Gilles Louppe

Orcid: 0000-0002-2082-3106

Affiliations:
  • University of Liège, Belgium


According to our database1, Gilles Louppe authored at least 65 papers between 2011 and 2023.

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Bibliography

2023
Cracking the genetic code with neural networks.
Frontiers Artif. Intell., February, 2023

Robust Hybrid Learning With Expert Augmentation.
Trans. Mach. Learn. Res., 2023

Distributional reinforcement learning with unconstrained monotonic neural networks.
Neurocomputing, 2023

Harnessing machine learning for accurate treatment of overlapping opacity species in GCMs.
CoRR, 2023

Robust Ocean Subgrid-Scale Parameterizations Using Fourier Neural Operators.
CoRR, 2023

Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model.
CoRR, 2023

Policy Gradient Algorithms Implicitly Optimize by Continuation.
CoRR, 2023

Balancing Simulation-based Inference for Conservative Posteriors.
CoRR, 2023

Implicit representation priors meet Riemannian geometry for Bayesian robotic grasping.
CoRR, 2023

Graph-informed simulation-based inference for models of active matter.
CoRR, 2023

Simulation-based Bayesian inference for robotic grasping.
CoRR, 2023

Score-based Data Assimilation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Dynamic NeRFs for Soccer Scenes.
Proceedings of the 6th International Workshop on Multimedia Content Analysis in Sports, 2023

Adaptive Self-Training for Object Detection.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
A Crisis In Simulation-Based Inference? Beware, Your Posterior Approximations Can Be Unfaithful.
Trans. Mach. Learn. Res., 2022

SAE: Sequential Anchored Ensembles.
CoRR, 2022

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Averting A Crisis In Simulation-Based Inference.
CoRR, 2021

Arbitrary Marginal Neural Ratio Estimation for Simulation-based Inference.
CoRR, 2021

Simulation-based Bayesian inference for multi-fingered robotic grasping.
CoRR, 2021

Diffusion Priors In Variational Autoencoders.
CoRR, 2021

Leveraging Global Parameters for Flow-based Neural Posterior Estimation.
CoRR, 2021

From global to local MDI variable importances for random forests and when they are Shapley values.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

HNPE: Leveraging Global Parameters for Neural Posterior Estimation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Truncated Marginal Neural Ratio Estimation.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Graphical Normalizing Flows.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Neural Empirical Bayes: Source Distribution Estimation and its Applications to Simulation-Based Inference.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning.
CoRR, 2020

Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time.
CoRR, 2020

Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization.
CoRR, 2020

You say Normalizing Flows I see Bayesian Networks.
CoRR, 2020

The Deep Quality-Value Family of Deep Reinforcement Learning Algorithms.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

Likelihood-free MCMC with Amortized Approximate Ratio Estimators.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
The frontier of simulation-based inference.
CoRR, 2019

Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms.
CoRR, 2019

Likelihood-free MCMC with Approximate Likelihood Ratios.
CoRR, 2019

Etalumis: bringing probabilistic programming to scientific simulators at scale.
Proceedings of the International Conference for High Performance Computing, 2019

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Unconstrained Monotonic Neural Networks.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

Deep Quality-Value (DQV) Learning.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

Adversarial Variational Optimization of Non-Differentiable Simulators.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

2018
Recurrent machines for likelihood-free inference.
CoRR, 2018

Likelihood-free inference with an improved cross-entropy estimator.
CoRR, 2018

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.
CoRR, 2018

Machine Learning in High Energy Physics Community White Paper.
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CoRR, 2018

Mining gold from implicit models to improve likelihood-free inference.
CoRR, 2018

Gradient Energy Matching for Distributed Asynchronous Gradient Descent.
CoRR, 2018

Random Subspace with Trees for Feature Selection Under Memory Constraints.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.
CoRR, 2017

Adversarial Variational Optimization of Non-Differentiable Simulators.
CoRR, 2017

Learning to Pivot with Adversarial Networks.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
carl: a likelihood-free inference toolbox.
J. Open Source Softw., 2016

Collaborative analysis of multi-gigapixel imaging data using Cytomine.
Bioinform., 2016

Visualization of Publication Impact.
Proceedings of the 18th Eurographics Conference on Visualization, 2016

Context-dependent feature analysis with random forests.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Ethnicity Sensitive Author Disambiguation Using Semi-supervised Learning.
Proceedings of the Knowledge Engineering and Semantic Web - 7th International Conference, 2016

2015
Scikit-learn: Machine Learning Without Learning the Machinery.
GetMobile Mob. Comput. Commun., 2015

2014
Understanding Random Forests: From Theory to Practice.
PhD thesis, 2014

A hybrid human-computer approach for large-scale image-based measurements using web services and machine learning.
Proceedings of the IEEE 11th International Symposium on Biomedical Imaging, 2014

Simple Connectome Inference from Partial Correlation Statistics in Calcium Imaging.
Proceedings of the Neural Connectomics Workshop at ECML 2014, 2014

2013
API design for machine learning software: experiences from the scikit-learn project.
CoRR, 2013

Understanding variable importances in forests of randomized trees.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

2012
Ensembles on Random Patches.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

2011
Learning to rank with extremely randomized trees.
Proceedings of the Yahoo! Learning to Rank Challenge, 2011


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