3 papers
cs.LG2019
Advantage Amplification in Slowly Evolving Latent-State Environments
Martin Mladenov, Ofer Meshi, Jayden Ooi +2
Latent-state environments with long horizons, such as those faced by recommender systems, pose significant challenges for reinforcement learning (RL). In this work, we identify and…
cs.LG2019
Empirical Bayes Regret Minimization
Chih-Wei Hsu, Branislav Kveton, Ofer Meshi +2
Most bandit algorithm designs are purely theoretical. Therefore, they have strong regret guarantees, but also are often too conservative in practice. In this work, we pioneer the i…
math.OC2016
Linear-memory and Decomposition-invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
Dan Garber, Ofer Meshi
Recently, several works have shown that natural modifications of the classical conditional gradient method (aka Frank-Wolfe algorithm) for constrained convex optimization, provably…