Regret Minimization

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12 papers in the last four weeks, up 71% on the four weeks before. 0.1% of all new papers.

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Latest papers 81

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Expected Sample Complexity in Multi-Armed Bandits
Oct 7, 2026Nadav Sukenik, Nadav MerlisMulti-Armed Bandits Regret Minimization
Technion – Institute of Technology
The Curvature of Regret in Contextual Linear Optimization
Oct 1, 2026Konstantinos Ziliaskopoulos, Alexander Vinel, Alice E. SmithDecision-Focused Learning Regret Minimization
Auburn University Auburn, AL, USA · University of Alabama Tuscaloosa, AL, USA
Minimax Optimal Regret for Causal Logistic Bandits with Counterfactual Fairness
Oct 1, 2026Junhyuk Huh, Seoungbin Bae, Dabeen LeeMulti-Armed Bandits Minimax Regret
Faculty of Mathematics, University of Cambridge · Department of Industrial & Systems Engineering, KAIST · Department of Mathematical Sciences, Seoul National University
Geometry-Dependent Bounds for Online Non-Monotone DR-Submodular Maximization
Sep 30, 2026Vaneet AggarwalSubmodular Optimization Regret Minimization
Purdue University
Bandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
Sep 29, 2026Kaixuan Ji, Qiwei Di, Qingyue Zhao +2Multi-Armed Bandits Minimax Regret
Department of Computer Science, University of California, Los Angeles, CA 90095, USA
Cost-Sensitive Online Window Size Selection for Portfolio Management
Sep 24, 2026Yi-Chen Liu, Chung-Han HsiehCost-Sensitive Learning Regret Minimization
Interdisciplinary Program of Management and Technology, National Tsing Hua University · Department of Quantitative Finance, National Tsing Hua University, Taiwan.
Prediction with Expert Advice: Anytime Regret with Many Experts Matches the Fixed-Time Constant
Sep 23, 2026Yang Cai, Vineet Gupta, Yanchen Jiang +4Minimax Regret Regret Minimization
Google Research · Yale University · Google DeepMind
From Switching to Dynamic Regret: A Simple Reduction via Unbiased Random Sequences
Sep 17, 2026Yibo Wang, Wenhao Yang, Sifan Yang +3Online Convex Optimization Dynamic Regret Minimization
State Key Laboratory for Novel Software Technology, Nanjing University · School of Artificial Intelligence, Nanjing University · School of Software Technology, Zhejiang University
Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices
Sep 15, 2026Tung MaiGame Theory Decentralized Learning
Adobe Research
GPU-CFR: 80x Faster Counterfactual Regret Minimization by Compiling the Game to Static Dataflow and CUDA Graph Replay
Sep 12, 2026Boning Li, Longbo HuangGPU Kernel Optimization GPU Acceleration
Institute for Interdisciplinary Information Sciences, Tsinghua University
Convex Optimization with Nested Evolving Feasible Sets (CONES) under Time-Varying Loss Functions
Sep 11, 2026Rahul VazeOnline Convex Optimization Regret Minimization
Online Inverse Integer Linear Optimization via Small-Gradient Skipping: Constant Regret and Finite Mistakes
Sep 9, 2026Akira KitaokaRegret Minimization
No-Regret Mixing of LRU and LFU with Optimal Switching Cost
Sep 7, 2026Younes Ben Mazziane, Xinying ZouRegret Minimization Online Learning
Linköping University
Constant regret in general games via higher-order optimism
Sep 3, 2026Omar Abbadi, Rida Laraki, Panayotis MertikopoulosFollow-the-Regularized-Leader Regret Minimization
Univ. Mohammed VI Polytechnic, CMSIS, MCGT, 11103 Rabat, Morocco · Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France
Online Non-Monotone DR-Submodular Maximization Matching the Offline $0.401$ Factor
Sep 2, 2026Vaneet Aggarwal, Yiyang LuSubmodular Optimization Regret Minimization
Purdue University
Constant Individual Regret in General Games
Aug 31, 2026Mingyang Liu, Gabriele Farina, Asuman OzdaglarGame Theory Follow-the-Regularized-Leader
LIDS, EECS, Massachusetts Institute of Technology
PokaiTrainer: Scaling Equilibrium Search to Competitive Pokémon VGC
Aug 29, 2026Max YuGame-Playing Agents Self-Play RL
Independent Researcher
Sharp Minimax Regret for Infinite-Memory Logistic Prediction
Aug 27, 2026Vaneet AggarwalLogistic Regression Bayesian Mixture Models
Purdue University
