Xinzhu Liang

Handbook of Bayesian Deep Learning

Claudio Agostinelli, Laurence Aitchison, Emanuel Aldea, Richard Allmendinger et autres

International audience

it, gb, fr, us, hk, de, ch, sa, dk, no, cn, nl, ca, au (code pays fourni par la source)

Handbook of Bayesian Deep Learning

Claudio Agostinelli, Laurence Aitchison, Emanuel Aldea, Richard Allmendinger et autres

it, gb, fr, us, hk, de, ch, sa, dk, no, cn, nl, ca, au (code pays fourni par la source)

Learning the subspace of variation for global optimization of functions with low effective dimension

Coralia Cartis, Xinzhu Liang, Estelle Massart, Adilet Otemissov

We propose an algorithmic framework, that employs active subspace techniques, for scalable global optimization of functions with low effective dimension (also referred to as low-rank functions). This proposal replaces the original high-dimensional problem by one or several lower-dimensional reduced subproblem(s), capturing the …

A randomized multi-index sequential Monte Carlo method

Xinzhu Liang, Shang‐Da Yang, Simon L. Cotter, Kody J. H. Law

Abstract We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. Under such an assumption, this work builds upon a recently …

gb (code pays fourni par la source)

A randomized Multi-index sequential Monte Carlo method

Simon L. Cotter, Kody J. H. Law, Xinzhu Liang, Shang‐Da Yang

Abstract We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. Under such an assumption, this work builds upon a recently …

gb (code pays fourni par la source)

A randomized multi-index sequential Monte Carlo method

Xinzhu Liang, Shang‐Da Yang, Simon L. Cotter, Kody J. H. Law

We consider the problem of estimating expectations with respect to a target distribution with an unknown normalizing constant, and where even the unnormalized target needs to be approximated at finite resolution. Under such an assumption, this work builds upon a recently introduced …