Alexandre Drouin | DeepAI

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∙ 07/11/2023

Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation

The practical utility of causality in decision-making is widespread and ...

0 Chris Chinenye Emezue, et al. ∙

∙ 07/05/2023

Causal Discovery with Language Models as Imperfect Experts

Understanding the causal relationships that underlie a system is a funda...

0 Stephanie Long, et al. ∙

∙ 06/07/2023

Invariant Causal Set Covering Machines

Rule-based models, such as decision trees, appeal to practitioners due t...

7 Thibaud Godon, et al. ∙

∙ 04/19/2023

Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts

Multivariate probabilistic time series forecasts are commonly evaluated ...

9 Étienne Marcotte, et al. ∙

∙ 08/11/2022

RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data

Background: Understanding the relationship between the Omics and the phe...

2 Thibaud Godon, et al. ∙

∙ 02/07/2022

TACTiS: Transformer-Attentional Copulas for Time Series

The estimation of time-varying quantities is a fundamental component of ...

6 Alexandre Drouin, 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. ∙

∙ 06/24/2021

byteSteady: Fast Classification Using Byte-Level n-Gram Embeddings

This article introduces byteSteady – a fast model for classification usi...

3 Xiang Zhang, et al. ∙

∙ 10/22/2020

In Search of Robust Measures of Generalization

One of the principal scientific challenges in deep learning is explainin...

9 Gintare Karolina Dziugaite, 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. ∙

∙ 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. ∙

∙ 01/10/2018

Deep Learning for Electromyographic Hand Gesture Signal Classification by Leveraging Transfer Learning

In recent years, the use of deep learning algorithms has become increasi...

0 Ulysse Côté-Allard, et al. ∙

∙ 10/11/2017

Maximum Margin Interval Trees

Learning a regression function using censored or interval-valued output ...

0 Alexandre Drouin, et al. ∙

∙ 05/22/2015

Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance

The Set Covering Machine (SCM) is a greedy learning algorithm that produ...

0 Alexandre Drouin, et al. ∙

∙ 12/02/2014

Learning interpretable models of phenotypes from whole genome sequences with the Set Covering Machine

The increased affordability of whole genome sequencing has motivated its...

0 Alexandre Drouin, et al. ∙

∙ 07/31/2012

Learning a peptide-protein binding affinity predictor with kernel ridge regression

We propose a specialized string kernel for small bio-molecules, peptides...

0 Sébastien Giguère, et al. ∙

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