Adversarial bandits with knapsacks
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Publication:6551256
DOI10.1145/3557045MaRDI QIDQ6551256
Nicole Immorlica, Karthik Abinav Sankararaman, Aleksandrs Slivkins, Robert E. Schapire
Publication date: 6 June 2024
Published in: Journal of the ACM (Search for Journal in Brave)
competitive ratioregretmulti-armed banditsprimal-dual algorithmsadversarial banditsbandits with knapsacks
Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05) Applications of game theory (91A80) Online algorithms; streaming algorithms (68W27)
Cites Work
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- Approximation algorithms for combinatorial problems
- On the ratio of optimal integral and fractional covers
- The weighted majority algorithm
- A decision-theoretic generalization of on-line learning and an application to boosting
- Adaptive game playing using multiplicative weights
- Near-optimal no-regret algorithms for zero-sum games
- Lectures on Modern Convex Optimization
- Geometry of Online Packing Linear Programs
- Bayesian Mechanism Design
- Close the Gaps: A Learning-While-Doing Algorithm for Single-Product Revenue Management Problems
- A Dynamic Near-Optimal Algorithm for Online Linear Programming
- The Design of Approximation Algorithms
- Multi‐Armed Bandit Allocation Indices
- Dynamic Pricing Without Knowing the Demand Function: Risk Bounds and Near-Optimal Algorithms
- Online Primal-Dual Algorithms for Covering and Packing
- AdWords and generalized online matching
- The online set cover problem
- Online Stochastic Packing Applied to Display Ad Allocation
- The Design of Competitive Online Algorithms via a Primal—Dual Approach
- Competitive paging algorithms
- Bandits with Knapsacks
- Jointly Private Convex Programming
- An Online Convex Optimization Approach to Proactive Network Resource Allocation
- Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems
- The Nonstochastic Multiarmed Bandit Problem
- Blind Network Revenue Management
- Kernel-based methods for bandit convex optimization
- Bandits with Global Convex Constraints and Objective
- Regret in Online Combinatorial Optimization
- Introduction to Multi-Armed Bandits
- Stochastic bandits robust to adversarial corruptions
- Multi-armed Bandits with Metric Switching Costs
- Watch and learn: optimizing from revealed preferences feedback
- Online Network Design Algorithms via Hierarchical Decompositions
- Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems
- Electrical flows, laplacian systems, and faster approximation of maximum flow in undirected graphs
- Prediction, Learning, and Games
- A Polylogarithmic-Competitive Algorithm for the k-Server Problem
- Approximation Algorithms for Correlated Knapsacks and Non-martingale Bandits
- A Simple Parallel Algorithm with an $O(1/t)$ Convergence Rate for General Convex Programs
- Expander flows, geometric embeddings and graph partitioning
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