Guillaume Rabusseau

Biography

I have been an assistant professor at Mila – Quebec Artificial Intelligence Institute and in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal (UdeM) since September 2018. I was awarded a Canada CIFAR AI Chair in March 2019. Before joining UdeM, I was a postdoctoral research fellow in the Reasoning and Learning Lab at McGill University, where I worked with Prakash Panangaden, Joelle Pineau and Doina Precup.

I obtained my PhD in 2016 from Aix-Marseille University (AMU) in France, where I worked in the Qarma team (Machine Learning and Multimedia) under the supervision of François Denis and Hachem Kadri. I also obtained my MSc in fundamental computer science and my BSc in computer science from AMU. I am interested in tensor methods for machine learning and in designing learning algorithms for structured data by leveraging linear and multilinear algebra (e.g., spectral methods).

Current Students

Kareem Ahmed

Postdoctorate - McGill University

Co-supervisor :

Siva Reddy

kareem.yousrii@gmail.com

Jun Dai

Postdoctorate - Université de Montréal

jun.dai@mila.quebec

alireza.dizaji@mila.quebec

Alireza Dizaji

Collaborating Alumni - Université de Montréal

marawan.gamal@mila.quebec

Marawan Gamal

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Jian Tang

farzaneh.heidari@mila.quebec

Collaborating researcher - McGill University

Principal supervisor :

PhD - Université de Montréal

lizairem@mila.quebec

Sitao Luan

Postdoctorate - McGill University

Co-supervisor :

Larry Ma

Research Intern - Polytechnique Montréal

maning.hust@gmail.com

Ewan Murphy

Collaborating researcher - INRIA

ewan.murphy@mila.quebec

charlotte.noxon@mila.quebec

Charlotte Noxon

Master's Research - Université de Montréal

michael.rizvi-martel@mila.quebec

Michael Rizvi-Martel

PhD - Université de Montréal

Co-supervisor :

Adam M. Oberman

Anantha Krishnan Sunilkumar

PhD - Université de Montréal

anantha.krishnan.sunilkumar@mila.quebec

pascal.tikeng@mila.quebec

Pascal Tikeng Notsawo

PhD - Université de Montréal

Co-supervisor :

Guillaume Dumas

Collaborating researcher - Université de Montréal

Co-supervisor :

Reihaneh Rabbany

aaron.tjandra@mila.quebec

Beheshteh Toloueirakhshan

PhD - Université de Montréal

rakhshab@mila.quebec

jingjing.zheng@mila.quebec

Jingjing Zheng

Research Intern - Université de Montréal

Publications

Laplacian Change Point Detection for Single and Multi-view Dynamic Graphs

Samy Coulombe

Dynamic graphs are rich data structures that are used to model complex relationships between entities over time. In particular, anomaly dete… (see more)ction in temporal graphs is crucial for many real world applications such as intrusion identification in network systems, detection of ecosystem disturbances and detection of epidemic outbreaks. In this paper, we focus on change point detection in dynamic graphs and address three main challenges associated with this problem: i). how to compare graph snapshots across time, ii). how to capture temporal dependencies, and iii). how to combine different views of a temporal graph. To solve the above challenges, we first propose Laplacian Anomaly Detection (LAD) which uses the spectrum of graph Laplacian as the low dimensional embedding of the graph structure at each snapshot. LAD explicitly models short term and long term dependencies by applying two sliding windows. Next, we propose MultiLAD, a simple and effective generalization of LAD to multi-view graphs. MultiLAD provides the first change point detection method for multi-view dynamic graphs. It aggregates the singular values of the normalized graph Laplacian from different views through the scalar power mean operation. Through extensive synthetic experiments, we show that i). LAD and MultiLAD are accurate and outperforms state-of-the-art baselines and their multi-view extensions by a large margin, ii). MultiLAD's advantage over contenders significantly increases when additional views are available, and iii). MultiLAD is highly robust to noise from individual views. In five real world dynamic graphs, we demonstrate that LAD and MultiLAD identify significant events as top anomalies such as the implementation of government COVID-19 interventions which impacted the population mobility in multi-view traffic networks.

