Linear Regret

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Period ending 2026-09-21

1 new paper

A weekly snapshot of new work published in Linear Regret.

Period ending 2026-09-14

2 new papers

A weekly snapshot of new work published in Linear Regret.

83 papers

Latest in Linear Regret

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Adapting to Decision-Relevant Non-Stationarity in Decentralized Heterogeneous Bandits
Sep 15, 2026Zhaojun PengLinear Regret Decentralization
Thompson Sampling for Non-Monotone Convex Ridge Bandits: Monotonicity Is Not Needed for Polynomial Regret
Sep 11, 2026Xuan LiThompson Sampling Linear Regret
Meta-LinEXP3: Online-within-Online Learning for Adversarial Linear Contextual Bandits
Sep 9, 2026Hao Li, Jie Xu, Zheng XieLinear Contextual Bandits Meta-Learning
Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader
Sep 8, 2026Ashkan Soleymani, Gabriele Farina, Patrick JailletMirror Descent Linear Regret
No-Regret Mixing of LRU and LFU with Optimal Switching Cost
Sep 7, 2026Younes Ben Mazziane, Xinying ZouEviction Linear Regret
Sharp Minimax Regret for Infinite-Memory Logistic Prediction
Aug 27, 2026Vaneet AggarwalLinear Regret Regret
Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
Aug 17, 2026Shion Takeno, Shogo IwazakiGeneralized Linear Bandit Linear Regret
Exponential Convex Calibration Dimension for the Multi-Label Jaccard Measure
Aug 13, 2026Mingyuan ZhangMulti-Label Classification Candidate Label Distributions
An Efficient Near-Optimal Algorithm for Adversarial $m$ -Set Bandits
Aug 12, 2026Francesco Bacchiocchi, Tommaso Cesari, Roberto ColomboniMulti-Armed Bandits Linear Regret
Dirichlet Follow-the-Leader Closes the Gap in Simultaneous Multiclass U-Calibration
Aug 7, 2026Pahan DewasurendraLinear Regret Multiplier Bootstrap
RASP-QAOA: Resource-Aware Per-Instance Selection for Exact QAOA Simulation
Aug 6, 2026Chih-Chung HsuQuantum Approximate Optimization Algorithm Penalties
Climate-Dyna Deep Hedging for XVAs: Model-Based Reinforcement Learning, Residual Climate HVA, and Hedge-Instrument Discovery
Aug 2, 2026Xiaozhen Wang, Francois Buet-GolfouseClimate Value Functions
When Do Surrogate Updates Improve Decisions? A Local Theory of Trajectory-Wise Transfer
Aug 2, 2026Yuyang ShenTime-Step-Wise Learnable Surrogate Gradient Function Linear Regret
The Greedy Advantage in Finite-Horizon Bandits
Jul 31, 2026Kai Zhou, Michael Lingzhi Li, Kai WangOptimal Bandit Algorithms Linear Regret
Simple-regret rates and minimax optimality of fixed-prior expected improvement in Matérn and squared-exponential RKHSs
Jul 31, 2026Emmanuel Vazquez, Sébastien PetitKernel Hilbert Spaces Linear Regret
Parameter-Free Dynamic Regret under Heavy-Tailed Noise
Jul 29, 2026Vaneet AggarwalLinear Regret Convex Optimization
Data-Dependent Regret and Polyak Corrections for Constrained Online Convex Optimization
Jul 28, 2026Wentao ZhangConvex Optimization O(T^Β)$ Simultaneous Regret
Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex
Jul 25, 2026Chanwoo Park, Asuman OzdaglarLinear Regret Regret
When Can Safe Controllers Adapt? Information before Commitment
Jul 18, 2026Venkatesh SaligramaAdaptive Controller Safety Constraints
Information-Directed Sampling for Causal Bandits
Jul 17, 2026Muhammad Qasim Elahi, Murat Kocaoglu, Mahsa GhasemiLinear Contextual Bandits Linear Regret
When Is Delegated Play Truthful? Within-Range Regret and the Trilemma of Aligned Delegation
Jul 15, 2026Taksch DubeHonesty Incentives
Price of Fairness in Bandits: A Tight Minimax Characterization
Jul 15, 2026Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray ChowdhuryLinear Regret Bandits
Functional Bilevel Optimization for Predictive Fairness
