b'Gilles Louppe'

research

∙ 02/08/2022

Robust Hybrid Learning With Expert Augmentation

Hybrid modelling reduces the misspecification of expert models by combin...

Antoine Wehenkel, et al. ∙

research

∙ 12/30/2021

SAE: Sequential Anchored Ensembles

Computing the Bayesian posterior of a neural network is a challenging ta...

Arnaud Delaunoy, et al. ∙

research

∙ 11/03/2021

From global to local MDI variable importances for random forests and when they are Shapley values

Random forests have been widely used for their ability to provide so-cal...

Antonio Sutera, et al. ∙

research

∙ 10/01/2021

Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference

In many areas of science, complex phenomena are modeled by stochastic pa...

François Rozet, et al. ∙

research

∙ 09/29/2021

Simulation-based Bayesian inference for multi-fingered robotic grasping

Multi-fingered robotic grasping is an undeniable stepping stone to unive...

Norman Marlier, et al. ∙

research

∙ 07/02/2021

Truncated Marginal Neural Ratio Estimation

Parametric stochastic simulators are ubiquitous in science, often featur...

Benjamin Kurt Miller, et al. ∙

research

∙ 06/29/2021

Diffusion Priors In Variational Autoencoders

Among likelihood-based approaches for deep generative modelling, variati...

Antoine Wehenkel, et al. ∙

research

∙ 06/06/2021

Distributional Reinforcement Learning with Unconstrained Monotonic Neural Networks

The distributional reinforcement learning (RL) approach advocates for re...

Thibaut Théate, et al. ∙

research

∙ 02/12/2021

Leveraging Global Parameters for Flow-based Neural Posterior Estimation

Inferring the parameters of a stochastic model based on experimental obs...

Pedro L. C. Rodrigues, et al. ∙

research

∙ 12/22/2020

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 ...

Pascal Leroy, et al. ∙

research

∙ 11/30/2020

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...

Joeri Hermans, et al. ∙

research

∙ 11/27/2020

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

We present algorithms (a) for nested neural likelihood-to-evidence ratio...

Benjamin Kurt Miller, et al. ∙

research

∙ 11/11/2020

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...

Maxime Vandegar, et al. ∙

research

∙ 10/24/2020

Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization

Gravitational waves from compact binaries measured by the LIGO and Virgo...

Arnaud Delaunoy, et al. ∙

research

∙ 06/03/2020

Graphical Normalizing Flows

Normalizing flows model complex probability distributions by combining a...

Antoine Wehenkel, et al. ∙

research

∙ 06/01/2020

You say Normalizing Flows I see Bayesian Networks

Normalizing flows have emerged as an important family of deep neural net...

Antoine Wehenkel, et al. ∙

research

∙ 11/04/2019

The frontier of simulation-based inference

Many domains of science have developed complex simulations to describe p...

Kyle Cranmer, et al. ∙

research

∙ 09/04/2019

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...

Johann Brehmer, et al. ∙

research

∙ 09/01/2019

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...

Matthia Sabatelli, et al. ∙

research

∙ 08/14/2019

Unconstrained Monotonic Neural Networks

Monotonic neural networks have recently been proposed as a way to define...

Antoine Wehenkel, et al. ∙

research

∙ 07/08/2019

Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale

Probabilistic programming languages (PPLs) are receiving widespread atte...

Atılım Güneş Baydin, et al. ∙

research

∙ 06/04/2019

Effective LHC measurements with matrix elements and machine learning

One major challenge for the legacy measurements at the LHC is that the l...

Johann Brehmer, et al. ∙

research

∙ 03/10/2019

Likelihood-free MCMC with Approximate Likelihood Ratios

We propose a novel approach for posterior sampling with intractable like...

Joeri Hermans, et al. ∙

research

∙ 11/30/2018

Recurrent machines for likelihood-free inference

Likelihood-free inference is concerned with the estimation of the parame...

Arthur Pesah, et al. ∙

research

∙ 09/30/2018

Deep Quality-Value (DQV) Learning

We introduce a novel Deep Reinforcement Learning (DRL) algorithm called ...

Matthia Sabatelli, et al. ∙

research

∙ 08/02/2018

Likelihood-free inference with an improved cross-entropy estimator

We extend recent work (Brehmer, et. al., 2018) that use neural networks ...

Markus Stoye, et al. ∙

research

∙ 07/20/2018

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

We present a novel framework that enables efficient probabilistic infere...

Atilim Gunes Baydin, et al. ∙

research

∙ 07/08/2018

Machine Learning in High Energy Physics Community White Paper

Machine learning is an important research area in particle physics, begi...

Kim Albertsson, et al. ∙

research

∙ 05/30/2018

Mining gold from implicit models to improve likelihood-free inference

Simulators often provide the best description of real-world phenomena; h...

Johann Brehmer, et al. ∙

research

∙ 05/22/2018

Gradient Energy Matching for Distributed Asynchronous Gradient Descent

Distributed asynchronous SGD has become widely used for deep learning in...

Joeri Hermans, et al. ∙

research

∙ 04/30/2018

A Guide to Constraining Effective Field Theories with Machine Learning

We develop, discuss, and compare several inference techniques to constra...

Johann Brehmer, et al. ∙

research

∙ 04/30/2018

Constraining Effective Field Theories with Machine Learning

We present powerful new analysis techniques to constrain effective field...

Johann Brehmer, et al. ∙

research

∙ 12/21/2017

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

We consider the problem of Bayesian inference in the family of probabili...

Mario Lezcano Casado, et al. ∙

research

∙ 09/04/2017

Random Subspace with Trees for Feature Selection Under Memory Constraints

Dealing with datasets of very high dimension is a major challenge in mac...

Antonio Sutera, et al. ∙

research

∙ 07/22/2017

Adversarial Variational Optimization of Non-Differentiable Simulators

Complex computer simulators are increasingly used across fields of scien...

Gilles Louppe, et al. ∙

research

∙ 02/02/2017

QCD-Aware Recursive Neural Networks for Jet Physics

Recent progress in applying machine learning for jet physics has been bu...

Gilles Louppe, et al. ∙

research

∙ 11/03/2016

Learning to Pivot with Adversarial Networks

Several techniques for domain adaptation have been proposed to account f...

Gilles Louppe, et al. ∙

research

∙ 05/20/2016

Visualization of Publication Impact

Measuring scholarly impact has been a topic of much interest in recent y...

Eamonn Maguire, et al. ∙

research

∙ 05/12/2016

Context-dependent feature analysis with random forests

In many cases, feature selection is often more complicated than identify...

Antonio Sutera, et al. ∙

research

∙ 08/31/2015

Ethnicity sensitive author disambiguation using semi-supervised learning

Author name disambiguation in bibliographic databases is the problem of ...

Gilles Louppe, et al. ∙

research

∙ 06/06/2015

Approximating Likelihood Ratios with Calibrated Discriminative Classifiers

In many fields of science, generalized likelihood ratio tests are establ...

Kyle Cranmer, et al. ∙

research

∙ 07/28/2014

Understanding Random Forests: From Theory to Practice

Data analysis and machine learning have become an integrative part of th...

Gilles Louppe, et al. ∙

research

∙ 06/30/2014

Simple connectome inference from partial correlation statistics in calcium imaging

In this work, we propose a simple yet effective solution to the problem ...

Antonio Sutera, et al. ∙

Gilles Louppe

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