arXiv Statistics ML
Strongly Adaptive Online Learning with Time-Varying Movement Costs
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arXiv Machine Learning
Aug 10
Parameter-free Dynamic Regret: Time-varying Movement Costs, Delayed Feedback, and Memory
arXiv:2602. 06902v3 Announce Type: replace Abstract: In this paper, we study dynamic regret in unconstrained online convex optimization (OCO) with movement costs.
arXiv Machine Learning
Sep 21
From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences
The paper introduces a straightforward framework that transforms dynamic regret minimization into switching regret minimization by constructing an unbiased random sequence for any comparator sequence. Using this reduction, the authors derive dynamic regret bounds for strongly convex and exp-concave losses of “~O(T^{1/3}P_T^{2/3})” and for general convex losses of “O(√{T(1+P_T)})”, matching known minimax optimal results. The approach leverages off-the-shelf switching regret algorithms and controlled variance to achieve these bounds.
arXiv Machine Learning
Sep 29
Dynamic Regret in Online Convex Optimization with Indicator Switching Costs
arXiv:2609. 30556v1 Announce Type: new Abstract: We study dynamic regret in online convex optimization with an \emph{indicator switching cost}: a fixed penalty incurred whenever two consecutive decisions differ.
arXiv Machine Learning
Jun 11
Capacity-Constrained Online Convex Optimization with Delayed Feedback
arXiv:2606. 11711v1 Announce Type: new Abstract: Online learning with delayed feedback typically assumes that the learner can track all pending rounds until their feedback arrives.
arXiv Machine Learning
Sep 11
Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions
arXiv:2609. 11207v1 Announce Type: new Abstract: Convex Optimization with Nested Evolving Feasible Sets (CONES)} was introduced in \cite{CONESVaze} 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 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 \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).