Reihaneh Rabbany

chin-chen.yang@mila.quebec

Zachary Yang

PhD - McGill University

elahe.kooshafar@mila.quebec

Elahe Kooshafar

Master's Research - McGill University

Farimah Poursafaei

Postdoctorate - McGill University

farimah.poursafaei@mila.quebec

jacob-junqi.tian@mila.quebec

Peter Yu

Collaborating researcher - McGill University

hao.yu@mila.quebec

Jacob-Junqi Tian

Collaborating researcher - McGill University

jean-francois.godbout@mila.quebec

Jean-François Godbout

Independent visiting researcher

Julia Gastinger

Research Intern - University of Mannheim

Principal supervisor :

julia.gastinger@mila.quebec

kellin.pelrine@mila.quebec

Kellin Pelrine

PhD - McGill University

mauricio.rivera@mila.quebec

Mauricio Rivera

Collaborating researcher - Graduated from McGill University

Pratheeksha Nair

PhD - McGill University

pratheeksha.nair@mila.quebec

Shirzadkhani Razieh Shirzadkhani

Collaborating researcher

Principal supervisor :

razieh.shirzadkhani@mila.quebec

sahar.omidishayegan@mila.quebec

Sahar Omidi Shayegan

Master's Research - McGill University

shahrad.mohammadzadeh@mila.quebec

Shahrad Mohammadzadeh

Collaborating researcher - McGill University

Co-supervisor :

Doina Precup

PhD - McGill University

Co-supervisor :

Collaborating researcher - McGill University

sophia.garrel@mila.quebec

Soroush Omranpour

Master's Research - McGill University

Co-supervisor :

soroush.omranpour@mila.quebec

Flight-SEIR: Incorporating Flight Data to Improve Epidemiological Modelling and Disease Outbreak Prevention

Vidya Sujaya

Master's Research - McGill University

vidya.sujaya@mila.quebec

Blog Posts

August 3, 2021

Shenyang Huang

Reihaneh Rabbany

Read the article

Publications

T-NET: Weakly Supervised Graph Learning for Combatting Human Trafficking

Pratheeksha Nair

Javin Liu

Catalina Vajiac

Andreas Olligschlaeger

Duen Horng Chau

Mirela T. Cazzolato

Cara Jones

Christos Faloutsos

Human trafficking (HT) for forced sexual exploitation, often described as modern-day slavery, is a pervasive problem that affects millions o… (see more)f people worldwide. Perpetrators of this crime post advertisements (ads) on behalf of their victims on adult service websites (ASW). These websites typically contain hundreds of thousands of ads including those posted by independent escorts, massage parlor agencies and spammers (fake ads). Detecting suspicious activity in these ads is difficult and developing data-driven methods is challenging due to the hard-to-label, complex and sensitive nature of the data. In this paper, we propose T-Net, which unlike previous solutions, formulates this problem as weakly supervised classification. Since it takes several months to years to investigate a case and obtain a single definitive label, we design domain-specific signals or indicators that provide weak labels. T-Net also looks into connections between ads and models the problem as a graph learning task instead of classifying ads independently. We show that T-Net outperforms all baselines on a real-world dataset of ads by 7% average weighted F1 score. Given that this data contains personally identifiable information, we also present a realistic data generator and provide the first publicly available dataset in this domain which may be leveraged by the wider research community.

2024-03-24

Proceedings of the AAAI Conference on Artificial Intelligence (published)

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)

Combining Confidence Elicitation and Sample-based Methods for Uncertainty Quantification in Misinformation Mitigation

Mauricio Rivera

Jean-François Godbout

Kellin Pelrine

2024-01-13

ArXiv (preprint)

Comparing GPT-4 and Open-Source Language Models in Misinformation Mitigation

Tyler Vergho

Jean-François Godbout

Kellin Pelrine

Recent large language models (LLMs) have been shown to be effective for misinformation detection. However, the choice of LLMs for experiment… (see more)s varies widely, leading to uncertain conclusions. In particular, GPT-4 is known to be strong in this domain, but it is closed source, potentially expensive, and can show instability between different versions. Meanwhile, alternative LLMs have given mixed results. In this work, we show that Zephyr-7b presents a consistently viable alternative, overcoming key limitations of commonly used approaches like Llama-2 and GPT-3.5. This provides the research community with a solid open-source option and shows open-source models are gradually catching up on this task. We then highlight how GPT-3.5 exhibits unstable performance, such that this very widely used model could provide misleading results in misinformation detection. Finally, we validate new tools including approaches to structured output and the latest version of GPT-4 (Turbo), showing they do not compromise performance, thus unlocking them for future research and potentially enabling more complex pipelines for misinformation mitigation.

