Kyle Cranmer

The frontier of simulation-based inference

Many domains of science have developed complex simulations to describe p...
11/04/2019 ∙ by Kyle Cranmer, et al. ∙ 91 ∙ share

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Set2Graph: Learning Graphs From Sets

Many problems in machine learning (ML) can be cast as learning functions...
02/20/2020 ∙ by Hadar Serviansky, et al. ∙ 28 ∙ share

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Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

The subtle and unique imprint of dark matter substructure on extended ar...
09/04/2019 ∙ by Johann Brehmer, et al. ∙ 24 ∙ share

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Flows for simultaneous manifold learning and density estimation

We introduce manifold-modeling flows (MFMFs), a new class of generative ...
03/31/2020 ∙ by Johann Brehmer, et al. ∙ 12 ∙ share

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Discovering Symbolic Models from Deep Learning with Inductive Biases

We develop a general approach to distill symbolic representations of a l...
06/19/2020 ∙ by Miles Cranmer, et al. ∙ 12 ∙ share

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Compact Representation of Uncertainty in Hierarchical Clustering

Hierarchical clustering is a fundamental task often used to discover mea...
02/26/2020 ∙ by Craig S. Greenberg, et al. ∙ 7 ∙ share

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Normalizing Flows on Tori and Spheres

Normalizing flows are a powerful tool for building expressive distributi...
02/06/2020 ∙ by Danilo Jimenez Rezende, et al. ∙ 5 ∙ share

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Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

We present a novel framework that enables efficient probabilistic infere...
07/20/2018 ∙ by Atilim Gunes Baydin, et al. ∙ 4 ∙ share

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Effective LHC measurements with matrix elements and machine learning

One major challenge for the legacy measurements at the LHC is that the l...
06/04/2019 ∙ by Johann Brehmer, et al. ∙ 2 ∙ share

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Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...
07/08/2019 ∙ by Atılım Güneş Baydin, et al. ∙ 2 ∙ share

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Adversarial Variational Optimization of Non-Differentiable Simulators

Complex computer simulators are increasingly used across fields of scien...
07/22/2017 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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QCD-Aware Recursive Neural Networks for Jet Physics

Recent progress in applying machine learning for jet physics has been bu...
02/02/2017 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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Learning to Pivot with Adversarial Networks

Several techniques for domain adaptation have been proposed to account f...
11/03/2016 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

In many fields of science, generalized likelihood ratio tests are establ...
06/06/2015 ∙ by Kyle Cranmer, et al. ∙ 0 ∙ share

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Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

We consider the problem of Bayesian inference in the family of probabili...
12/21/2017 ∙ by Mario Lezcano Casado, et al. ∙ 0 ∙ share

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A Guide to Constraining Effective Field Theories with Machine Learning

We develop, discuss, and compare several inference techniques to constra...
04/30/2018 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Constraining Effective Field Theories with Machine Learning

We present powerful new analysis techniques to constrain effective field...
04/30/2018 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Mining gold from implicit models to improve likelihood-free inference

Simulators often provide the best description of real-world phenomena; h...
05/30/2018 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Backdrop: Stochastic Backpropagation

We introduce backdrop, a flexible and simple-to-implement method, intuit...
06/04/2018 ∙ by Siavash Golkar, et al. ∙ 0 ∙ share

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Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...
07/08/2018 ∙ by Kim Albertsson, et al. ∙ 0 ∙ share

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Likelihood-free inference with an improved cross-entropy estimator

We extend recent work (Brehmer, et. al., 2018) that use neural networks ...
08/02/2018 ∙ by Markus Stoye, et al. ∙ 0 ∙ share

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Inferring the quantum density matrix with machine learning

We introduce two methods for estimating the density matrix for a quantum...
04/11/2019 ∙ by Kyle Cranmer, et al. ∙ 0 ∙ share

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MadMiner: Machine learning-based inference for particle physics

The legacy measurements of the LHC will require analyzing high-dimension...
07/24/2019 ∙ by Johann Brehmer, et al. ∙ 0 ∙ share

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Hamiltonian Graph Networks with ODE Integrators

We introduce an approach for imposing physically informed inductive bias...
09/27/2019 ∙ by Alvaro Sanchez-Gonzalez, et al. ∙ 0 ∙ share

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Equivariant flow-based sampling for lattice gauge theory

We define a class of machine-learned flow-based sampling algorithms for ...
03/13/2020 ∙ by Gurtej Kanwar, et al. ∙ 0 ∙ share

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Kyle Cranmer

Featured Co-authors

Kyunghyun Cho

Andrew McCallum

Frank Wood

Jialin Liu

Gilles Louppe

Prabhat

Danilo Jimenez Rezende

Philip Torr

Peter Battaglia

Atılım Güneş Baydin

Yaron Lipman

The frontier of simulation-based inference

Set2Graph: Learning Graphs From Sets

Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Flows for simultaneous manifold learning and density estimation

Discovering Symbolic Models from Deep Learning with Inductive Biases

Compact Representation of Uncertainty in Hierarchical Clustering

Normalizing Flows on Tori and Spheres

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model

Effective LHC measurements with matrix elements and machine learning

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Adversarial Variational Optimization of Non-Differentiable Simulators

QCD-Aware Recursive Neural Networks for Jet Physics

Learning to Pivot with Adversarial Networks

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators

A Guide to Constraining Effective Field Theories with Machine Learning

Constraining Effective Field Theories with Machine Learning

Mining gold from implicit models to improve likelihood-free inference

Backdrop: Stochastic Backpropagation

Machine Learning in High Energy Physics Community White Paper

Likelihood-free inference with an improved cross-entropy estimator

Inferring the quantum density matrix with machine learning

MadMiner: Machine learning-based inference for particle physics

Hamiltonian Graph Networks with ODE Integrators

Equivariant flow-based sampling for lattice gauge theory