Fred Roosta

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The University of Queensland

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Average-reward model-free reinforcement learning: a systematic review and literature mapping

Model-free reinforcement learning (RL) has been an active area of resear...
10/18/2020 ∙ by Vektor Dewanto, et al. ∙ 14 ∙ share

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Stochastic Normalizing Flows

We introduce stochastic normalizing flows, an extension of continuous no...
02/21/2020 ∙ by Liam Hodgkinson, et al. ∙ 0 ∙ share

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Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks

Analysing and computing with Gaussian processes arising from infinitely ...
02/20/2020 ∙ by Russell Tsuchida, et al. ∙ 47 ∙ share

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The reproducing Stein kernel approach for post-hoc corrected sampling

Stein importance sampling is a widely applicable technique based on kern...
01/25/2020 ∙ by Liam Hodgkinson, et al. ∙ 0 ∙ share

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Richer priors for infinitely wide multi-layer perceptrons

It is well-known that the distribution over functions induced through a ...
11/29/2019 ∙ by Russell Tsuchida, et al. ∙ 2 ∙ share

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LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data

We apply methods from randomized numerical linear algebra (RandNLA) to d...
11/27/2019 ∙ by Ali Eshragh, et al. ∙ 0 ∙ share

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Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings

Graph embeddings, a class of dimensionality reduction techniques designe...
09/29/2019 ∙ by Keith Levin, et al. ∙ 0 ∙ share

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Implicit Langevin Algorithms for Sampling From Log-concave Densities

For sampling from a log-concave density, we study implicit integrators r...
03/29/2019 ∙ by Liam Hodgkinson, et al. ∙ 0 ∙ share

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DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization

For optimization of a sum of functions in a distributed computing enviro...
01/16/2019 ∙ by Rixon Crane, et al. ∙ 0 ∙ share

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Newton-MR: Newton's Method Without Smoothness or Convexity

Establishing global convergence of the classical Newton's method has lon...
09/30/2018 ∙ by Fred Roosta, et al. ∙ 0 ∙ share

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