conftrace_

regret bound

1926 papers

Explore in graph

Also known as

RB

Co-occurring keywords

online learning (1777) multi-armed bandit (1098) contextual bandit (381) stochastic optimization (1060) reinforcement learning (4352) online algorithm (445) upper confidence bound (197) markov decision process (790) thompson sampling (237) online convex optimization (165)

Papers

Generalized Linear Bandits with Local Differential Privacy NIPS 2021
Multitask Bandit Learning Through Heterogeneous Feedback Aggregation AISTATS 2021
An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints NIPS 2021
Online k-means Clustering AISTATS 2021
Non-Stationary Off-Policy Optimization AISTATS 2021
On the Optimality of Batch Policy Optimization Algorithms ICML 2021
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle NIPS 2021
Online Learning in Unknown Markov Games ICML 2021
No Regrets for Learning the Prior in Bandits NIPS 2021
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning NIPS 2021
Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions ICML 2021
The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains NIPS 2021
Optimal Algorithms for Stochastic Contextual Preference Bandits NIPS 2021
Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NIPS 2021
Fair Algorithms for Multi-Agent Multi-Armed Bandits NIPS 2021
Prior-independent Dynamic Auctions for a Value-maximizing Buyer NIPS 2021
Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback NIPS 2021
Decision Making Problems with Funnel Structure: A Multi-Task Learning Approach with Application to Email Marketing Campaigns AISTATS 2021
Bandits with Knapsacks beyond the Worst Case NIPS 2021
Achieving Near Instance-Optimality and Minimax-Optimality in Stochastic and Adversarial Linear Bandits Simultaneously ICML 2021
Multinomial Logit Contextual Bandits: Provable Optimality and Practicality AAAI 2021
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits AISTATS 2021
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization NIPS 2021
Scalable Thompson Sampling using Sparse Gaussian Process Models NIPS 2021
Continuous Mean-Covariance Bandits NIPS 2021