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Non-stationary Stochastic Optimization under L_{p,q}-Variation Measures

https://doi.org/10.2139/ssrn.3014773
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21/21 checkable references clean · checked 2026-08-29

Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.

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The 21 checked references that resolve
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Stochastic Convex Optimization with Bandit Feedback
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Convex Optimization
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Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
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Kernel-based methods for bandit convex optimization
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Minimax Bounds for Active Learning
resolves10.1002/047174882x
Elements of Information Theory
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Dynamic pricing and learning: Historical origins, current research, and new directions
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Dynamic Pricing and Learning with Finite Inventories
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Online Convex Optimization in Dynamic Environments
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Introduction to Online Convex Optimization
resolves10.1007/s10994-007-5016-8
Logarithmic regret algorithms for online convex optimization
resolves10.1007/978-1-4899-0027-2_4
Local Asymptotic Normality of Families of Distributions
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Chasing Demand: Learning and Earning in a Changing Environment
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Online optimization in dynamic environments: Improved regret rates for strongly convex problems
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Interior-Point Polynomial Algorithms in Convex Programming
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Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
resolves10.1007/b13794
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The 17 references without a DOI — listed, not checked
no DOI — not checkedCompeting in the dark: An efficient algorithm for bandit linear optimization
no DOI — not checkedOptimal algorithms for online convex optimization with multi-point bandit feedback
no DOI — not checkedref11
no DOI — not checkedOnline convex optimization in the bandit setting: gradient descent without a gradient
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no DOI — not checkedBeyond the regret minimization barrier: Optimal algorithms for stochastic stronglyconvex optimization
no DOI — not checkedBandit convex optimization: Towards tight bounds
no DOI — not checkedOnline optimization: Competing with dynamic comparators
no DOI — not checkedQuery complexity of derivative-free optimization
no DOI — not checkedMulti-armed bandits: Competing with optimal sequences
no DOI — not checkedA survey of algorithms and analysis for adaptive online learning
no DOI — not checkedImproved regret guarantees for online smooth convex optimization with bandit feedback
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no DOI — not checkedNoise-adaptive margin-based active learning and lower bounds under tsybakov noise condition
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no DOI — not checkedOnline convex programming and generalized infinitesimal gradient ascent
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