arXiv Machine Learning July 14, 2026 By Haricharan Balasundaram, Karthick Krishna Mahendran, Rahul Vaze

Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO)

Read the original on arXiv Machine Learning →

arXiv:2607. 10808v1 Announce Type: new Abstract: The problem of constrained online convex optimization is considered, where at each round, once a learner commits to an action $x_t \in \mathcal{X} \subset \mathbb{R}^d$, a convex loss function $f_t$ and a convex constraint function $g_t$ that drives the constraint $g_t(x)\le 0$ are revealed.

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

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Breaking the $T^{3/4}$ Barrier for Regret Minimization With Bi-Dimensional CDFs

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Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence

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1d ago

Convex Optimization with Nested Evolving Feasible Sets

arXiv:2605. 07386v2 Announce Type: replace Abstract: \emph{Convex Optimization with Nested Evolving Feasible Sets (CONES)} is considered where the objective function $f$ remains fixed but the feasible region evolves over time as a nested sequence $S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T$.

By Karthick Krishna M., Haricharan Balasundaram, Rahul Vaze

benchmarks

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