149 citations · 348 across the 17 of their papers we have counts for
6 papers · 1 filter
Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello +6
We consider reinforcement learning in an environment modeled by an episodic, finite, stage-dependent Markov decision process of horizon with states, and actions. The pe…
Orthogonal Estimation of Wasserstein Distances
Mark Rowland, Jiri Hron, Yunhao Tang +3
Wasserstein distances are increasingly used in a wide variety of applications in machine learning. Sliced Wasserstein distances form an important subclass which may be estimated ef…
Statistics and Samples in Distributional Reinforcement Learning
Mark Rowland, Robert Dadashi, Saurabh Kumar +3
We present a unifying framework for designing and analysing distributional reinforcement learning (DRL) algorithms in terms of recursively estimating statistics of the return distr…
Antithetic and Monte Carlo kernel estimators for partial rankings
Maria Lomeli, Mark Rowland, Arthur Gretton +1
In the modern age, rankings data is ubiquitous and it is useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, m…
Gaussian Process Behaviour in Wide Deep Neural Networks
Alexander G. de G. Matthews, Mark Rowland, Jiri Hron +2
Whilst deep neural networks have shown great empirical success, there is still much work to be done to understand their theoretical properties. In this paper, we study the relation…
An Analysis of Categorical Distributional Reinforcement Learning
Mark Rowland, Marc G. Bellemare, Will Dabney +2
Distributional approaches to value-based reinforcement learning model the entire distribution of returns, rather than just their expected values, and have recently been shown to yi…