:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ashkezari, Sajad, Ben-David, Shai
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2602.17103
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Robust Online Learning
par: Ashkezari, Sajad
Publié: (2026)

Beyond Bandit Feedback in Online Multiclass Classification
par: van der Hoeven, Dirk, et autres
Publié: (2021)

Multiclass Online Learnability under Bandit Feedback
par: Raman, Ananth, et autres
Publié: (2023)

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
par: Filmus, Yuval, et autres
Publié: (2024)

Multiclass Transductive Online Learning
par: Hanneke, Steve, et autres
Publié: (2024)

Universal Multiclass Transductive Online Learning
par: Hanneke, Steve, et autres
Publié: (2026)

The Sample Complexity of Multiclass and Sparse Contextual Bandits
par: Erez, Liad, et autres
Publié: (2026)

The Real Price of Bandit Information in Multiclass Classification
par: Erez, Liad, et autres
Publié: (2024)

Fast Rates for Bandit PAC Multiclass Classification
par: Erez, Liad, et autres
Publié: (2024)

Understanding Aggregations of Proper Learners in Multiclass Classification
par: Asilis, Julian, et autres
Publié: (2024)

Learning from positive and unlabeled examples -Finite size sample bounds
par: Mansouri, Farnam, et autres
Publié: (2025)

Online Budget Allocation with Censored Semi-Bandit Feedback
par: Bachoc, François, et autres
Publié: (2025)

A Novel Data-Dependent Learning Paradigm for Large Hypothesis Classes
par: Pour, Alireza F., et autres
Publié: (2025)

How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation
par: Feldman, Shai, et autres
Publié: (2026)

Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents
par: Liu, Junyan, et autres
Publié: (2024)

Confounded Budgeted Causal Bandits
par: Jamshidi, Fateme, et autres
Publié: (2024)

Multi-Task Combinatorial Bandits for Budget Allocation
par: Ge, Lin, et autres
Publié: (2024)

Online Bandit Learning with Offline Preference Data for Improved RLHF
par: Agnihotri, Akhil, et autres
Publié: (2024)

Multi-Agent Lipschitz Bandits
par: Chakraborty, Sourav, et autres
Publié: (2026)

Online Structured Prediction with Fenchel--Young Losses and Improved Surrogate Regret for Online Multiclass Classification with Logistic Loss
par: Sakaue, Shinsaku, et autres
Publié: (2024)

Learning with Multiple Correct Answers -- A Trichotomy of Regret Bounds under Different Feedback Models
par: Pour, Alireza F., et autres
Publié: (2026)

Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits
par: Jeong, Woojin, et autres
Publié: (2024)

Improved Online Confidence Bounds for Multinomial Logistic Bandits
par: Lee, Joongkyu, et autres
Publié: (2025)

Incentivized Learning in Principal-Agent Bandit Games
par: Scheid, Antoine, et autres
Publié: (2024)

Contextual Bandits with Budgeted Information Reveal
par: Gan, Kyra, et autres
Publié: (2023)

On the Computability of Multiclass PAC Learning
par: Gourdeau, Pascale, et autres
Publié: (2025)

Regularization and Optimal Multiclass Learning
par: Asilis, Julian, et autres
Publié: (2023)

Improved Regret Bounds for Online Fair Division with Bandit Learning
par: Schiffer, Benjamin, et autres
Publié: (2025)

Collaborating in Multi-Armed Bandits with Strategic Agents
par: Barnea, Idan, et autres
Publié: (2026)

Active learning from positive and unlabeled examples
par: Mansouri, Farnam, et autres
Publié: (2026)

Bandit Pareto Set Identification: the Fixed Budget Setting
par: Kone, Cyrille, et autres
Publié: (2023)

LLMs Are In-Context Bandit Reinforcement Learners
par: Monea, Giovanni, et autres
Publié: (2024)

Multi-Objective Multi-Agent Bandits: From Learning Efficiency to Fairness Optimization
par: Wang, John, et autres
Publié: (2026)

Multi-Agent Stochastic Bandits Robust to Adversarial Corruptions
par: Ghaffari, Fatemeh, et autres
Publié: (2024)

Adaptive Sample Sharing for Multi Agent Linear Bandits
par: Cherkaoui, Hamza, et autres
Publié: (2023)

BAGEN: Are LLM Agents Budget-Aware?
par: Lin, Yuxiang, et autres
Publié: (2026)

Personalized Reinforcement Learning with a Budget of Policies
par: Ivanov, Dmitry, et autres
Publié: (2024)

GOAL: A Generalist Combinatorial Optimization Agent Learner
par: Drakulic, Darko, et autres
Publié: (2024)

Fixed-Budget Constrained Best Arm Identification in Grouped Bandits
par: Mukherjee, Raunak, et autres
Publié: (2026)

Fixed-Budget Change Point Identification in Piecewise Constant Bandits
par: Lazzaro, Joseph, et autres
Publié: (2025)