Cover Image

p-Mean Regret for Stochastic Bandits

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Krishna, Anand, John, Philips George, Barik, Adarsh, Tan, Vincent Y. F.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Computer Science and Game Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Regret Analysis of Sleeping Competing Bandits
by: Uba, Shinnosuke, et al.
Published: (2026)

Honor Among Bandits: No-Regret Learning for Online Fair Division
by: Procaccia, Ariel D., et al.
Published: (2024)

Improved Regret Bounds for Online Fair Division with Bandit Learning
by: Schiffer, Benjamin, et al.
Published: (2025)

Learning not to Regret
by: Sychrovský, David, et al.
Published: (2023)

Doubly Optimal No-Regret Online Learning in Strongly Monotone Games with Bandit Feedback
by: Ba, Wenjia, et al.
Published: (2021)

Optimism Without Regularization: Constant Regret in Zero-Sum Games
by: Lazarsfeld, John, et al.
Published: (2025)

Neural Mean-Field Games: Extending Mean-Field Game Theory with Neural Stochastic Differential Equations
by: Thöni, Anna C. M., et al.
Published: (2025)

GPU-Accelerated Counterfactual Regret Minimization
by: Kim, Juho
Published: (2024)

Barriers to Welfare Maximization with No-Regret Learning
by: Anagnostides, Ioannis, et al.
Published: (2024)

Regret Analysis of Repeated Delegated Choice
by: Hajiaghayi, MohammadTaghi, et al.
Published: (2023)

Full Swap Regret and Discretized Calibration
by: Fishelson, Maxwell, et al.
Published: (2025)

Regret Minimization in Stackelberg Games with Side Information
by: Harris, Keegan, et al.
Published: (2024)

Forecasting for Swap Regret for All Downstream Agents
by: Roth, Aaron, et al.
Published: (2024)

Meta-Learning in Self-Play Regret Minimization
by: Sychrovský, David, et al.
Published: (2025)

Regret Minimization in Bilateral Trade With Perturbed Markets
by: Lunghi, Anna, et al.
Published: (2026)

Selling Joint Ads: A Regret Minimization Perspective
by: Aggarwal, Gagan, et al.
Published: (2024)

The Relationship between No-Regret Learning and Online Conformal Prediction
by: Ramalingam, Ramya, et al.
Published: (2025)

Strategic Linear Contextual Bandits
by: Buening, Thomas Kleine, et al.
Published: (2024)

Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback
by: Pokhriyal, Subham, et al.
Published: (2026)

No-Regret Algorithms in non-Truthful Auctions with Budget and ROI Constraints
by: Aggarwal, Gagan, et al.
Published: (2024)

ReLExS: Reinforcement Learning Explanations for Stackelberg No-Regret Learners
by: Huang, Xiangge, et al.
Published: (2024)

Computational Lower Bounds for Regret Minimization in Normal-Form Games
by: Anagnostides, Ioannis, et al.
Published: (2024)

Tight Regret Bounds for Bilateral Trade under Semi Feedback
by: Jin, Yaonan
Published: (2026)

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
by: Bechavod, Yahav, et al.
Published: (2025)

Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games
by: Cai, Yang, et al.
Published: (2023)

Regret Minimization for Piecewise Linear Rewards: Contracts, Auctions, and Beyond
by: Bacchiocchi, Francesco, et al.
Published: (2025)

No-Regret Learning in Bilateral Trade via Global Budget Balance
by: Bernasconi, Martino, et al.
Published: (2023)

Nearly Tight Regret Bounds for Profit Maximization in Bilateral Trade
by: Di Gregorio, Simone, et al.
Published: (2025)

Last-Iterate Convergence of No-Regret Learning for Equilibria in Bargaining Games
by: Kamp, Serafina, et al.
Published: (2025)

Swap Regret and Correlated Equilibria Beyond Normal-Form Games
by: Arunachaleswaran, Eshwar Ram, et al.
Published: (2025)

Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
by: Lucier, Brendan, et al.
Published: (2023)

Convergence of Regret Matching in Potential Games and Constrained Optimization
by: Anagnostides, Ioannis, et al.
Published: (2025)

Incentivized Truthful Communication for Federated Bandits
by: Wei, Zhepei, et al.
Published: (2024)

No Coin Left Behind: Maximizing Strategic Surplus Against No-Regret Dynamics
by: Su, Yiheng, et al.
Published: (2026)

A Tight Regret Analysis of Non-Parametric Repeated Contextual Brokerage
by: Bachoc, François, et al.
Published: (2025)

Sample Efficient Omniprediction and Downstream Swap Regret for Non-Linear Losses
by: Lu, Jiuyao, et al.
Published: (2025)

Better Regret Rates in Bilateral Trade via Sublinear Budget Violation
by: Lunghi, Anna, et al.
Published: (2025)

Incentive-compatible Bandits: Importance Weighting No More
by: Zimmert, Julian, et al.
Published: (2024)

Incentivized Learning in Principal-Agent Bandit Games
by: Scheid, Antoine, et al.
Published: (2024)

Adaptive Bandit Algorithms for Contextual Matching Markets
by: Lin, Shiyun, et al.
Published: (2026)