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regret bound
regret bound
1926 papers
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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
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
ICML 2016
Online Learning with Feedback Graphs Without the Graphs
ICML 2016
DCM Bandits: Learning to Rank with Multiple Clicks
ICML 2016
Efficient Algorithms for Adversarial Contextual Learning
ICML 2016
Coin Betting and Parameter-Free Online Learning
NIPS 2016
Refined Lower Bounds for Adversarial Bandits
NIPS 2016
Copeland Dueling Bandit Problem: Regret Lower Bound, Optimal Algorithm, and Computationally Efficient Algorithm
ICML 2016
Tracking the Best Expert in Non-stationary Stochastic Environments
NIPS 2016
Online Pricing with Strategic and Patient Buyers
NIPS 2016
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
NIPS 2016
Conservative Bandits
ICML 2016
Multi-armed Bandits: Competing with Optimal Sequences
NIPS 2016
Online Convex Optimization with Unconstrained Domains and Losses
NIPS 2016
Adaptive Smoothed Online Multi-Task Learning
NIPS 2016
Open Problem: Parameter-Free and Scale-Free Online Algorithms
COLT 2016
Open Problem: Second order regret bounds based on scaling time
COLT 2016
Double Thompson Sampling for Dueling Bandits
NIPS 2016
Optimistic Bandit Convex Optimization
NIPS 2016
Reinforcement Learning of POMDPs using Spectral Methods
COLT 2016
Regret Bounds for Non-decomposable Metrics with Missing Labels
NIPS 2016
Combinatorial Multi-Armed Bandit with General Reward Functions
NIPS 2016
Efficient Second Order Online Learning by Sketching
NIPS 2016
Multiple-Play Bandits in the Position-Based Model
NIPS 2016
Scalable Adaptive Stochastic Optimization Using Random Projections
NIPS 2016
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
ICML 2016
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