Inverse Linear Bandits via Linear Programs

Ziqing Song, Lin Yang, Ruosong Wang

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

Nearly-Optimal Algorithm for Adversarial Kernelized Bandits

Shogo Iwazaki

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Borda-Based Fair Multi-User Dueling Bandit in Tabular and Generalized Linear Settings

Maheed H Ahmed, Mahsa Ghasemi

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Assistive Dueling Bandits: No-Regret Algorithms for Assisting No-Regret Users

Mark Bedaywi, Cassidy Laidlaw, Austin Tripp, Nika Haghtalab

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

High Probability Risk Control for Online Policy Learning

Yihong Guo, Drew Prinster, Suchi Saria, Anqi Liu

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Multi-Objective Causal Bandits: Minimal Intervention Space and Policy-Level Learning

Muhammad Qasim Elahi, Mahsa Ghasemi, Murat Kocaoglu

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Near-Optimal Best-of-Both-Worlds Algorithms for Decoupled Exploration and Exploitation in Multi-armed Bandits

Hibiki Sekiya, Shinji Ito

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Bayesian Test-Time Inference of Task-Aligned Similarity from Weak Interactive Feedback

Weida Liang, Kenji Kawaguchi

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Bilinear Matching Bandits

Wooseong Cho, Min-hwan Oh

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Nash Social Welfare for Multi Armed Bandits: Trajectory-wise Expected and High Probability Regret

Avishek Ghosh

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Stochastic Matching Bandits with Rare Optimization Updates

Jung-hun Kim, Min-hwan Oh

Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

On the Impact of Side-Information in Bandit Learning: More is Not Always Merrier

Ramakrishnan Krishnamurthy

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Online Maximization of Non-Decomposable Test and Population Utilities

Wojciech Kotlowski, Marek Wydmuch, Krzysztof Dembczynski

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Online Control with Multiple Sensors

Matthew Faw, Siva Theja Maguluri

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

TraceTriage: A Benchmark for Cost-Aware Stop-or-Continue Decisions in Delayed-Outcome Workflows

Sihang Lei, Yihang Qiu, Xueyan Zhao, Yuwei Wang and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Quantum Safe Stochastic Linear Bandits

Ruizhe Zhang, Junyi Wu, Guang Lin

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Safe Linear Bandits with Unknown Safety Gaps

MAOLI LIU, Zhuohua Li, Zeyu Zhang, Xiangxiang Dai and 1 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Linear Contextual Bandits with Quasi-Optimism

Min-hwan Oh, Harin Lee

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

DialBandit: Adaptive Sequential Search with Tunable Evaluation Fidelity

Jianping Huang, Xiang Liu, Feng Shan

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Near-Optimal Learning in Parametric Bandits with Action-Dependent Coarsened Feedback

Zhuohua Li, MAOLI LIU, Yuwen Huang, Cheng Wen and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

Thompson Sampling using Prior-fitted Diffusion Transformers

Sihwa Park, Jingsen Zhu, Vinamr Jain, Sheng-Yen Chou and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Tree-Guided Identify Then Exploit: A Unified Framework of Pure Exploration and Regret Minimization for Dueling Bandits

Pu Wang, Yao-Xiang Ding

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

$\epsilon$-Good Action Identification in Fixed-Budget Monte Carlo Tree Search

Yinan Li, Ngo Tuan Nguyen, Kwang-Sung Jun

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Learning to target with network interference

Adaptive targeting under sparse network interference achieves near-optimal regret depending on structural knowledge, proving standard linear bandits are inefficient and offering practical algorithms.

Xiaomeng Wang, Hamsa Bastani, Osbert Bastani, Zhimei Ren

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

An Efficient Algorithm for Thresholding Monte Carlo Tree Search

A Track-and-Stop algorithm solves thresholding Monte Carlo Tree Search with asymptotically optimal sample complexity, and a ratio-based D-Tracking modification improves empirical efficiency and reduces per-round computation to logarithmic time.

Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama, Koji Tabata

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Learning to Bid in Repeated Second-Price Auctions with Dynamic Values and Aggregated Feedback

A bidder with dynamic auction values and only aggregated feedback learns near-optimal bidding policies via plug-in estimators with logarithmic or sublinear regret.

Benjamin Heymann, Otmane Sakhi

Paris Poster Session 5, Fri, Dec 11, 11:30 AM–1:30 PM, Paris Poster Hall · Published 2026

Bayesian Decision Making around Experts

In Bayesian multi-armed bandits, pretraining on expert data tightens regret bounds by mutual information with the optimal action, while an information-directed rule selects data sources maximizing immediate information gain, and trust inference safeguards against ineffective or compromised experts.

Daniel Jarne Ornia, Joel Dyer, Nicholas Bishop, Anisoara Calinescu and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Two-Fidelity Best-Action Identification for Stochastic Minimax Tree

2FFS adaptively combines cheap biased heuristics and expensive accurate rollouts to identify best actions in stochastic minimax trees with fewer samples than baselines.

Peter Chen, Xi Chen

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026 · ▲ 2 on Hugging Face

Direction-Aware Offline-to-Online Learning in Linear Contextual Bandits

A directional bias certificate enables Ellipsoidal-MINUCB to safely exploit offline data in linear contextual bandits, reducing regret when low-bias directions align with historical coverage.

Zean Han, Ruihan Lin, Zezhen Ding, Jiheng Zhang

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

Not all uncertainty is alike: volatility, stochasticity, and exploration

Volatility and stochasticity both increase uncertainty but drive optimal exploration in opposite directions; CAUSE captures this asymmetry and improves restless-bandit performance.

Payam Piray

Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Prudent-Banker: No Extra Fees for Baseline Safety in Adversarial Bandits With and Without Delays

Prudent-Banker achieves minimax adversarial bandit regret with near-constant safe baseline regret despite arbitrary delayed feedback, matching new lower bounds.

Ting Hu, Luanda Cai, Emmanouil-Vasileios Vlatakis-Gkaragkounis

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing

Proposed correlation-aware contextual bandit algorithms use coupled and decoupled surrogate rewards to improve LLM routing efficiency and robustness.

Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi, Vijaykrishnan Narayanan

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability

Forward-KL-regularized offline contextual bandits achieve epsilon^{-1} sample complexity under single-policy concentrability via pessimism, with matching lower bounds showing slow rates at weak regularization.

Qingyue Zhao, Kaixuan Ji, Heyang Zhao, Quanquan Gu

Atlanta Poster Session 4, Thu, Dec 10, 4:30 PM–7:30 PM, Hall C1 · Published 2026

SURF: Steering the Scalarization Weight to Uniformly Traverse the Pareto Front

SURF inverts a geometric arc-length cumulative distribution to sample scalarization weights yielding uniform Pareto front coverage and converges linearly to a finite-sampling floor.

Liuyuan Jiang, Chentong Huang, Lisha Chen

Atlanta Poster Session 5, Fri, Dec 11, 10:00 AM–1:00 PM, Hall C1 · Published 2026

Bandits via Additive Quantized Representations

Residual Quantization maps contexts to discrete additive codes enabling nonlinear contextual bandits with strictly bounded memory, beating linear variants on 11 of 13 datasets and matching heavy retrained baselines with up to 1000x less memory.

Ami Tavory, Noam Touitou, Tal Sarig, Frank Cheng and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Provably Efficient Regularized Online RLHF with Generalized Bilinear Preferences

Online RLHF with generalized bilinear preferences achieves polylogarithmic regret via generic strong convexity and skew-symmetry, proving fast rates are not KL-specific.

Junghyun Lee, Minju Hong, Kwang-Sung Jun, Chulhee Yun and 1 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

Active Context Selection Improves Simple Regret in Contextual Bandits

Active context selection improves contextual bandit simple regret from order root n over T times L1/2 norm of p to root n over T times L2/3 norm, with gains up to k to the 1/4.

Mohammad Shahverdikondori, Jalal Etesami, Negar Kiyavash

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026