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Online Learning via Offline Greedy: Applications in Market Design and Optimization

https://doi.org/10.2139/ssrn.3613756
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27/27 checkable references clean · checked 2026-08-15

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.

30 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.

The 27 checked references that resolve
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Maximizing a class of submodular utility functions
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Optimal auctions vs. anonymous pricing
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Display Optimization for Vertically Differentiated Locations Under Multinomial Logit Preferences
resolves10.1093/qje/qjr028
Position Auctions with Consumer Search
resolves10.1287/moor.2013.0598
Regret in Online Combinatorial Optimization
resolves10.1137/S0097539701398375
The Nonstochastic Multiarmed Bandit Problem
resolves10.1007/s10107-018-1248-6
Submodular functions: from discrete to continuous domains
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An analog of the minimax theorem for vector payoffs
resolves10.1137/130929205
A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization
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Maximizing a Monotone Submodular Function Subject to a Matroid Constraint
resolves10.1109/TIT.2014.2365772
Regret Minimization for Reserve Prices in Second-Price Auctions
resolves10.1145/3328526.3329594
LP-based Approximation for Personalized Reserve Prices
resolves10.1145/3391403.3399483
Product Ranking on Online Platforms
resolves10.1145/1109557.1109675
An improved approximation algorithm for combinatorial auctions with submodular bidders
resolves10.1137/090779346
Maximizing Non-monotone Submodular Functions
resolves10.1109/FOCS.2011.46
A Unified Continuous Greedy Algorithm for Submodular Maximization
resolves10.1287/moor.2020.1053
Efficient Online Linear Optimization with Approximation Algorithms
resolves10.1287/opre.2020.1991
Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions
resolves10.1145/3465456.3467580
Learning Product Rankings Robust to Fake Users
resolves10.1145/1566374.1566407
Simple versus optimal mechanisms
resolves10.1137/19M1304271
Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization
resolves10.1145/2897518.2897536
The computational power of optimization in online learning
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Playing Games with Approximation Algorithms
resolves10.1016/j.jcss.2004.10.016
Efficient algorithms for online decision problems
resolves10.1145/956750.956769
Maximizing the spread of influence through a social network
resolves10.1007/s001820200115
Approachability in infinite dimensional spaces
resolves10.1007/BF02579435
An analysis of the greedy algorithm for the submodular set covering problem
The 30 references without a DOI — listed, not checked
no DOI — not checkedCompeting in the dark: An efficient algorithm for bandit linear optimization
no DOI — not checkedBlackwell approachability and no-regret learning are equivalent
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no DOI — not checkedGuaranteed nonconvex optimization: Submodular maximization over continuous domains
no DOI — not checkedTowards minimax policies for online linear optimization with bandit feedback
no DOI — not checkedref14
no DOI — not checkedDeterministic algorithms for submodular maximization problems
no DOI — not checkedNicolo Cesa-Bianchi and G�bor Lugosi. Combinatorial bandits
no DOI — not checkedProjection-free online optimization with stochastic gradient: From convexity to submodularity
no DOI — not checkedOnline continuous submodular maximization
no DOI — not checkedBlack box submodular maximization: Discrete and continuous settings
no DOI — not checkedCombinatorial multi-armed bandit: General framework and applications
no DOI — not checkedCombinatorial bandits revisited
no DOI — not checkedOracle-efficient online learning and auction design
no DOI — not checkedOnline learning for global cost functions
no DOI — not checkedLearning to rank an assortment of products
no DOI — not checkedOnline convex optimization in the bandit setting: gradient descent without a gradient
no DOI — not checkedGradient methods for submodular maximization
no DOI — not checkedVolumetric spanners: an efficient exploration basis for learning
no DOI — not checkedOnline improper learning with an approximation oracle
no DOI — not checkedAssortment planning: Review of literature and industry practice
no DOI — not checkedOnline learning and blackwell approachability with partial monitoring: optimal convergence rates
no DOI — not checkedSelecting sequences of items via submodular maximization
no DOI — not checkedAlgorithms for adversarial bandit problems with multiple plays
no DOI — not checkedOptimal approximation for the submodular welfare problem in the value oracle model
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no DOI — not checkedProbabilistic computations: Toward a unified measure of complexity
no DOI — not checkedOnline continuous submodular maximization: From full-information to bandit feedback
no DOI — not checkedOne sample stochastic frank-wolfe
no DOI — not checkedBeating stochastic and adversarial semi-bandits optimally and simultaneously
What this badge says. CiteStamped means the CHECKABLE references of this work were clean at the dated check: each resolved to a known work in a public registry, and none carried a retraction notice at that time. It says nothing about the quality, findings, or importance of the work itself, and nothing about references deposited without a DOI.

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