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

Omar Chikar

Master's Research - Université de Montréal

Jun Dai

Postdoctorate - Université de Montréal

Alireza Dizaji

Master's Research - Université de Montréal

Marawan Gamal

PhD - Université de Montréal

Julia Gastinger

Collaborating researcher - University of Mannheim

Co-supervisor :

Farzaneh Heidari

PhD - Université de Montréal

Co-supervisor :

Jian Tang

Shenyang Huang

PhD - McGill University

Principal supervisor :

Research Intern - Université de Montréal

Maude Lizaire

PhD - Université de Montréal

Soroush Omranpour

Master's Research - McGill University

Principal supervisor :

Razieh Razieh Shirzadkhani

Collaborating researcher

Co-supervisor :

Michael Rizvi-Martel

PhD - Université de Montréal

Co-supervisor :

Pascal Tikeng Notsawo

PhD - Université de Montréal

Co-supervisor :

Beheshteh Toloueirakhshan

PhD - Université de Montréal

Website

Publications

Efficient Leverage Score Sampling for Tensor Train Decomposition

Vivek Bharadwaj

Beheshteh T. Rakhshan

Osman Asif Malik

Tensor Train~(TT) decomposition is widely used in the machine learning and quantum physics communities as a popular tool to efficiently comp… (see more)ress high-dimensional tensor data. In this paper, we propose an efficient algorithm to accelerate computing the TT decomposition with the Alternating Least Squares (ALS) algorithm relying on exact leverage scores sampling. For this purpose, we propose a data structure that allows us to efficiently sample from the tensor with time complexity logarithmic in the product of the tensor dimensions. Our contribution specifically leverages the canonical form of the TT decomposition. By maintaining the canonical form through each iteration of ALS, we can efficiently compute (and sample from) the leverage scores, thus achieving significant speed-up in solving each sketched least-square problem. Experiments on synthetic and real data on dense and sparse tensors demonstrate that our method outperforms SVD-based and ALS-based algorithms.

2024-09-25

NeurIPS.cc/2024/Conference (poster)

openreview.net

UTG: Towards a Unified View of Snapshot and Event Based Models for Temporal Graphs

Shenyang Huang

Farimah Poursafaei

Emanuele Rossi

Many real world graphs are inherently dynamic, constantly evolving with node and edge additions. These graphs can be represented by temporal… (see more) graphs, either through a stream of edge events or a sequence of graph snapshots. Until now, the development of machine learning methods for both types has occurred largely in isolation, resulting in limited experimental comparison and theoretical crosspollination between the two. In this paper, we introduce Unified Temporal Graph (UTG), a framework that unifies snapshot-based and event-based machine learning models under a single umbrella, enabling models developed for one representation to be applied effectively to datasets of the other. We also propose a novel UTG training procedure to boost the performance of snapshot-based models in the streaming setting. We comprehensively evaluate both snapshot and event-based models across both types of temporal graphs on the temporal link prediction task. Our main findings are threefold: first, when combined with UTG training, snapshot-based models can perform competitively with event-based models such as TGN and GraphMixer even on event datasets. Second, snapshot-based models are at least an order of magnitude faster than most event-based models during inference. Third, while event-based methods such as NAT and DyGFormer outperforms snapshot-based methods on both types of temporal graphs, this is because they leverage joint neighborhood structural features thus emphasizing the potential to incorporate these features into snapshotbased models as well. These findings highlight the importance of comparing model architectures independent of the data format and suggest the potential of combining the efficiency of snapshot-based models with the performance of event-based models in the future.

2024-07-17

ArXiv (preprint)

MiNT: Multi-Network Training for Transfer Learning on Temporal Graphs

Kiarash Shamsi

Tran Gia Bao Ngo

Razieh Shirzadkhani

Shenyang Huang

Farimah Poursafaei

Poupak Azad

Baris Coskunuzer

Cuneyt Gurcan Akcora

2024-06-14

ArXiv (preprint)

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs

Julia Gastinger

Shenyang Huang

Mikhail Galkin

Erfan Loghmani

Ali Parviz

Farimah Poursafaei

Jacob Danovitch

Emanuele Rossi

Ioannis Koutis

Heiner Stuckenschmidt

Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entiti… (see more)es over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly larger than existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods.