2023-11-05

ACM Transactions on Knowledge Discovery from Data (published)

arxiv.org

Generative Learning of Continuous Data by Tensor Networks

Alex Meiburg

Jian Hua Chen

Jacob Miller

Raphaelle Tihon

Alejandro Perdomo-ortiz

2023-10-30

ArXiv (preprint)

arxiv.org

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

Shenyang Huang

Farimah Poursafaei

Jacob Danovitch

Matthias Fey

Weihua Hu

Emanuele Rossi

Jure Leskovec

Michael M. Bronstein

Reihaneh Rabbany

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and r… (see more)obust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.

2023-09-24

NeurIPS.cc/2023/Track/Datasets_and_Benchmarks (poster)

openreview.net

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets (Ultra Large Dataset)

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Ali Parviz

Luis T. Díaz Müller

Jama Hussein Mohamud

Michael Craig

Cristian Gabellini

Jian Tang … (see 8 more)

Christopher G. Morris

Mirco Ravanelli

Guy Wolf

Prudencio Tossou

Hadrien Mary

Błażej Banaszewski

Chad Martin

Dominic Masters

2023-09-21

Zenodo (unknown)

Preface

François Coste

Faissal Ouardi

2023-07-04

Proceedings of 16th edition of the International Conference on Grammatical Inference (published)

proceedings.mlr.press

ROSA: Random Orthogonal Subspace Adaptation

2023-06-19

ICML.cc/2023/Workshop/ES-FoMO (poster)

openreview.net

Fast and Attributed Change Detection on Dynamic Graphs with Density of States

2023-05-14

arXiv (preprint)

arxiv.org

Benchmarking State-Merging Algorithms for Learning Regular Languages.

Adil Soubki

Jeffrey Heinz

François Coste

Faissal Ouardi

2022-12-31

International Conference on Graphics and Interaction (published)

Explaining Graph Neural Networks Using Interpretable Local Surrogates

Farzaneh Heidari

Perouz Taslakian

2022-12-31

TAG-ML (published)

proceedings.mlr.press

Formal and Empirical Studies of Counting Behaviour in ReLU RNNs.

Nadine El-Naggar

Andrew Ryzhikov

Laure Daviaud

Pranava Madhyastha

Tillman Weyde

François Coste

Faissal Ouardi

2022-12-31

International Conference on Graphics and Interaction (published)

Identification of Substitutable Context-Free Languages over Infinite Alphabets from Positive Data

Yutaro Numaya

Diptarama Hendrian

Ryo Yoshinaka

Ayumi Shinohara

François Coste

Faissal Ouardi

This paper is concerned with the identification in the limit from positive data of sub-stitutable context-free languages cfl s) over infinit… (see more)e alphabets. Clark and Eyraud (2007) showed that substitutable cfl s over finite alphabets are learnable in this learning paradigm. We show that substitutable cfl s generated by grammars whose production rules may have predicates that represent sets of potentially infinitely many terminal symbols in a compact manner are learnable if the terminal symbol sets represented by those predicates are learnable, under a certain condition. This can be seen as a result parallel to Argyros and D’Antoni’s work (2018) that amplifies the query learnability of predicate classes to that of symbolic automata classes. Our result is the first that shows such amplification is possible for identifying some cfl s in the limit from positive data.

2022-12-31

ICGI (published)

Learning Syntactic Monoids from Samples by extending known Algorithms for learning State Machines

Simon Dieck

Sicco Verwer

François Coste

Faissal Ouardi

For the inference of regular languages, most current methods learn a version of deterministic finite automata. Syntactic monoids are an alte… (see more)rnative representation of regular languages, which have some advantages over automata. For example, traces can be parsed starting from any index and the star-freeness of the language they represent can be checked in polynomial time. But, to date, there existed no passive learning algorithm for syntactic monoids. In this paper, we prove that known state-merging algorithms for learning deterministic finite automata can be instrumented to learn syntactic monoids instead, by using as the input a special structure proposed in this paper: the interfix-graph. Further, we introduce a method to encode frequencies on the interfix-graph, such that models can also be learned from only positive traces. We implemented this structure and performed experiments with both traditional data and data containing only positive traces. As such this work answers basic theoretical and experimental questions regarding a novel passive learning algorithm for syntactic monoids.

2022-12-31

International Conference on Graphics and Interaction (published)