Portrait of Alexandre Drouin

Alexandre Drouin

Associate Industry Member
Adjunct professor, Université Laval, Department of Electrical Engineering and Computer Engineering
Research Scientist, ServiceNow
Research Topics
Causality
Computational Biology
Deep Learning
LLM Agent
Time Series Forecasting
Website
Google Scholar
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Biography

Alexandre Drouin is a research scientist at ServiceNow Research in Montréal, and an adjunct professor of computer science at Université Laval. He also leads ServiceNow’s Human Decision Support research program, which explores the use of machine learning for decision-making in complex dynamic environments.

Droiun’s main research interest is causal decision-making under uncertainty, where the goal is to answer questions of causal nature (interventions, counterfactual), while accounting for sources of uncertainty, such as ambiguity in causal structures and unmeasured variables. He is also interested in probabilistic time series forecasting and its use in foreseeing the long-term effect of actions. His PhD in computer science from Université Laval was on machine learning algorithms for biomarker discovery in large genomic datasets and their application to the global problem of antibiotic resistance.

Current Students

Arjun Ashok
PhD - Université de Montréal
Principal supervisor :
Irina Rish
Website
Github
Google Scholar
Léo Boisvert
PhD - Polytechnique Montréal
Co-supervisor :
Quentin Cappart
Philippe Brouillard
PhD - Université de Montréal
Principal supervisor :
Dhanya Sridhar
philippebrouillard@gmail.com
Website
Github
Google Scholar

