. Moving past the standard external regret framing, the authors pursue a geometric account of these deviations and derive closed-form expressions for the resulting regret bounds.

research/optimization/theory/online-learning