Search Results for author: Alexandre Lacoste

Found 39 papers, 24 papers with code

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies

1 code implementation10 Jan 2024 Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

We develop a nonparametric identifiability theory that formalizes this principle and shows that the latent factors can be recovered by regularizing the learned causal graph to be sparse.


GEO-Bench: Toward Foundation Models for Earth Monitoring

1 code implementation NeurIPS 2023 Alexandre Lacoste, Nils Lehmann, Pau Rodriguez, Evan David Sherwin, Hannah Kerner, Björn Lütjens, Jeremy Andrew Irvin, David Dao, Hamed Alemohammad, Alexandre Drouin, Mehmet Gunturkun, Gabriel Huang, David Vazquez, Dava Newman, Yoshua Bengio, Stefano Ermon, Xiao Xiang Zhu

Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to downstream tasks.

Choreographer: Learning and Adapting Skills in Imagination

1 code implementation23 Nov 2022 Pietro Mazzaglia, Tim Verbelen, Bart Dhoedt, Alexandre Lacoste, Sai Rajeswar

Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment.

Unsupervised Reinforcement Learning

A General Purpose Neural Architecture for Geospatial Systems

no code implementations4 Nov 2022 Nasim Rahaman, Martin Weiss, Frederik Träuble, Francesco Locatello, Alexandre Lacoste, Yoshua Bengio, Chris Pal, Li Erran Li, Bernhard Schölkopf

Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to support a wide variety of important applications.

Disaster Response Earth Observation +2

Mastering the Unsupervised Reinforcement Learning Benchmark from Pixels

1 code implementation24 Sep 2022 Sai Rajeswar, Pietro Mazzaglia, Tim Verbelen, Alexandre Piché, Bart Dhoedt, Aaron Courville, Alexandre Lacoste

In this work, we study the URLB and propose a new method to solve it, using unsupervised model-based RL, for pre-training the agent, and a task-aware fine-tuning strategy combined with a new proposed hybrid planner, Dyna-MPC, to adapt the agent for downstream tasks.

reinforcement-learning Reinforcement Learning (RL) +1

Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark

no code implementations1 Dec 2021 Alexandre Lacoste, Evan David Sherwin, Hannah Kerner, Hamed Alemohammad, Björn Lütjens, Jeremy Irvin, David Dao, Alex Chang, Mehmet Gunturkun, Alexandre Drouin, Pau Rodriguez, David Vazquez

Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to impressive increases in generalisation for downstream tasks.

Typing assumptions improve identification in causal discovery

1 code implementation22 Jul 2021 Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sebastien Lachapelle, Alexandre Drouin

Causal discovery from observational data is a challenging task that can only be solved up to a set of equivalent solutions, called an equivalence class.

Causal Discovery

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA

1 code implementation21 Jul 2021 Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on past latent factors and/or observed auxiliary variables.


Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

1 code implementation14 Jun 2021 Yashas Annadani, Jonas Rothfuss, Alexandre Lacoste, Nino Scherrer, Anirudh Goyal, Yoshua Bengio, Stefan Bauer

However, a crucial aspect to acting intelligently upon the knowledge about causal structure which has been inferred from finite data demands reasoning about its uncertainty.

Bayesian Inference Causal Inference +2

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

2 code implementations ICCV 2021 Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam Laradji, Laurent Charlin, David Vazquez

Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.

Attribute BIG-bench Machine Learning +2

Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations

no code implementations1 Jan 2021 Pau Rodriguez, Massimo Caccia, Alexandre Lacoste, Lee Zamparo, Issam H. Laradji, Laurent Charlin, David Vazquez

In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail.

Attribute counterfactual +1

Differentiable Causal Discovery from Interventional Data

1 code implementation NeurIPS 2020 Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.

Causal Discovery

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

2 code implementations17 Jun 2020 Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste

Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs.

Active Learning

Quantifying the Carbon Emissions of Machine Learning

2 code implementations21 Oct 2019 Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, Thomas Dandres

From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits.

BIG-bench Machine Learning


no code implementations25 Sep 2019 Prudencio Tossou, Basile Dura, Daniel Cohen, Mario Marchand, François Laviolette, Alexandre Lacoste

Due to the significant costs of data generation, many prediction tasks within drug discovery are by nature few-shot regression (FSR) problems, including accurate modelling of biological assays.

Drug Discovery

Stochastic Neural Network with Kronecker Flow

no code implementations10 Jun 2019 Chin-wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to scale to the high-dimensional setting of stochastic neural networks.

Multi-Armed Bandits Thompson Sampling +1

Adaptive Deep Kernel Learning

no code implementations28 May 2019 Prudencio Tossou, Basile Dura, Francois Laviolette, Mario Marchand, Alexandre Lacoste

Deep kernel learning provides an elegant and principled framework for combining the structural properties of deep learning algorithms with the flexibility of kernel methods.

Benchmarking Drug Discovery +2

Hierarchical Importance Weighted Autoencoders

1 code implementation13 May 2019 Chin-wei Huang, Kris Sankaran, Eeshan Dhekane, Alexandre Lacoste, Aaron Courville

We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation.

Variational Inference

On Difficulties of Probability Distillation

no code implementations27 Sep 2018 Chin-wei Huang, Faruk Ahmed, Kundan Kumar, Alexandre Lacoste, Aaron Courville

Probability distillation has recently been of interest to deep learning practitioners as it presents a practical solution for sampling from autoregressive models for deployment in real-time applications.

Improving Explorability in Variational Inference with Annealed Variational Objectives

1 code implementation NeurIPS 2018 Chin-wei Huang, Shawn Tan, Alexandre Lacoste, Aaron Courville

Despite the advances in the representational capacity of approximate distributions for variational inference, the optimization process can still limit the density that is ultimately learned.

Variational Inference

Neural Autoregressive Flows

5 code implementations ICML 2018 Chin-wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville

Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time, via Inverse Autoregressive Flows (IAF).

Density Estimation Speech Synthesis

Deep Prior

no code implementations13 Dec 2017 Alexandre Lacoste, Thomas Boquet, Negar Rostamzadeh, Boris Oreshkin, Wonchang Chung, David Krueger

The recent literature on deep learning offers new tools to learn a rich probability distribution over high dimensional data such as images or sounds.

Hierarchical Question Answering for Long Documents

no code implementations6 Nov 2016 Eunsol Choi, Daniel Hewlett, Alexandre Lacoste, Illia Polosukhin, Jakob Uszkoreit, Jonathan Berant

We present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models.

Question Answering Reading Comprehension +1

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

2 code implementations ACL 2016 Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, David Berthelot

The task contains a rich variety of challenging classification and extraction sub-tasks, making it well-suited for end-to-end models such as deep neural networks (DNNs).

Document Classification General Classification +2

PAC-Bayesian Theory Meets Bayesian Inference

no code implementations NeurIPS 2016 Pascal Germain, Francis Bach, Alexandre Lacoste, Simon Lacoste-Julien

That is, for the negative log-likelihood loss function, we show that the minimization of PAC-Bayesian generalization risk bounds maximizes the Bayesian marginal likelihood.

Bayesian Inference regression

Sequential Model-Based Ensemble Optimization

no code implementations4 Feb 2014 Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand

One of the most tedious tasks in the application of machine learning is model selection, i. e. hyperparameter selection.

Model Selection

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