Gilles Louppe

scikit-learn: machine learning in Python

Lectures for INFO8010 Deep Learning, ULiège

Lectures for INFO8006 Introduction to Artificial Intelligence, ULiège

Materials for DATS0001 Foundations of Data Science, ULiège

The story of an AI agent running inside a container. :book:

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Repository of my thesis "Understanding Random Forests"

Scikit-Learn tutorials

Lectures for INFO8004 Advanced Machine Learning, ULiège

Lectures for INFO8002 - Large-scale Data Systems, ULiège

Talk on "Tree models with Scikit-Learn: Great learners with little assumptions" presented at PyPata Paris 2015

Repository for the code of "QCD-Aware Recursive Neural Networks for Jet Physics"

Repository for the paper "Learning to Pivot with Adversarial Networks"

Template for talks in remark+KaTeX.

Talk on "Bayesian optimisation", beginner level

Repository for the paper "Ethnicity sensitive author disambiguation using semi-supervised learning"

Random fiddling stored in notebooks

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Code for the Kaggle Marinexplore challenge

Repository for the paper "Adversarial Variational Optimization of Non-Differentiable Simulators"

Sequential model-based optimization with a `scipy.optimize` interface

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Tutorial "An introduction to Machine Learning with Scikit-Learn", presented at CERN

Bibliographic Entity Automatic Recognition and Disambiguation

Materials for PROJ0016 - Big data project

An experimental AI system that can autonomously fix and improve code

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Code for the Kaggle Solar Energy Prediction challenge

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Talk on "Learning to Pivot with Adversarial Networks"

Machine learning lectures given as part of ICCUB 2016 http://icc.ub.edu/congress/ICCUB_DM_SCHOOL

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Repository for the paper "Understanding variable importances in forests of randomized trees"

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Curriculum vitae

Website of the Machine Learning and the Physical Sciences workshop at NeurIPS conference

Talk on "Machine Learning for Author Disambiguation" presented at Inspire weekly meeting

Code for the Kaggle Higgs Boson challenge

IPython notebook for PyData SF 2014 tutorial: "Gradient Boosted Regression Trees in scikit-learn"

Talk on "Scikit-Learn in Particle Physics" presented at Telecom ParisTech

Source of http://glouppe.github.io

Talk on "Teaching machines to discover particles"

Scikit-learn tutorials for the Scipy 2013 conference

Talk on "Accelerating Random Forests in Scikit-Learn" presented at EuroScipy 2014

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Simulation-based inference in PyTorch

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Manuscript and code for the paper "Gradient Energy Matching for Distributed Asynchronous Gradient Descent".

Talk on "QCD-aware recursive neural networks for jet physics"

Talk on "Pitfalls of evaluating a classifier's performance in high energy physics applications" presented at the ALEPH workshop, NIPS, December 2015

Code for the Kaggle Connectomics challenge

Talk on "Classification with a control channel" presented at CERN, October 2015

Talk on "Scikit-Learn: ML in the Python ecosystem" presented at the NIPS 2013 MLOSS workshop

Repository for the paper "Simple connectome inference from partial correlation statistics in calcium imaging"

Source of http://glouppe.github.io

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🌍 A Collection of Awesome Large Weather Models (LWMs) | AI for Earth (AI4Earth) | AI for Science (AI4Science)

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Likelihood-free AMortized Posterior Estimation with PyTorch

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Probing the nature of dark matter by inferring the dark matter particle mass with machine learning and stellar streams.

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Talk on "Mining gold from implicit models to improve likelihood-free inference"

Materials + Stuff for Astro Hack Week 2018

Paper by scikit-learn contributers for ECML/PKDD 2013 conference

Code repository for the generalized Galton board example in the paper "Mining gold from implicit models to improve likelihood-free inference"

Code repository for the paper "Constraining Effective Field Theories with Machine Learning"

Coming up soon....

Code for Mondrian Forests (for classification and regression)

Talk on "Approximating Likelihood ratios with Calibrated Classifiers"

Repository for the paper "Approximating Likelihood ratios with Calibrated Classifiers"

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on Theano or TensorFlow.

Efficiently computes derivatives of numpy code.

Collaborative work on scikit-learn SigMobile entry

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

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Likelihood-free inference toolbox.