conftrace_
← Learning Types
Machine Learning › Learning Types ›

Multi-Armed Bandits

1,057 papers

Papers per year

5
27
87
38
'10
'15
'20
'25

Papers

Further Adaptive Best-of-Both-Worlds Algorithm for Combinatorial Semi-Bandits AISTATS 2023
Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference AISTATS 2023
Causal Entropy Optimization AISTATS 2023
Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo AISTATS 2023
Nonstochastic Contextual Combinatorial Bandits AISTATS 2023
Interactive Learning with Pricing for Optimal and Stable Allocations in Markets AISTATS 2023
Posterior Tracking Algorithm for Classification Bandits AISTATS 2023
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation AISTATS 2023
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws AISTATS 2023
A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs COLT 2023
Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regression COLT 2023
Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design EMNLP 2023
Affective and Dynamic Beam Search for Story Generation EMNLP 2023
On the convergence of the MLE as an estimator of the learning rate in the Exp3 algorithm ICML 2023
Robust Budget Pacing with a Single Sample ICML 2023
Last Switch Dependent Bandits with Monotone Payoff Functions ICML 2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters ICML 2023
Probably Anytime-Safe Stochastic Combinatorial Semi-Bandits ICML 2023
Banker Online Mirror Descent: A Universal Approach for Delayed Online Bandit Learning ICML 2023
Federated Linear Contextual Bandits with User-level Differential Privacy ICML 2023
Combinatorial Neural Bandits ICML 2023
Smooth Non-stationary Bandits ICML 2023
Online Restless Bandits with Unobserved States ICML 2023
Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023
Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning ICML 2023