regret minimization

Convergence of Monte Carlo Tree Search in Simultaneous Move Games NeurIPS 2013 From Bandits to Experts: A Tale of Domination and Independence NeurIPS 2013 How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal NeurIPS 2013 Minimax Optimal Algorithms for Unconstrained Linear Optimization NeurIPS 2013 (More) Efficient Reinforcement Learning via Posterior Sampling NeurIPS 2013 Online Learning for Time Series Prediction COLT 2013 Online learning in episodic Markovian decision processes by relative entropy policy search NeurIPS 2013 Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions NeurIPS 2013 Online Learning with Switching Costs and Other Adaptive Adversaries NeurIPS 2013 Regret Minimization for Branching Experts COLT 2013 Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence NeurIPS 2012 Distributed Non-Stochastic Experts NeurIPS 2012 Online Optimization with Gradual Variations COLT 2012 The adversarial stochastic shortest path problem with unknown transition probabilities AISTATS 2012 Adaptive Hedge NeurIPS 2011 A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences COLT 2011 Bandits, Query Learning, and the Haystack Dimension COLT 2011 Beyond the regret minimization barrier: an optimal algorithm for stochastic strongly-convex optimization COLT 2011 Online Learning: Beyond Regret COLT 2011 Prediction strategies without loss NeurIPS 2011 Robust approachability and regret minimization in games with partial monitoring COLT 2011 Non-Stochastic Bandit Slate Problems NeurIPS 2010 Online Classification with Specificity Constraints NeurIPS 2010 Random Walk Approach to Regret Minimization NeurIPS 2010 A Parameter-free Hedging Algorithm NeurIPS 2009