Cover Image

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kim, Wonyoung, Oh, Min-Hwan, Iyengar, Garud, Zeevi, Assaf
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning the Pareto Front Using Bootstrapped Observation Samples
by: Kim, Wonyoung, et al.
Published: (2023)

Linear Bandits with Partially Observable Features
by: Kim, Wonyoung, et al.
Published: (2025)

The Cost of Learning under Multiple Change Points
by: Gafni, Tomer, et al.
Published: (2026)

Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
by: Li, Long-Fei, et al.
Published: (2024)

Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
by: Hwang, Taehyun, et al.
Published: (2022)

Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
by: Lee, Joongkyu, et al.
Published: (2026)

Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization
by: Iyengar, Garud, et al.
Published: (2024)

Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
by: Cho, Wooseong, et al.
Published: (2024)

Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities
by: Hwang, Taehyun, et al.
Published: (2026)

A Tractable Online Learning Algorithm for the Multinomial Logit Contextual Bandit
by: Agrawal, Priyank, et al.
Published: (2020)

Contextual Multinomial Logit Bandits with General Value Functions
by: Zhang, Mengxiao, et al.
Published: (2024)

Adaptive Querying with AI Persona Priors
by: Wang, Kaizheng, et al.
Published: (2026)

Bayesian Design Principles for Frequentist Sequential Learning
by: Xu, Yunbei, et al.
Published: (2023)

Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
by: Lee, Joongkyu, et al.
Published: (2024)

Adaptive Data Augmentation for Thompson Sampling
by: Kim, Wonyoung
Published: (2025)

Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
by: Park, Jaehyun, et al.
Published: (2024)

Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits
by: Xu, Yunbei, et al.
Published: (2020)

Optimizer's Information Criterion: Dissecting and Correcting Bias in Data-Driven Optimization
by: Iyengar, Garud, et al.
Published: (2023)

A Broader View of Thompson Sampling
by: Qu, Yanlin, et al.
Published: (2025)

Improved Online Confidence Bounds for Multinomial Logistic Bandits
by: Lee, Joongkyu, et al.
Published: (2025)

Learning Multinomial Logits in $O(n \log n)$ time
by: Chierichetti, Flavio, et al.
Published: (2026)

Learning in Position-Aware Multinomial Logit Bandits: From Multiplicative to General Position Effects
by: Chen, Xi, et al.
Published: (2026)

Model-Free Assessment of Simulator Fidelity via Quantile Curves
by: Iyengar, Garud, et al.
Published: (2025)

Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs
by: Boudart, Pierre, et al.
Published: (2026)

Minimax Optimal Reinforcement Learning with Quasi-Optimism
by: Lee, Harin, et al.
Published: (2025)

Adaptive Resolving Methods for Reinforcement Learning with Function Approximations
by: Jiang, Jiashuo, et al.
Published: (2025)

A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
by: Zhao, Heyang, et al.
Published: (2023)

Optimal and Practical Batched Linear Bandit Algorithm
by: Yu, Sanghoon, et al.
Published: (2025)

Peng's Q($λ$) for Conservative Value Estimation in Offline Reinforcement Learning
by: Kim, Byeongchan, et al.
Published: (2026)

Achieving Limited Adaptivity for Multinomial Logistic Bandits
by: Midigeshi, Sukruta Prakash, et al.
Published: (2025)

Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
by: Yu, Sanghoon, et al.
Published: (2026)

Latent Representation Alignment for Offline Goal-Conditioned Reinforcement Learning
by: Kang, Hyungkyu, et al.
Published: (2026)

Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
by: Zhang, Haochen, et al.
Published: (2026)

FIRAL: An Active Learning Algorithm for Multinomial Logistic Regression
by: Chen, Youguang, et al.
Published: (2024)

Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
by: Boudart, Pierre, et al.
Published: (2025)

Combinatorial Reinforcement Learning with Preference Feedback
by: Lee, Joongkyu, et al.
Published: (2025)

ADAM Optimization with Adaptive Batch Selection
by: Kim, Gyu Yeol, et al.
Published: (2025)

Nonstationary Reinforcement Learning with Linear Function Approximation
by: Zhou, Huozhi, et al.
Published: (2020)

Replicable Reinforcement Learning with Linear Function Approximation
by: Eaton, Eric, et al.
Published: (2025)

A Temporally Correlated Latent Exploration for Reinforcement Learning
by: Oh, SuMin, et al.
Published: (2024)