Jul 6, 2026Ieva Petrulionyte, Julien Mairal, Michael ArbelBilevel Optimization Quadratic Slack Penalty
A Hierarchy of Policy Learning Problems
Jul 3, 2026Hamsa Bastani, Osbert Bastani, Shihan ChenOptimal Policies Linear Regret
Dynamic Regret for Non-Stationary Linear Bandits via Misspecification Reductions
Jul 3, 2026Zihao Hu, Yuan Yao, Jiheng Zhang +1Near-Optimal Regret Guarantees Linear Bandits
Online Resource Allocation with Continuous Random Consumption: Regret under Degeneracy
Jul 2, 2026Jiawei ZhangLinear Regret Regret
Tight Lower Bounds for the Multi-Secretary Problem via Bellman Certificates
Jul 2, 2026Jiawei ZhangLinear Regret Lower Bounds
Liquidity-Based Audit of Algorithmic Trading Strategies
Jun 27, 2026Irene AldridgePrices Linear Regret
Adaptive Bayes exactly tracks information over intrinsic time
Jun 26, 2026Akshay BalsubramaniLinear Regret Bayesian
Selective Ensemble Based on Preference-Directed Multi-Objective Bandits
Jun 20, 2026Lanjihong Ma, Zhen-Yu Zhang, Masashi Sugiyama +1Multi-Armed Bandits Pareto Frontier
A Solver-Free Training Method for Predict-then-Optimize
Jun 17, 2026Beichen Wan, Mo LiuLearning-Augmented Algorithms Llm-Based Optimization Modeling
Toward Simultaneously Optimal Regret in U-Calibration
Jun 16, 2026Rafael Frongillo, Haipeng Luo, Nishant A. Mehta +1Linear Regret Regret
Sequential Hiring of Contingent Workers Through Learning-Based Optimization
Jun 16, 2026Chris Lee, Xiuli Chao, Izak DuenyasStochastic Delays Optimal Bandit Algorithms
Decision-Weighted Flow Matching for Contextual Stochastic Optimization
Jun 15, 2026Jize Xie, Haomiao Wu, Qiang Chen +2Conditional Flow Matching Stochastic Optimization
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
Jun 9, 2026Rahul Roy, Nur Sunar, Jayashankar M. SwaminathanLinear Regret Semiparametric Framework
Multi-Armed Bandits with Arriving Arms: Sequential Screening, Dynamic Regret, and Sublinear Guarantees
Jun 8, 2026Deqi Zheng, Xiaoyang Xu, Yuhong YangLinear Bandits Multi-Armed Bandits
Evaluating AI Investment Strategies
Jun 7, 2026Irene AldridgeAlgorithm Auditing Linear Regret
Noise-Adaptive High-Probability Regret Bounds for Online Convex Optimization
Jun 6, 2026Wentao Zhang, Yutong Zhang, Wentao MoConvex Optimization Linear Regret
Learn to Match: Two-Sided Matching with Temporally Extended Feedback
Jun 4, 2026Haijing Zong, Yancheng Liang, Boyang Zhou +1Bipartite Perfect Matchings Matching Markets
Online Learning with Gradient-Variation Interval Regret
Jun 2, 2026Yan-Feng Xie, Shuche Wang, Peng Zhao +1Learning-Augmented Algorithms Linear Regret
Dynamics Are Learned, Not Told: Semi-Supervised Discovery of Latent Dynamics Geometries For Zero-Shot Policy Adaptation
Jun 1, 2026Zhiming Xu, Weitao Zhou, Xianghui Pan +4Latent Cluster Geometry Linear Regret
AdaWeather: Adaptively Mixing Probabilistic Weather Forecasts with Logarithmic Regret
Jun 1, 2026Saptarishi Dhanuka, Sarvesh Iyer, Manmeet Singh +5Artificial Intelligence Weather Models Linear Regret
Tree-Guided Identify-Then-Exploit: A Unified Framework of Best Arm Identification and Regret Minimization for Dueling Bandits
Jun 1, 2026Pu Wang, Yao-Xiang DingBandits Linear Regret
Support sufficiency as action-sufficient compression: a single-cycle rate-regret formulation
May 28, 2026Mark WalshLinear Regret Sufficient Conditions
Optimality of Sequential Filtering Under Independent Cost and Selectivity Models
May 28, 2026Hrishikesh Paranjape, Abhishek Mandal, Xian SunOrder Matters Sequential Decision Making
TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning
May 27, 2026Chusen Li, Zhou Liu, Shuigeng Zhou +1Deep Reinforcement Learning Group Relative Policy Optimization
