JP

Juan Pavez

0000-0002-7205-0053

physics.data-anstat.MLhep-phcs.LGcs.CLhep-eximplicit modelsneural density estimationsimulation-based inferencestat.AP

On Valency

published · living versions

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

with Kyle Cranmer, Gilles Louppe

1 version

W_tpg3tch3v1 · current

Preprints & journals

7 papers in the corpus · 2015–2019

Mining gold from implicit models to improve likelihood-free inference.

Brehmer, Johann, Louppe, Gilles, Pavez, Juan et al.

159 citations

320797252020Proceedings of the National Academy of Sciences of the United States of America. 2020;117(10):5242-5249

Effective LHC measurements with matrix elements and machine learning

Johann Brehmer, Kyle Cranmer, Irina Espejo et al.

24 citations

1906.01578v12019arXiv

Constraining Effective Field Theories with Machine Learning.

Brehmer, Johann, Cranmer, Kyle, Louppe, Gilles et al.

160 citations

302651252018Physical review letters. 2018;121(11):111801

Likelihood-free inference with an improved cross-entropy estimator

Markus Stoye, Johann Brehmer, Gilles Louppe et al.

32 citations

1808.00973v12018arXiv

Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module

Juan Pavez, H'ector Allende, H'ector Allende-Cid

20 citations

1805.09354v12018arXiv

A Guide to Constraining Effective Field Theories with Machine Learning

Johann Brehmer, Kyle Cranmer, Gilles Louppe et al.

146 citations

1805.00020v42018Phys. Rev. D 98, 052004 (2018)

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Kyle Cranmer, Juan Pavez, Gilles Louppe

134 citations

1506.02169v22015arXiv

Career total: 17 works. 7 are in this corpus.

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