Nearly Minimax Optimal Regret for Multinomial Logistic Bandit
Fuente:
arXiv
Saved in:
| Main Authors: | Lee, Joongkyu, Oh, Min-hwan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improved Online Confidence Bounds for Multinomial Logistic Bandits
by: Lee, Joongkyu, et al.
Published: (2025)
by: Lee, Joongkyu, et al.
Published: (2025)
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
by: Lee, Joongkyu, et al.
Published: (2026)
by: Lee, Joongkyu, et al.
Published: (2026)
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
by: Cho, Wooseong, et al.
Published: (2024)
by: Cho, Wooseong, et al.
Published: (2024)
Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
by: Lee, Joongkyu, et al.
Published: (2026)
by: Lee, Joongkyu, et al.
Published: (2026)
Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
by: Lee, Joongkyu, et al.
Published: (2024)
by: Lee, Joongkyu, et al.
Published: (2024)
Combinatorial Reinforcement Learning with Preference Feedback
by: Lee, Joongkyu, et al.
Published: (2025)
by: Lee, Joongkyu, et al.
Published: (2025)
Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
by: Hwang, Taehyun, et al.
Published: (2022)
by: Hwang, Taehyun, et al.
Published: (2022)
Unified Framework of Distributional Regret in Multi-Armed Bandits and Reinforcement Learning
by: Lee, Harin, et al.
Published: (2026)
by: Lee, Harin, et al.
Published: (2026)
Minimax Optimal Reinforcement Learning with Quasi-Optimism
by: Lee, Harin, et al.
Published: (2025)
by: Lee, Harin, et al.
Published: (2025)
Tractable Multinomial Logit Contextual Bandits with Non-Linear Utilities
by: Hwang, Taehyun, et al.
Published: (2026)
by: Hwang, Taehyun, et al.
Published: (2026)
Minimax Optimal Variance-Aware Regret Bounds for Multinomial Logistic MDPs
by: Boudart, Pierre, et al.
Published: (2026)
by: Boudart, Pierre, et al.
Published: (2026)
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
by: Lee, Junghyun, et al.
Published: (2023)
by: Lee, Junghyun, et al.
Published: (2023)
Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
by: Liu, Shuai, et al.
Published: (2026)
by: Liu, Shuai, et al.
Published: (2026)
Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
by: Boudart, Pierre, et al.
Published: (2025)
by: Boudart, Pierre, et al.
Published: (2025)
Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
by: Kim, Seok-Jin, et al.
Published: (2024)
by: Kim, Seok-Jin, et al.
Published: (2024)
Optimal and Practical Batched Linear Bandit Algorithm
by: Yu, Sanghoon, et al.
Published: (2025)
by: Yu, Sanghoon, et al.
Published: (2025)
Lasso Bandit with Compatibility Condition on Optimal Arm
by: Lee, Harin, et al.
Published: (2024)
by: Lee, Harin, et al.
Published: (2024)
Improved Regret of Linear Ensemble Sampling
by: Lee, Harin, et al.
Published: (2024)
by: Lee, Harin, et al.
Published: (2024)
Preference-based Reinforcement Learning beyond Pairwise Comparisons: Benefits of Multiple Options
by: Lee, Joongkyu, et al.
Published: (2025)
by: Lee, Joongkyu, et al.
Published: (2025)
Infrequent Exploration in Linear Bandits
by: Lee, Harin, et al.
Published: (2025)
by: Lee, Harin, et al.
Published: (2025)
Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates
by: Yu, Sanghoon, et al.
Published: (2026)
by: Yu, Sanghoon, et al.
Published: (2026)
Learning Uncertainty-Aware Temporally-Extended Actions
by: Lee, Joongkyu, et al.
Published: (2024)
by: Lee, Joongkyu, et al.
Published: (2024)
Achieving Limited Adaptivity for Multinomial Logistic Bandits
by: Midigeshi, Sukruta Prakash, et al.
Published: (2025)
by: Midigeshi, Sukruta Prakash, et al.
Published: (2025)
Blessings of Multiple Good Arms in Multi-Objective Linear Bandits
by: Ann, Heesang, et al.
Published: (2026)
by: Ann, Heesang, et al.
Published: (2026)
Follow-the-Perturbed-Leader with Fréchet-type Tail Distributions: Optimality in Adversarial Bandits and Best-of-Both-Worlds
by: Lee, Jongyeong, et al.
Published: (2024)
by: Lee, Jongyeong, et al.
Published: (2024)
Queueing Matching Bandits with Preference Feedback
by: Kim, Jung-hun, et al.
Published: (2024)
by: Kim, Jung-hun, et al.
Published: (2024)
Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
by: Kim, Chaiwon, et al.
Published: (2025)
by: Kim, Chaiwon, et al.
Published: (2025)
Exploration via Feature Perturbation in Contextual Bandits
by: Yi, Seouh-won, et al.
Published: (2025)
by: Yi, Seouh-won, et al.
Published: (2025)
Stochastic Matching Bandits with Rare Optimization Updates
by: Kim, Jung-hun, et al.
Published: (2025)
by: Kim, Jung-hun, et al.
Published: (2025)
Near-Optimal Regret in Adversarial Kernel Bandits
by: Zhang, Yu-Jie, et al.
Published: (2026)
by: Zhang, Yu-Jie, et al.
Published: (2026)
Thompson Sampling for Multi-Objective Linear Contextual Bandit
by: Park, Somangchan, et al.
Published: (2025)
by: Park, Somangchan, et al.
Published: (2025)
Near Optimal Pure Exploration in Logistic Bandits
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)
by: Rivera, Eduardo Ochoa, et al.
Published: (2024)
Revisiting Follow-the-Perturbed-Leader with Unbounded Perturbations in Bandit Problems
by: Lee, Jongyeong, et al.
Published: (2025)
by: Lee, Jongyeong, et al.
Published: (2025)
Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
by: Tajdini, Artin, et al.
Published: (2023)
by: Tajdini, Artin, et al.
Published: (2023)
Oracle-Efficient Combinatorial Semi-Bandits
by: Kim, Jung-hun, et al.
Published: (2025)
by: Kim, Jung-hun, et al.
Published: (2025)
Asymptotically and Minimax Optimal Regret Bounds for Multi-Armed Bandits with Abstention
by: Yang, Junwen, et al.
Published: (2024)
by: Yang, Junwen, et al.
Published: (2024)
Experimental Design for Semiparametric Bandits
by: Kim, Seok-Jin, et al.
Published: (2025)
by: Kim, Seok-Jin, et al.
Published: (2025)
Near-Optimal Regret in Linear MDPs with Aggregate Bandit Feedback
by: Cassel, Asaf, et al.
Published: (2024)
by: Cassel, Asaf, et al.
Published: (2024)
Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
by: Di, Qiwei, et al.
Published: (2024)
by: Di, Qiwei, et al.
Published: (2024)
Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
by: Yoon, Deokgyu, et al.
Published: (2026)
by: Yoon, Deokgyu, et al.
Published: (2026)
Similar Items
-
Improved Online Confidence Bounds for Multinomial Logistic Bandits
by: Lee, Joongkyu, et al.
Published: (2025) -
Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification
by: Lee, Joongkyu, et al.
Published: (2026) -
Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
by: Cho, Wooseong, et al.
Published: (2024) -
Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent
by: Lee, Joongkyu, et al.
Published: (2026) -
Demystifying Linear MDPs and Novel Dynamics Aggregation Framework
by: Lee, Joongkyu, et al.
Published: (2024)