Gilles Louppe

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NYU college ∙ CERN ∙ University of Liège

Associate Professor in artificial intelligence and deep learning at the University of Liège

QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

This paper introduces four new algorithms that can be used for tackling ...
12/22/2020 ∙ by Pascal Leroy, et al. ∙ 8 ∙ share

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Towards constraining warm dark matter with stellar streams through neural simulation-based inference

A statistical analysis of the observed perturbations in the density of s...
11/30/2020 ∙ by Joeri Hermans, et al. ∙ 5 ∙ share

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Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time

We present algorithms (a) for nested neural likelihood-to-evidence ratio...
11/27/2020 ∙ by Benjamin Kurt Miller, et al. ∙ 9 ∙ share

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Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference

We revisit empirical Bayes in the absence of a tractable likelihood func...
11/11/2020 ∙ by Maxime Vandegar, et al. ∙ 11 ∙ share

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Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization

Gravitational waves from compact binaries measured by the LIGO and Virgo...
10/24/2020 ∙ by Arnaud Delaunoy, et al. ∙ 11 ∙ share

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Graphical Normalizing Flows

Normalizing flows model complex probability distributions by combining a...
06/03/2020 ∙ by Antoine Wehenkel, et al. ∙ 37 ∙ share

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You say Normalizing Flows I see Bayesian Networks

Normalizing flows have emerged as an important family of deep neural net...
06/01/2020 ∙ by Antoine Wehenkel, et al. ∙ 33 ∙ share

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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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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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Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms

This paper makes one step forward towards characterizing a new family of...
09/01/2019 ∙ by Matthia Sabatelli, et al. ∙ 24 ∙ share

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Unconstrained Monotonic Neural Networks

Monotonic neural networks have recently been proposed as a way to define...
08/14/2019 ∙ by Antoine Wehenkel, et al. ∙ 1 ∙ 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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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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Likelihood-free MCMC with Approximate Likelihood Ratios

We propose a novel approach for posterior sampling with intractable like...
03/10/2019 ∙ by Joeri Hermans, et al. ∙ 10 ∙ share

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Recurrent machines for likelihood-free inference

Likelihood-free inference is concerned with the estimation of the parame...
11/30/2018 ∙ by Arthur Pesah, et al. ∙ 7 ∙ share

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Deep Quality-Value (DQV) Learning

We introduce a novel Deep Reinforcement Learning (DRL) algorithm called ...
09/30/2018 ∙ by Matthia Sabatelli, 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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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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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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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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Gradient Energy Matching for Distributed Asynchronous Gradient Descent

Distributed asynchronous SGD has become widely used for deep learning in...
05/22/2018 ∙ by Joeri Hermans, 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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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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Random Subspace with Trees for Feature Selection Under Memory Constraints

Dealing with datasets of very high dimension is a major challenge in mac...
09/04/2017 ∙ by Antonio Sutera, et al. ∙ 0 ∙ 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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Visualization of Publication Impact

Measuring scholarly impact has been a topic of much interest in recent y...
05/20/2016 ∙ by Eamonn Maguire, et al. ∙ 0 ∙ share

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Context-dependent feature analysis with random forests

In many cases, feature selection is often more complicated than identify...
05/12/2016 ∙ by Antonio Sutera, et al. ∙ 0 ∙ share

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Ethnicity sensitive author disambiguation using semi-supervised learning

Author name disambiguation in bibliographic databases is the problem of ...
08/31/2015 ∙ 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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Understanding Random Forests: From Theory to Practice

Data analysis and machine learning have become an integrative part of th...
07/28/2014 ∙ by Gilles Louppe, et al. ∙ 0 ∙ share

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Simple connectome inference from partial correlation statistics in calcium imaging

In this work, we propose a simple yet effective solution to the problem ...
06/30/2014 ∙ by Antonio Sutera, et al. ∙ 0 ∙ share

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Gilles Louppe

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