Portrait de Alexandre Drouin

Alexandre Drouin

Membre industriel associé
Professeur adjoint, Université Laval, Département de génie électrique et de génie informatique
Chercheur scientifique, ServiceNow
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Biographie

Alexandre Drouin est chercheur en intelligence artificielle chez ServiceNow Research à Montréal et professeur associé au Département d’informatique et de génie logiciel de l’Université Laval. Il dirige une équipe de recherche qui explore l’utilisation de l’apprentissage automatique pour la prise de décision dans des environnements dynamiques complexes. Son intérêt de recherche principal est la prise de décision causale, dont le but est de répondre à des questions interventionnelles et contrefactuelles en tenant compte des sources d’incertitude potentielles, par exemple l’ambiguïté des relations causales sous-jacentes à un système et l’effet de variables latentes. Il s’intéresse aussi aux modèles de prédiction probabiliste pour les séries temporelles et à leur utilisation pour prédire l’effet à long terme d’actions.

Il est détenteur d’un doctorat en informatique de l’Université Laval, qu’il a reçu pour son travail sur le développement d’algorithmes d’apprentissage automatique pour la découverte de biomarqueurs en génomique et leur application au problème de résistance aux antibiotiques.

Étudiants actuels

Arjun Ashok
Doctorat
Superviseur⋅e principal⋅e :
Irina Rish
arjun.ashok@mila.quebec
Site web
Github
Google Scholar
Navita Goyal
Stagiaire de recherche - Université de Montréal
Superviseur⋅e principal⋅e :
Dhanya Sridhar
navita.goyal@mila.quebec
Site web
Github
Google Scholar
Philippe Brouillard
Doctorat - Université de Montréal
Superviseur⋅e principal⋅e :
Dhanya Sridhar
brouilph@mila.quebec
Site web
Github
Google Scholar
Thibaud Godon
Doctorat - Université Laval
Superviseur⋅e principal⋅e :
Pascal Germain
thibaud.godon@mila.quebec
Github
Google Scholar

