157 citations · 713 across the 56 of their papers we have counts for
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Faster saddle-point optimization for solving large-scale Markov decision processes
Joan Bas-Serrano, Gergely Neu
We consider the problem of computing optimal policies in average-reward Markov decision processes. This classical problem can be formulated as a linear program directly amenable to…
Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates
Hugo Penedones, Carlos Riquelme, Damien Vincent +5
We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (T…
Bandit Principal Component Analysis
Wojciech Kotłowski, Gergely Neu
We consider a partial-feedback variant of the well-studied online PCA problem where a learner attempts to predict a sequence of -dimensional vectors in terms of a quadratic loss…
Beating SGD Saturation with Tail-Averaging and Minibatching
Nicole Mücke, Gergely Neu, Lorenzo Rosasco
While stochastic gradient descent (SGD) is one of the major workhorses in machine learning, the learning properties of many practically used variants are poorly understood. In this…