Sequence prediction under a lying oracle
Aug 14, 2026Puspabeethi Samanta, Nikhil Karamchandani, Jayakrishnan NairRegret Minimization Sequence Modeling
Department of Electrical Engineering IIT Bombay
Tracking the Best Strategy in an Extensive-Form Game
Aug 10, 2026Stephen Pasteris, Rahul Savani, Theodore TurocyImperfect-Information Games Regret Minimization
The Alan Turing Institute · London, United Kingdom · The University of Liverpool +3
Sublogarithmic Swap Regret in Multiplayer General-Sum Games via Hybrid Regularization
Aug 4, 2026Taira TsuchiyaFollow-the-Regularized-Leader Regret Minimization
The University of Tokyo and RIKEN
A Spectral Filtering Approach to Regret Analysis of Distributed Online Control for Linear Dynamical Systems
Aug 3, 2026Ting-Jui ChangDynamical Systems Distributed Optimization
T.J. Chang is with the Department of Aeronautics and Astronautics, National Cheng Kung University, Taiwan.
Simple-regret rates and minimax optimality of fixed-prior expected improvement in Matérn and squared-exponential RKHSs
Jul 31, 2026Emmanuel Vazquez, Sébastien PetitBayesian Optimization Gaussian Processes
Université Paris-Saclay, CNRS, CentraleSupélec, Laboratoire des Signaux et Systèmes, 91190 Gif-sur-Yvette, France · Laboratoire national de métrologie et d’essais (LNE), 1 rue Gaston Boissier, 75724 Paris Cedex 15, France
Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
Jul 29, 2026Atharva Navsalkar, Hongyu Zhou, Vasileios TzoumasDynamical Systems Regime-Switching Dynamical Systems
Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48109 USA
Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex
Jul 25, 2026Chanwoo Park, Asuman OzdaglarSelf-Attention Regret Minimization
1MIT EECS
Breaking the $T^{3/4}$ Barrier for Regret Minimization With Bi-Dimensional CDFs
Jul 22, 2026Matteo Castiglioni, Anna Lunghi, Alberto MarchesiMulti-Armed Bandits Regret Minimization
Politecnico di Milano
Optimizing Regret
Jul 21, 2026Irene AldridgeGradient Descent Regret Minimization
Risk AI Center
Efficient Online Proportional Sampling with Applications to Smoothed Online Learning
Jul 13, 2026Amirmahdi Mirfakhar, Maria-Florina Balcan, Hedyeh BeyhaghiAdaptive Sampling Regret Minimization
University of Massachusetts Amherst · Carnegie Mellon University
Bandit PCA with Minimax Optimal Regret
Jul 12, 2026Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa SahaPrincipal Component Analysis Minimax Regret
Georgia Tech · University of Illinois, Chicago
Contextual Procurement Auctions with Bandit Learning
Jul 7, 2026Yiling Chen, Shi Feng, Sadie ZhaoMechanism Design Regret Minimization
Harvard University

Oct 7, 2026, cs.LGJ/K move · Enter open · S save

Expected Sample Complexity in Multi-Armed Bandits

Nadav Sukenik, Nadav Merlis

Technion – Institute of Technology

Multi-Armed Bandits Regret Minimization Thompson Sampling

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Sample complexity is a widely used metric in sequential decision-making problems, defined as the number of suboptimal decisions during the interaction between the agent and an environment. We study the sample complexity of stochastic multi-armed bandit problems and introduce the expected sample complexity performance measure, analyzing it in a novel framework called approximately correct in expectation (ACE). We show that ACE guarantees imply almost sure convergence to the optimal expected reward, in contrast to high-probability guarantees found in other frameworks, and also show how to convert ACE guarantees into explicit expected regret bounds. We further show that, in contrast to existing measures, deterministic algorithms cannot obtain favorable ACE bounds, and analyze stochastic algorithms in two settings: when the allowed suboptimality level

ε

is known to the algorithm and when it is unknown. In the former, we devise an explore-then-

ε

-greedy algorithm, and in the latter, we analyze the expected sample complexity of Thompson sampling. Finally, we establish nearly matching lower bounds for both settings, showing that the algorithms are tight in

ε

and proving a performance separation between the two regimes.

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Latest papers 81