44 citations · 54 across the 5 of their papers we have counts for
10 papers
Non-PSD Matrix Sketching with Applications to Regression and Optimization
Zhili Feng, Fred Roosta, David P. Woodruff
A variety of dimensionality reduction techniques have been applied for computations involving large matrices. The underlying matrix is randomly compressed into a smaller one, while…
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…
Richer priors for infinitely wide multi-layer perceptrons
Russell Tsuchida, Fred Roosta, Marcus Gallagher
It is well-known that the distribution over functions induced through a zero-mean iid prior distribution over the parameters of a multi-layer perceptron (MLP) converges to a Gaussi…