44 citations · 153 across the 39 of their papers we have counts for
5 papers · 1 filter
Average-reward model-free reinforcement learning: a systematic review and literature mapping
Vektor Dewanto, George Dunn, Ali Eshragh +2
Reinforcement learning is important part of artificial intelligence. In this paper, we review model-free reinforcement learning that utilizes the average reward optimality criterio…
DINO: Distributed Newton-Type Optimization Method
Rixon Crane, Fred Roosta
We present a novel communication-efficient Newton-type algorithm for finite-sum optimization over a distributed computing environment. Our method, named DINO, overcomes both theore…
Stochastic Normalizing Flows
Liam Hodgkinson, Chris van der Heide, Fred Roosta +1
We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differenti…
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
Russell Tsuchida, Tim Pearce, Chris van der Heide +2
Analysing and computing with Gaussian processes arising from infinitely wide neural networks has recently seen a resurgence in popularity. Despite this, many explicit covariance fu…
The reproducing Stein kernel approach for post-hoc corrected sampling
Liam Hodgkinson, Robert Salomone, Fred Roosta
Stein importance sampling is a widely applicable technique based on kernelized Stein discrepancy, which corrects the output of approximate sampling algorithms by reweighting the em…