Publications

On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.
Thibaud Godon
Pier-Luc Plante
Jacques Corbeil
Pascal Germain
Alexandre Drouin
Metabolomics, the study of small molecules within biological systems, offers insights into metabolic processes and, consequently, holds grea… (see more)t promise for advancing health outcomes. Biomarker discovery in metabolomics represents a significant challenge, notably due to the high dimensionality of the data. Recent work has addressed this problem by analyzing the most important variables in machine learning models. Unfortunately, this approach relies on prior hypotheses about the structure of the data and may overlook simple patterns. To assess the true usefulness of machine learning methods, we evaluate them on a collection of 835 metabolomics data sets. This effort provides valuable insights for metabolomics researchers regarding where and when to use machine learning. It also establishes a benchmark for the evaluation of future methods. Nonetheless, the results emphasize the high diversity of data sets in metabolomics and the complexity of finding biologically relevant biomarkers. As a result, we propose a novel approach applicable across all data sets, offering guidance for future analyses. This method involves directly comparing univariate and multivariate models. We demonstrate through selected examples how this approach can guide data analysis across diverse data set structures, representative of the observed variability. Code and data are available for research purposes.
2025-06-12
Analytical Chemistry (published)
doi.org
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
Léo Boisvert
Abhay Puri
Gabriel Huang
Mihir Bansal
Chandra Kiran Reddy Evuru
Avinandan Bose
Maryam Fazel
Quentin Cappart
Alexandre Lacoste
Jason Stanley
Alexandre Drouin
Krishnamurthy Dj Dvijotham
We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework… (see more) and integrates easily into realistic agentic frameworks like BrowserGym (for web agents) and
2025-06-08
ICML.cc/2025/Workshop/WCUA (poster)
doi.org
openreview.net
How to Train Your LLM Web Agent: A Statistical Diagnosis
Dheeraj Vattikonda
Santhoshi Ravichandran
Emiliano Penaloza
Hadi Nekoei
Megh Thakkar
Thibault Le Sellier De Chezelles
Nicolas Gontier
Miguel Muñoz-Mármol
Sahar Omidi Shayegan
Stefania Raimondo
Xue (Steve) Liu
Alexandre Drouin
Laurent Charlin
Alexandre Piché
Alexandre Lacoste
Massimo Caccia
Large language model (LLM) agents for web interfaces have advanced rapidly, yet open-source systems still lag behind proprietary agents. Bri… (see more)dging this gap is key to enabling customizable, efficient, and privacy-preserving agents. Two challenges hinder progress: the reproducibility issues in RL and LLM agent training, where results often depend on sensitive factors like seeds and decoding parameters, and the focus of prior work on single-step tasks, overlooking the complexities of web-based, multi-step decision-making. We address these gaps by providing a statistically driven study of training LLM agents for web tasks. Our two-stage pipeline combines imitation learning from a Llama 3.3 70B teacher with on-policy fine-tuning via Group Relative Policy Optimization (GRPO) on a Llama 3.1 8B student. Through 240 configuration sweeps and rigorous bootstrapping, we chart the first compute allocation curve for open-source LLM web agents. Our findings show that dedicating one-third of compute to teacher traces and the rest to RL improves MiniWoB++ success by 6 points and closes 60% of the gap to GPT-4o on WorkArena, while cutting GPU costs by 45%. We introduce a principled hyperparameter sensitivity analysis, offering actionable guidelines for robust and cost-effective agent training.
2025-06-08
ICML.cc/2025/Workshop/WCUA (oral)
openreview.net
openreview.net
Silent Sabotage: Injecting Backdoors into AI Agents Through Fine-Tuning
Léo Boisvert
Abhay Puri
Chandra Kiran Reddy Evuru
Joshua Kazdan
Avinandan Bose
Quentin Cappart
Maryam Fazel
Sai Rajeswar
Jason Stanley
Nicolas Chapados
Alexandre Drouin
Krishnamurthy Dj Dvijotham
The rise of AI agents that can use tools, browse the web and interact with computers on behalf of a user, has sparked strong interest in imp… (see more)roving these capabilities by explicitly fine-tuning the LLMs/VLMs that power these agents. Several researchers have proposed collecting data by letting the agents interact with their environment (e.g., a computer operating system, the web or a collection of APIs exposed as tools), and improve agent performance by fine tuning on this data. In this work, we show that such data collection can be manipulated by adversaries to insert poisoned traces. By modifying just 5% of collected traces, adversaries can embed stealthy bad behaviors into agents—like leaking confidential user information whenever the tool or webpage exposes a trigger. Our results raise important security concerns in the development of AI agents, and underscore the importance of careful scrutiny of all data collection processes used to improve agentic AI.
2025-06-08
ICML.cc/2025/Workshop/WCUA (poster)
openreview.net
openreview.net
Context is Key: A Benchmark for Forecasting with Essential Textual Information
Andrew Robert Williams
Arjun Ashok
Étienne Marcotte
Valentina Zantedeschi
Jithendaraa Subramanian
Roland Riachi
James Requeima
Alexandre Lacoste
Irina Rish
Nicolas Chapados
Alexandre Drouin
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks
Benjamin Leblanc
Mathieu Bazinet
Nathaniel D'Amours
Alexandre Drouin
Pascal Germain
Both PAC-Bayesian and Sample Compress learning frameworks have been shown instrumental for deriving tight (non-vacuous) generalization bound… (see more)s for neural networks. We leverage these results in a meta-learning scheme, relying on a hypernetwork that outputs the parameters of a downstream predictor from a dataset input. The originality of our approach lies in the investigated hypernetwork architectures that encode the dataset before decoding the parameters: (1) a PAC-Bayesian encoder that expresses a posterior distribution over a latent space, (2) a Sample Compress encoder that selects a small sample of the dataset input along with a message from a discrete set, and (3) a hybrid between both approaches motivated by a new Sample Compress theorem handling continuous messages. The latter theorem exploits the pivotal information transiting at the encoder-decoder junction in order to compute generalization guarantees for each downstream predictor obtained by our meta-learning scheme.
2025-05-01
ICML.cc/2025/Conference (poster)
openreview.net
openreview.net
DoomArena: A framework for Testing AI Agents Against Evolving Security Threats
Léo Boisvert
Mihir Bansal
