We introduce a probability distribution, combined with an efficient samp...
Even the best scientific equipment can only partially observe reality.
R...
We propose a machine-learning approach to model long-term out-of-sample
...
Neural networks have recently gained attention in solving inverse proble...
Meta-learning of numerical algorithms for a given task consist of the
da...
Safe Policy Improvement (SPI) is an important technique for offline
rein...
We construct a reduced, data-driven, parameter dependent effective Stoch...
We introduce a data-driven approach to building reduced dynamical models...
Safe Policy Improvement (SPI) aims at provable guarantees that a learned...
We present a data-driven approach to characterizing nonidentifiability o...
We discuss the correspondence between Gaussian process regression and
Ge...
In this work, we propose a method to learn probability distributions usi...
We study the meta-learning of numerical algorithms for scientific comput...
We extract data-driven, intrinsic spatial coordinates from observations ...
We propose to test, and when possible establish, an equivalence between ...
We propose a deep-learning based method for obtaining standardized data
...
In this paper, we propose a spectral method for deriving functions that ...
A systematic mathematical framework for the study of numerical algorithm...
Different observations of a relation between inputs ("sources") and outp...
The problem of domain adaptation has become central in many applications...
In statistical modeling with Gaussian Process regression, it has been sh...
The document serves as a reference for researchers trying to capture a l...