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Typing assumptions improve identification in causal discovery

Jul 22, 2021
Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sebastien Lachapelle, Alexandre Drouin

* Accepted for presentation as a contributed talk at the Workshop on the Neglected Assumptions in Causal Inference (NACI) at the 38th International Conference on Machine Learning, 2021 

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Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA

Jul 21, 2021
SĂ©bastien Lachapelle, Pau RodrĂ­guez LĂłpez, RĂ©mi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

* Appears in: Workshop on the Neglected Assumptions in Causal Inference (NACI) at the 38 th International Conference on Machine Learning, 2021. 19 pages 

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Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Jun 14, 2021
Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

* 10 pages, 6 figures 

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Can Active Learning Preemptively Mitigate Fairness Issues?

Apr 14, 2021
Frédéric Branchaud-Charron, Parmida Atighehchian, Pau Rodríguez, Grace Abuhamad, Alexandre Lacoste

* Presented at ICLR 2021 Workshop on Responsable AI 

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Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Mar 30, 2021
Oscar Mañas, Alexandre Lacoste, Xavier Giro-i-Nieto, David Vazquez, Pau Rodriguez

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Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Mar 18, 2021
Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin, David Vazquez

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Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery

Nov 14, 2020
Issam Laradji, Pau Rodriguez, Freddie Kalaitzis, David Vazquez, Ross Young, Ed Davey, Alexandre Lacoste

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Synbols: Probing Learning Algorithms with Synthetic Datasets

Sep 14, 2020
Alexandre Lacoste, Pau Rodríguez, Frédéric Branchaud-Charron, Parmida Atighehchian, Massimo Caccia, Issam Laradji, Alexandre Drouin, Matt Craddock, Laurent Charlin, David Vázquez

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Differentiable Causal Discovery from Interventional Data

Jul 03, 2020
Philippe Brouillard, SĂ©bastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

* 34 pages 

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Bayesian active learning for production, a systematic study and a reusable library

Jun 17, 2020
Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste

* 10 pages, 6 figures 

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Embedding Propagation: Smoother Manifold for Few-Shot Classification

Mar 09, 2020
Pau RodrĂ­guez, Issam Laradji, Alexandre Drouin, Alexandre Lacoste

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Quantifying the Carbon Emissions of Machine Learning

Nov 04, 2019
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres

* Machine Learning Emissions Calculator: 

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Stochastic Neural Network with Kronecker Flow

Jun 10, 2019
Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

* 18 pages 

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Tackling Climate Change with Machine Learning

Jun 10, 2019
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio

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Adaptive Deep Kernel Learning

May 28, 2019
Prudencio Tossou, Basile Dura, Francois Laviolette, Mario Marchand, Alexandre Lacoste

* Submitted at NeurIPS 2019 

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Hierarchical Importance Weighted Autoencoders

May 13, 2019
Chin-Wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville

* Accepted by ICML 2019. 17 pages 

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TADAM: Task dependent adaptive metric for improved few-shot learning

Nov 06, 2018
Boris N. Oreshkin, Pau Rodriguez, Alexandre Lacoste

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Improving Explorability in Variational Inference with Annealed Variational Objectives

Oct 26, 2018
Chin-Wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville

* To appear in NIPS 2018 

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Uncertainty in Multitask Transfer Learning

Jul 06, 2018
Alexandre Lacoste, Boris Oreshkin, Wonchang Chung, Thomas Boquet, Negar Rostamzadeh, David Krueger

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Bayesian Hypernetworks

Apr 24, 2018
David Krueger, Chin-Wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron Courville

* David Krueger and Chin-Wei Huang contributed equally 

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Neural Autoregressive Flows

Apr 03, 2018
Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

* 16 pages, 10 figures, 3 tables 

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Deep Prior

Dec 16, 2017
Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris Oreshkin, Wonchang Chung, David Krueger

* Workshop paper, Accepted at Bayesian Deep Learning workshop, NIPS 2017 

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WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

Mar 15, 2017
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, David Berthelot

* Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016, pp. 1535-1545 

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PAC-Bayesian Theory Meets Bayesian Inference

Feb 13, 2017
Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien

* Advances in Neural Information Processing Systems 29 (NIPS 2016), p. 1884-1892 
* Published at NIPS 2015 (

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Hierarchical Question Answering for Long Documents

Feb 08, 2017
Eunsol Choi, Daniel Hewlett, Alexandre Lacoste, Illia Polosukhin, Jakob Uszkoreit, Jonathan Berant

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Sequential Model-Based Ensemble Optimization

Feb 04, 2014
Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand

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