Publications

WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
2024-05-01
ICML.cc/2024/Conference (poster)
doi.org
openreview.net
Evaluating Interventional Reasoning Capabilities of Large Language Models
Tejas Kasetty
Divyat Mahajan
Gintare Karolina Dziugaite
Alexandre Drouin
Dhanya Sridhar
Numerous decision-making tasks require estimating causal effects under interventions on different parts of a system. As practitioners consid… (voir plus)er using large language models (LLMs) to automate decisions, studying their causal reasoning capabilities becomes crucial. A recent line of work evaluates LLMs ability to retrieve commonsense causal facts, but these evaluations do not sufficiently assess how LLMs reason about interventions. Motivated by the role that interventions play in causal inference, in this paper, we conduct empirical analyses to evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention. We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types, and enable a study of intervention-based reasoning. These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts. Our analysis on four LLMs highlights that while GPT- 4 models show promising accuracy at predicting the intervention effects, they remain sensitive to distracting factors in the prompts.
2024-04-08
ArXiv (prépublication)
doi.org
arxiv.org
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
2024-03-12
ArXiv (prépublication)
doi.org
arxiv.org
WorkArena: How Capable are Web Agents at Solving Common Knowledge Work Tasks?
Alexandre Drouin
Maxime Gasse
Massimo Caccia
Issam Hadj Laradji
Manuel Del Verme
Tom Marty
Léo Boisvert
Megh Thakkar
Quentin Cappart
David Vazquez
Nicolas Chapados
Alexandre Lacoste
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuri… (voir plus)ng the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterprise software systems. To this end, we propose WorkArena, a remote-hosted benchmark of 29 tasks based on the widely-used ServiceNow platform. We also introduce BrowserGym, an environment for the design and evaluation of such agents, offering a rich set of actions as well as multimodal observations. Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation. Notably, our analysis uncovers a significant performance disparity between open and closed-source LLMs, highlighting a critical area for future exploration and development in the field.
2024-03-11
ICLR.cc/2024/Workshop/LLMAgents (poster)
doi.org
openreview.net
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Nicolas Chapados
Alexandre Drouin
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (voir plus)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Capture the Flag: Uncovering Data Insights with Large Language Models
Issam Hadj Laradji
Perouz Taslakian
Sai Rajeswar
Valentina Zantedeschi
Alexandre Lacoste
Nicolas Chapados
David Vazquez
Chris Pal
Alexandre Drouin
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making. Howev… (voir plus)er, accomplishing this task requires considerable technical skills, domain expertise, and human labor. This study explores the potential of using Large Language Models (LLMs) to automate the discovery of insights in data, leveraging recent advances in reasoning and code generation techniques. We propose a new evaluation methodology based on a"capture the flag"principle, measuring the ability of such models to recognize meaningful and pertinent information (flags) in a dataset. We further propose two proof-of-concept agents, with different inner workings, and compare their ability to capture such flags in a real-world sales dataset. While the work reported here is preliminary, our results are sufficiently interesting to mandate future exploration by the community.
2023-12-21
ArXiv (prépublication)
doi.org
arxiv.org
Capture the Flag: Uncovering Data Insights with Large Language Models
Issam Hadj Laradji
Perouz Taslakian
Sai Rajeswar
Valentina Zantedeschi
Alexandre Lacoste
Nicolas Chapados
David Vazquez
Chris Pal
Alexandre Drouin
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (publié)
doi.org
openreview.net
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Sai Rajeswar
Hector Palacios
Quentin Cappart
David Vazquez
Nicolas Chapados
Maxime Gasse
Alexandre Lacoste
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (publié)
openreview.net
openreview.net
Lag-Llama: Towards Foundation Models for Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Arian Khorasani
George Adamopoulos
Rishika Bhagwatkar
Marin Biloš
Hena Ghonia
Nadhir Hassen
Anderson Schneider
Sahil Garg
Alexandre Drouin
Nicolas Chapados
Yuriy Nevmyvaka
Irina Rish
Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (voir plus)Llama, a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen "out-of-distribution" time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws to fit and predict model scaling behavior. The open source code is made available at https://github.com/kashif/pytorch-transformer-ts.
2023-11-01
NeurIPS.cc/2023/Workshop/R0-FoMo (poster)
doi.org
openreview.net
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Arian Khorasani
George Adamopoulos
Rishika Bhagwatkar
Marin Bilovs
Hena Ghonia
Nadhir Hassen
Anderson Schneider
Sahil Garg
Alexandre Drouin
Nicolas Chapados
Yuriy Nevmyvaka
Irina Rish
2023-10-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Kashif Rasul
Arjun Ashok
Andrew Robert Williams
Arian Khorasani
George Adamopoulos
Rishika Bhagwatkar
Marin Bilovs
Hena Ghonia
N. Hassen
Anderson Schneider
Sahil Garg
Alexandre Drouin
Nicolas Chapados
Yuriy Nevmyvaka
Irina Rish
Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.
2023-10-12
ArXiv (prépublication)
arxiv.org
arxiv.org
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Nicolas Chapados
Alexandre Drouin
We introduce a new model for multivariate probabilistic time series prediction, designed to flexibly address a range of tasks including fore… (voir plus)casting, interpolation, and their combinations. Building on copula theory, we propose a simplified objective for the recently-introduced transformer-based attentional copulas (TACTiS), wherein the number of distributional parameters now scales linearly with the number of variables instead of factorially. The new objective requires the introduction of a training curriculum, which goes hand-in-hand with necessary changes to the original architecture. We show that the resulting model has significantly better training dynamics and achieves state-of-the-art performance across diverse real-world forecasting tasks, while maintaining the flexibility of prior work, such as seamless handling of unaligned and unevenly-sampled time series. Code is made available at https://github.com/ServiceNow/TACTiS.
2023-10-02
ArXiv (prépublication)
doi.org
arxiv.org