We consider extensions of the Newton-MR algorithm for nonconvex optimiza...
The problem of model selection is considered for the setting of interpol...
The quality of many modern machine learning models improves as model
com...
A variety of dimensionality reduction techniques have been applied for
c...
Model-free reinforcement learning (RL) has been an active area of resear...
We introduce stochastic normalizing flows, an extension of continuous
no...
Analysing and computing with Gaussian processes arising from infinitely ...
Stein importance sampling is a widely applicable technique based on
kern...
It is well-known that the distribution over functions induced through a
...
We apply methods from randomized numerical linear algebra (RandNLA) to
d...
Graph embeddings, a class of dimensionality reduction techniques designe...
For sampling from a log-concave density, we study implicit integrators
r...
For optimization of a sum of functions in a distributed computing
enviro...
Establishing global convergence of the classical Newton's method has lon...