arXiv Machine Learning
Jul 15
A Geometric Approach to Constrained Online Learning
arXiv:2605. 21107v2 Announce Type: replace Abstract: We study constrained online convex optimization with adversarial time-varying constraints.
$\tilde{O}(\sqrt{T})$ Regret and Polylogarithmic Constraint Violation for COCO
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Aug 27
BAGEL: Adversarially Constrained Online Convex Optimization under Separation Oracle Access
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Constrained Online Learning with Noisy Constraint Values
arXiv:2609. 06921v1 Announce Type: cross Abstract: We study constrained online convex optimization with adversarial constraints when constraint values and gradients are observed through unbiased noise.
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Lower Bound on the Cumulative Constrained Violation for the OGD+Projection algorithm for Constrained Online Convex Optimization (COCO)
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.
arXiv Machine Learning
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Asymptotically Optimal Regret for Reinforcement Learning without Horizon Dependence
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arXiv Machine Learning
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Logarithmic High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
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arXiv Machine Learning
Aug 18
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 \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).
arXiv AI
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Nested Convex-Body Chasing for Online Optimization with Evolving Feasible Sets
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Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions
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arXiv Machine Learning
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Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints
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Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization
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