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Found 16 papers, 1 papers with code
No-Regret Learning in Bilateral Trade via Global Budget Balance
Bilateral trade models the problem of intermediating between two rational agents -- a seller and a buyer -- both characterized by a private valuation for an item they want to trade.
The Role of Transparency in Repeated First-Price Auctions with Unknown Valuations
We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won.
Bandits with Replenishable Knapsacks: the Best of both Worlds
The bandits with knapsack (BwK) framework models online decision-making problems in which an agent makes a sequence of decisions subject to resource consumption constraints.
Fully Dynamic Submodular Maximization over Matroids
Maximizing monotone submodular functions under a matroid constraint is a classic algorithmic problem with multiple applications in data mining and machine learning.
Fairness in Streaming Submodular Maximization over a Matroid Constraint
Streaming submodular maximization is a natural model for the task of selecting a representative subset from a large-scale dataset.
Repeated Bilateral Trade Against a Smoothed Adversary
We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback models in the two cases where the learner can post either the same or different prices to buyers and sellers.
An $α$-regret analysis of Adversarial Bilateral Trade
We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i. e., determined by an adversary).
Learning on the Edge: Online Learning with Stochastic Feedback Graphs
The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback.
Deletion Robust Non-Monotone Submodular Maximization over Matroids
Maximizing a submodular function is a fundamental task in machine learning and in this paper we study the deletion robust version of the problem under the classic matroids constraint.
Deletion Robust Submodular Maximization over Matroids
Maximizing a monotone submodular function is a fundamental task in machine learning.
Allocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairness
For Round-Robin we show that all of its pure Nash equilibria induce allocations that are EF1 with respect to the underlying true values, while for the algorithm of Plaut and Roughgarden we show that the corresponding allocations not only are EFX but also satisfy maximin share fairness, something that is not true for this algorithm in the non-strategic setting!
Bilateral Trade: A Regret Minimization Perspective
In this paper, we cast the bilateral trade problem in a regret minimization framework over $T$ rounds of seller/buyer interactions, with no prior knowledge on their private valuations.
Beyond Bandit Feedback in Online Multiclass Classification
We study the problem of online multiclass classification in a setting where the learner's feedback is determined by an arbitrary directed graph.
A Regret Analysis of Bilateral Trade
Despite the simplicity of this problem, a classical result by Myerson and Satterthwaite (1983) affirms the impossibility of designing a mechanism which is simultaneously efficient, incentive compatible, individually rational, and budget balanced.
Submodular Maximization subject to a Knapsack Constraint: Combinatorial Algorithms with Near-optimal Adaptive Complexity
Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability.
Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint
Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing.