Infra-Bayesian Reinforcement Learning Agents Outperform Classical RL For Worst-Case Robustness
May 22, 2026Manish Aryal, Faiyaz Azam, Agnivo Banerjee +11Entropy Regularized Reinforcement Learning Deep Reinforcement Learning
Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing
May 22, 2026Shugang Hao, Lingjie DuanCrowdsourced Framework Large Language Model Fine-Tuning
Finite-Time Regret Analysis of Retry-Aware Bandits
May 20, 2026Bingkui Tong, Junpei Komiyama, Soichiro Nishimori +1Multi-Armed Bandits Linear Regret
Active Context Selection Improves Simple Regret in Contextual Bandits
May 19, 2026Mohammad Shahverdikondori, Jalal Etesami, Negar KiyavashOptimal Bandit Algorithms Linear Regret
Catching a Moving Subspace: Low-Rank Bandits Beyond Stationarity
May 18, 2026Hamed Khosravi, Xiaoming HuoMulti-Armed Bandits Bandits
Calibeating for general proper losses: A Bregman divergence approach
May 17, 2026Maximilian Fichtl, Cristóbal Guzmán, Nishant A. MehtaBregman Divergences Linear Regret
Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning
May 15, 2026Zijun Chen, Zihan ZhangLinear Regret Value Functions
In-Context Learning for Data-Driven Censored Inventory Control
May 14, 2026Sohom Mukherjee, Anh-Duy Pham, Richard Pibernik +1Monotonic Inventory Estimation Linear Regret
IGT-OMD: Implicit Gradient Transport for Decision-Focused Learning under Delayed Feedback
May 12, 2026Benjamin Amoh, Geoffrey G. Parker, Wesley MarreroLinear Regret Bilevel Optimization
Learning Weakly Communicating Average-Reward CMDPs: Strong Duality and Improved Regret
May 12, 2026Kihyun Yu, Beomhan Baek, Dabeen LeeMarkov Decision Processes Linear Regret
Online Sharp-Calibrated Bayesian Optimization
May 11, 2026Marshal Arijona Sinaga, Julien Martinelli, Teemu Turpeinen +1Bayesian Optimization Gaussian Process
PFN-TS: Thompson Sampling for Contextual Bandits via Prior-Data Fitted Networks
May 11, 2026Yan Shuo Tan, Kenyon Ng, Ruizhe Deng +3Thompson Sampling Tabular Prior-Data Fitted Network
The Value of Mechanistic Priors in Sequential Decision Making
May 11, 2026Itai Shufaro, Gal Benor, Shie MannorSequential Decision Making Prior Knowledge
Online Set Learning from Precision and Recall Feedback
May 10, 2026Lee Cohen, Yishay Mansour, Shay Moran +1Learning-Augmented Algorithms Optimal Sample Complexity

Sep 15, 2026 · cs.LGJ/K move · Enter open · S save

Adapting to Decision-Relevant Non-Stationarity in Decentralized Heterogeneous Bandits

Zhaojun Peng

Decentralized bandit systems often contain heterogeneous agents: rewards can change at individual agents even when the best action for the network stays the same. These local changes may cancel when rewards are averaged across agents, so the number of local changes

\Stloc

can be much larger than the number of changes in the best common arm

\Stdec

. We introduce Decision-Relevant Fresh Comparison (DRFC), which uses new, balanced samples from all agents to compare arms at the network level and switches only when fresh global evidence indicates that the common best arm has changed. We prove a high-probability dynamic regret bound with no adaptation term depending on

\Stloc

, and show that every algorithm must still pay for identifying genuine decision switches and propagating them through the communication graph. Under a distinct time-average benchmark, an anytime-valid sliding-window extension handles gradual drift; experiments on synthetic, semi-real, and MovieLens-1M replays show that DRFC ignores decision-irrelevant local changes while the extension avoids false switches.

Linear Regret Decentralization Sequential Decision Making Non-Stationarity Comparison

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