Alexandre Lacoste | DeepAI

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Featured Co-authors

Yoshua Bengio
448 publications
Francis Bach
160 publications
Aaron Courville
152 publications
Jonathan Berant
84 publications
Simon Lacoste-Julien
79 publications
Hugo Larochelle
75 publications
Stefan Bauer
68 publications
Xavier Giro-i-Nieto
63 publications
Anirudh Goyal
61 publications
Pascal Vincent
59 publications
David Vazquez
50 publications

∙ 11/23/2022

Choreographer: Learning and Adapting Skills in Imagination

Unsupervised skill learning aims to learn a rich repertoire of behaviors...

0 Pietro Mazzaglia, et al. ∙

∙ 09/24/2022

Unsupervised Model-based Pre-training for Data-efficient Control from Pixels

Controlling artificial agents from visual sensory data is an arduous tas...

0 Sai Rajeswar, et al. ∙

∙ 07/22/2021

Typing assumptions improve identification in causal discovery

Causal discovery from observational data is a challenging task to which ...

10 Philippe Brouillard, et al. ∙

∙ 07/21/2021

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

It can be argued that finding an interpretable low-dimensional represent...

23 Sébastien Lachapelle, et al. ∙

∙ 06/14/2021

Variational Causal Networks: Approximate Bayesian Inference over Causal Structures

Learning the causal structure that underlies data is a crucial step towa...

0 Yashas Annadani, et al. ∙

∙ 04/14/2021

Can Active Learning Preemptively Mitigate Fairness Issues?

Dataset bias is one of the prevailing causes of unfairness in machine le...

13 Frederic Branchaud-Charron, et al. ∙

∙ 03/30/2021

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

Remote sensing and automatic earth monitoring are key to solve global-sc...

4 Oscar Manas, et al. ∙

∙ 03/18/2021

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Explainability for machine learning models has gained considerable atten...

26 Pau Rodríguez, et al. ∙

∙ 11/14/2020

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

Cattle farming is responsible for 8.8% of greenhouse gas emissions world...

4 Issam Laradji, et al. ∙

∙ 09/14/2020

Synbols: Probing Learning Algorithms with Synthetic Datasets

Progress in the field of machine learning has been fueled by the introdu...

58 Alexandre Lacoste, et al. ∙

∙ 07/03/2020

Differentiable Causal Discovery from Interventional Data

Discovering causal relationships in data is a challenging task that invo...

6 Philippe Brouillard, et al. ∙

∙ 06/17/2020

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

Active learning is able to reduce the amount of labelling effort by usin...

1 Parmida Atighehchian, et al. ∙

∙ 03/09/2020

Embedding Propagation: Smoother Manifold for Few-Shot Classification

Few-shot classification is challenging because the data distribution of ...

4 Pau Rodríguez, et al. ∙

∙ 10/21/2019

Quantifying the Carbon Emissions of Machine Learning

From an environmental standpoint, there are a few crucial aspects of tra...

2 Alexandre Lacoste, et al. ∙

∙ 06/10/2019

Stochastic Neural Network with Kronecker Flow

Recent advances in variational inference enable the modelling of highly ...

1 Chin-Wei Huang, et al. ∙

∙ 06/10/2019

Tackling Climate Change with Machine Learning

Climate change is one of the greatest challenges facing humanity, and we...

1 David Rolnick, et al. ∙

∙ 05/28/2019

Adaptive Deep Kernel Learning

Deep kernel learning provides an elegant and principled framework for co...

0 Prudencio Tossou, et al. ∙

∙ 05/13/2019

Hierarchical Importance Weighted Autoencoders

Importance weighted variational inference (Burda et al., 2015) uses mult...

0 Chin-Wei Huang, et al. ∙

∙ 09/06/2018

Improving Explorability in Variational Inference with Annealed Variational Objectives

Despite the advances in the representational capacity of approximate dis...

0 Chin-Wei Huang, et al. ∙

∙ 06/20/2018

Uncertainty in Multitask Transfer Learning

Using variational Bayes neural networks, we develop an algorithm capable...

0 Alexandre Lacoste, et al. ∙

∙ 05/23/2018

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

Few-shot learning has become essential for producing models that general...

0 Boris N. Oreshkin, et al. ∙

∙ 04/03/2018

Neural Autoregressive Flows

Normalizing flows and autoregressive models have been successfully combi...

0 Chin-Wei Huang, et al. ∙

∙ 12/13/2017

Deep Prior

The recent literature on deep learning offers new tools to learn a rich ...

0 Alexandre Lacoste, et al. ∙

∙ 10/13/2017

Bayesian Hypernetworks

We propose Bayesian hypernetworks: a framework for approximate Bayesian ...

0 David Krueger, et al. ∙

∙ 11/06/2016

Hierarchical Question Answering for Long Documents

We present a framework for question answering that can efficiently scale...

0 Eunsol Choi, et al. ∙

∙ 08/11/2016

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

We present WikiReading, a large-scale natural language understanding tas...

0 Daniel Hewlett, et al. ∙

∙ 05/27/2016

PAC-Bayesian Theory Meets Bayesian Inference

We exhibit a strong link between frequentist PAC-Bayesian risk bounds an...

0 Pascal Germain, et al. ∙

∙ 02/04/2014

Sequential Model-Based Ensemble Optimization

One of the most tedious tasks in the application of machine learning is ...

0 Alexandre Lacoste, et al. ∙

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