Chandra Kiran Reddy Evuru
Gabriel Huang
Abhay Puri
Avinandan Bose
Maryam Fazel
Quentin Cappart
Jason Stanley
Alexandre Lacoste
Alexandre Drouin
Krishnamurthy Dj Dvijotham
2025-04-18
ArXiv (preprint)
arxiv.org
arxiv.org
Learning to Defer for Causal Discovery with Imperfect Experts
Oscar Clivio
Divyat Mahajan
Perouz Taslakian
Sara Magliacane
Ioannis Mitliagkas
Valentina Zantedeschi
Alexandre Drouin
Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not… (see more) guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existing methods based on soft constraints or inconsistencies in predicted causal relationships fail to account for these variations in expertise. To remedy this, we propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. By adapting learning-to-defer (L2D) algorithms for pairwise causal discovery (CD), we learn a deferral function that selects whether to rely on classical causal discovery methods using numerical data or expert recommendations based on textual meta-data. We evaluate L2D-CD on the canonical Tübingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation. Moreover, our approach identifies domains where the expert's performance is strong or weak. Finally, we outline a strategy for generalizing this approach to causal discovery on graphs with more than two variables, paving the way for further research in this area.
2025-03-05
ICLR.cc/2025/Workshop/LLM_Reason_and_Plan (published)
openreview.net
openreview.net
Learning to Defer for Causal Discovery with Imperfect Experts
Oscar Clivio
Divyat Mahajan
Perouz Taslakian
Sara Magliacane
Ioannis Mitliagkas
Valentina Zantedeschi
Alexandre Drouin
Integrating expert knowledge, e.g. from large language models, into causal discovery algorithms can be challenging when the knowledge is not… (see more) guaranteed to be correct. Expert recommendations may contradict data-driven results, and their reliability can vary significantly depending on the domain or specific query. Existing methods based on soft constraints or inconsistencies in predicted causal relationships fail to account for these variations in expertise. To remedy this, we propose L2D-CD, a method for gauging the correctness of expert recommendations and optimally combining them with data-driven causal discovery results. By adapting learning-to-defer (L2D) algorithms for pairwise causal discovery (CD), we learn a deferral function that selects whether to rely on classical causal discovery methods using numerical data or expert recommendations based on textual meta-data. We evaluate L2D-CD on the canonical Tübingen pairs dataset and demonstrate its superior performance compared to both the causal discovery method and the expert used in isolation. Moreover, our approach identifies domains where the expert's performance is strong or weak. Finally, we outline a strategy for generalizing this approach to causal discovery on graphs with more than two variables, paving the way for further research in this area.
2025-03-05
ICLR.cc/2025/Workshop/LLM_Reason_and_Plan (published)
doi.org
openreview.net
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard
Chandler Squires
Jonas Wahl
Konrad Paul Kording
Karen Sachs
Alexandre Drouin
Dhanya Sridhar
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientifi… (see more)c disciplines. However, its real-world applications remain limited. Current methods often rely on unrealistic assumptions and are evaluated only on simple synthetic toy datasets, often with inadequate evaluation metrics. In this paper, we substantiate these claims by performing a systematic review of the recent causal discovery literature. We present applications in biology, neuroscience, and Earth sciences - fields where causal discovery holds promise for addressing key challenges. We highlight available simulated and real-world datasets from these domains and discuss common assumption violations that have spurred the development of new methods. Our goal is to encourage the community to adopt better evaluation practices by utilizing realistic datasets and more adequate metrics.
2025-01-28
cclear.cc/CLeaR/2025/Conference (poster)
doi.org
openreview.net
InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation
Gaurav Sahu
Abhay Puri
Juan A. Rodriguez
Amirhossein Abaskohi
Mohammad Chegini
Alexandre Drouin
Perouz Taslakian
Valentina Zantedeschi
Alexandre Lacoste
David Vazquez
Nicolas Chapados
Chris Pal
Sai Rajeswar
Issam Hadj Laradji
2025-01-22
ICLR.cc/2025/Conference (poster)
doi.org
openreview.net
The BrowserGym Ecosystem for Web Agent Research
Thibault Le Sellier De Chezelles
Maxime Gasse
Alexandre Lacoste
Massimo Caccia
Alexandre Drouin
Léo Boisvert
Megh Thakkar
Tom Marty
Rim Assouel
Sahar Omidi Shayegan
Lawrence Keunho Jang
Xing Han Lu
Ori Yoran
Dehan Kong
Frank F. Xu
Siva Reddy
Graham Neubig
Quentin Cappart
Russ Salakhutdinov
Nicolas Chapados
The BrowserGym ecosystem addresses the growing need for efficient evaluation and benchmarking of web agents, particularly those leveraging a… (see more)utomation and Large Language Models (LLMs) for web interaction tasks. Many existing benchmarks suffer from fragmentation and inconsistent evaluation methodologies, making it challenging to achieve reliable comparisons and reproducible results. BrowserGym aims to solve this by providing a unified, gym-like environment with well-defined observation and action spaces, facilitating standardized evaluation across diverse benchmarks. Combined with AgentLab, a complementary framework that aids in agent creation, testing, and analysis, BrowserGym offers flexibility for integrating new benchmarks while ensuring consistent evaluation and comprehensive experiment management. This standardized approach seeks to reduce the time and complexity of developing web agents, supporting more reliable comparisons and facilitating in-depth analysis of agent behaviors, and could result in more adaptable, capable agents, ultimately accelerating innovation in LLM-driven automation. As a supporting evidence, we conduct the first large-scale, multi-benchmark web agent experiment and compare the performance of 6 state-of-the-art LLMs across all benchmarks currently available in BrowserGym. Among other findings, our results highlight a large discrepancy between OpenAI and Anthropic's latests models, with Claude-3.5-Sonnet leading the way on almost all benchmarks, except on vision-related tasks where GPT-4o is superior. Despite these advancements, our results emphasize that building robust and efficient web agents remains a significant challenge, due to the inherent complexity of real-world web environments and the limitations of current models.
2025-01-01
Trans. Mach. Learn. Res. (published)
doi.org
openreview.net