Copertina

Achieving Limited Adaptivity for Multinomial Logistic Bandits

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Midigeshi, Sukruta Prakash, Goyal, Tanmay, Sinha, Gaurav
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback
di: Goyal, Tanmay, et al.
Pubblicazione: (2025)

Plan*RAG: Efficient Test-Time Planning for Retrieval Augmented Generation
di: Verma, Prakhar, et al.
Pubblicazione: (2024)

Generalized Linear Bandits with Limited Adaptivity
di: Sawarni, Ayush, et al.
Pubblicazione: (2024)

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

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

Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion
di: Lee, Junghyun, et al.
Pubblicazione: (2023)

Causal Contextual Bandits with Adaptive Context
di: Madhavan, Rahul, et al.
Pubblicazione: (2024)

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

Linear Contextual Bandits with Hybrid Payoff: Revisited
di: Das, Nirjhar, et al.
Pubblicazione: (2024)

Riemannian Multinomial Logistics Regression for SPD Neural Networks
di: Chen, Ziheng, et al.
Pubblicazione: (2023)

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

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

Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function
di: Pham, Tuan Minh, et al.
Pubblicazione: (2026)

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

RMLR: Extending Multinomial Logistic Regression into General Geometries
di: Chen, Ziheng, et al.
Pubblicazione: (2024)

Neural Logistic Bandits
di: Bae, Seoungbin, et al.
Pubblicazione: (2025)

Combinatorial Logistic Bandits
di: Liu, Xutong, et al.
Pubblicazione: (2024)

A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts
di: Nguyen, Huy, et al.
Pubblicazione: (2023)

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

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

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

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

BanditQ: Fair Bandits with Guaranteed Rewards
di: Sinha, Abhishek
Pubblicazione: (2023)

Scalable Inference for Bayesian Multinomial Logistic-Normal Dynamic Linear Models
di: Saxena, Manan, et al.
Pubblicazione: (2024)

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

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

Near Optimal Pure Exploration in Logistic Bandits
di: Rivera, Eduardo Ochoa, et al.
Pubblicazione: (2024)

An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits
di: Gouverneur, Amaury, et al.
Pubblicazione: (2024)

Improving Minimax Estimation Rates for Contaminated Mixture of Multinomial Logistic Experts via Expert Heterogeneity
di: Yan, Fanqi, et al.
Pubblicazione: (2026)

Constrained Contextual Bandits with Adversarial Contexts
di: Sarkar, Dhruv, et al.
Pubblicazione: (2026)

Collaborative Min-Max Regret in Grouped Multi-Armed Bandits
di: Blanchard, Moïse, et al.
Pubblicazione: (2025)

Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models
di: Tran, TrungKhang, et al.
Pubblicazione: (2026)

Causal Bandits: The Pareto Optimal Frontier of Adaptivity, a Reduction to Linear Bandits, and Limitations around Unknown Marginals
di: Liu, Ziyi, et al.
Pubblicazione: (2024)

Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits
di: Liu, Shuai, et al.
Pubblicazione: (2026)

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
di: Kim, Wonyoung, et al.
Pubblicazione: (2026)

A Simple Reduction Scheme for Constrained Contextual Bandits with Adversarial Contexts via Regression
di: Sarkar, Dhruv, et al.
Pubblicazione: (2026)

Logistic Bandits with $\tilde{O}(\sqrt{dT})$ Regret without Context Diversity Assumptions
di: Bae, Seoungbin, et al.
Pubblicazione: (2026)

The Fundamental Limits of Fraud Detection in Card Payment Networks
di: Dhama, Gaurav
Pubblicazione: (2026)

MNL-Bandit with Knapsacks: a near-optimal algorithm
di: Aznag, Abdellah, et al.
Pubblicazione: (2021)

Achieving Optimal Static and Dynamic Regret Simultaneously in Bandits with Deterministic Losses
di: Qian, Jian, et al.
Pubblicazione: (2026)