2024-06-14

ArXiv (preprint)

Towards Neural Scaling Laws for Foundation Models on Temporal Graphs

Razieh Shirzadkhani

Tran Gia Bao Ngo

Kiarash Shamsi

Shenyang Huang

Farimah Poursafaei

Poupak Azad

Baris Coskunuzer

Cuneyt Gurcan Akcora

The field of temporal graph learning aims to learn from evolving network data to forecast future interactions. Given a collection of observe… (see more)d temporal graphs, is it possible to predict the evolution of an unseen network from the same domain? To answer this question, we first present the Temporal Graph Scaling (TGS) dataset, a large collection of temporal graphs consisting of eighty-four ERC20 token transaction networks collected from 2017 to 2023. Next, we evaluate the transferability of Temporal Graph Neural Networks (TGNNs) for the temporal graph property prediction task by pre-training on a collection of up to sixty-four token transaction networks and then evaluating the downstream performance on twenty unseen token networks. We find that the neural scaling law observed in NLP and Computer Vision also applies in temporal graph learning, where pre-training on greater number of networks leads to improved downstream performance. To the best of our knowledge, this is the first empirical demonstration of the transferability of temporal graphs learning. On downstream token networks, the largest pre-trained model outperforms single model TGNNs on thirteen unseen test networks. Therefore, we believe that this is a promising first step towards building foundation models for temporal graphs.

2024-06-14

ArXiv (preprint)

Efficient Leverage Score Sampling for Tensor Train Decomposition

Vivek Bharadwaj

Beheshteh T. Rakhshan

Osman Asif Malik

2024-06-04

ArXiv (preprint)

A Tensor Decomposition Perspective on Second-order RNNs

Maude Lizaire

Michael Rizvi-Martel

Marawan Gamal

2024-05-01

ICML.cc/2024/Conference (spotlight)

proceedings.mlr.press

openreview.net

Length independent PAC-Bayes bounds for Simple RNNs

Volodimir Mitarchuk

Clara Lacroce

Rémi Eyraud

Rémi Emonet

Amaury Habrard

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Simulating weighted automata over sequences and trees with transformers

Michael Rizvi-Martel

Maude Lizaire

Clara Lacroce

2024-04-18

Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (published)

Simulating Weighted Automata over Sequences and Trees with Transformers

Michael Rizvi

Maude Lizaire

Clara Lacroce

2024-03-12

ArXiv (preprint)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets

Dominique Beaini

Shenyang Huang

Joao Alex Cunha

Zhiyi Li

Gabriela Moisescu-Pareja

Oleksandr Dymov

Samuel Maddrell-Mander

Callum McLean

Frederik Wenkel

Luis Müller

Jama Hussein Mohamud

Ali Parviz

Michael Craig

Michał Koziarski

Jiarui Lu

Zhaocheng Zhu

Cristian Gabellini

Kerstin Klaser

Josef Dean

Cas Wognum … (see 15 more)

Maciej Sypetkowski

Jian Tang

Christopher Morris

Ioannis Koutis

Mirco Ravanelli

Guy Wolf

Prudencio Tossou

Hadrien Mary

Therence Bois

Andrew William Fitzgibbon

Blazej Banaszewski

Chad Martin

Dominic Masters

Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, wh… (see more)ere datasets are often hand-curated, and hence typically small, the lack of datasets with labeled features, and codebases to manage those datasets, has hindered the development of foundation models. In this work, we present seven novel datasets categorized by size into three distinct categories: ToyMix, LargeMix and UltraLarge. These datasets push the boundaries in both the scale and the diversity of supervised labels for molecular learning. They cover nearly 100 million molecules and over 3000 sparsely defined tasks, totaling more than 13 billion individual labels of both quantum and biological nature. In comparison, our datasets contain 300 times more data points than the widely used OGB-LSC PCQM4Mv2 dataset, and 13 times more than the quantum-only QM1B dataset. In addition, to support the development of foundational models based on our proposed datasets, we present the Graphium graph machine learning library which simplifies the process of building and training molecular machine learning models for multi-task and multi-level molecular datasets. Finally, we present a range of baseline results as a starting point of multi-task and multi-level training on these datasets. Empirically, we observe that performance on low-resource biological datasets show improvement by also training on large amounts of quantum data. This indicates that there may be potential in multi-task and multi-level training of a foundation model and fine-tuning it to resource-constrained downstream tasks. The Graphium library is publicly available on Github and the dataset links are available in Part 1 and Part 2.

2024-01-16

ICLR.cc/2024/Conference (poster)

openreview.net

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

Shenyang Huang

Samy Coulombe

Yasmeen Hitti

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 article, 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.

2024-01-12

ACM Transactions on Knowledge Discovery from Data (published)