2024-01-12

ArXiv (preprint)

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)

GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning

Aarash Feizi

Randall Balestriero

Arantxa Casanova

Adriana Romero Soriano

We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised L… (see more)earning (SSL) positive samples selection. Current SSL methods leverage Data-Augmentations (DA) for generating positive samples and incorporate prior knowledge - an incorrect, or too weak DA will drastically reduce the quality of the learned representation. GPS-SSL proposes instead to design a metric space where Euclidean distances become a meaningful proxy for semantic relationship. In that space, it is now possible to generate positive samples from nearest neighbor sampling. Any prior knowledge can now be embedded into that metric space independently from the employed DA. From its simplicity, GPS-SSL is applicable to any SSL method, e.g. SimCLR or BYOL. A key benefit of GPS-SSL is in reducing the pressure in tailoring strong DAs. For example GPS-SSL reaches 85.58% on Cifar10 with weak DA while the baseline only reaches 37.51%. We therefore move a step forward towards the goal of making SSL less reliant on DA. We also show that even when using strong DAs, GPS-SSL outperforms the baselines on under-studied domains. We evaluate GPS-SSL along with multiple baseline SSL methods on numerous downstream datasets from different domains when the models use strong or minimal data augmentations. We hope that GPS-SSL will open new avenues in studying how to inject a priori knowledge into SSL in a principled manner.

2024-01-03

ArXiv (preprint)

Uncertainty Resolution in Misinformation Detection

Yury Orlovskiy

Camille Thibault

Anne Imouza

Jean-François Godbout

Kellin Pelrine

2024-01-02

ArXiv (preprint)

Quantifying learning-style adaptation in effectiveness of LLM teaching

Ruben Weijers

Gabrielle Fidelis de Castilho

Jean-François Godbout

Kellin Pelrine

This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehens… (see more)ion and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model’s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.

Temporal Graph Analysis with TGX

Razieh Shirzadkhani

Shenyang Huang

Elahe Kooshafar

Farimah Poursafaei

Real-world networks, with their evolving relations, are best captured as temporal graphs. However, existing software libraries are largely d… (see more)esigned for static graphs where the dynamic nature of temporal graphs is ignored. Bridging this gap, we introduce TGX, a Python package specially designed for analysis of temporal networks that encompasses an automated pipeline for data loading, data processing, and analysis of evolving graphs. TGX provides access to eleven built-in datasets and eight external Temporal Graph Benchmark (TGB) datasets as well as any novel datasets in the .csv format. Beyond data loading, TGX facilitates data processing functionalities such as discretization of temporal graphs and node subsampling to accelerate working with larger datasets. For comprehensive investigation, TGX offers network analysis by providing a diverse set of measures, including average node degree and the evolving number of nodes and edges per timestamp. Additionally, the package consolidates meaningful visualization plots indicating the evolution of temporal patterns, such as Temporal Edge Appearance (TEA) and Temporal Edge Trafficc (TET) plots. The TGX package is a robust tool for examining the features of temporal graphs and can be used in various areas like studying social networks, citation networks, and tracking user interactions. We plan to continuously support and update TGX based on community feedback. TGX is publicly available on: https://github.com/ComplexData-MILA/TGX.

2024-01-01

WSDM (published)

Exhaustive Evaluation of Dynamic Link Prediction

Farimah Poursafaei

Dynamic link prediction is a crucial task in the study of evolving graphs, which serve as abstract models for various real-world application… (see more)s. Recent dynamic graph representation learning models have claimed near-perfect performance in this task. However, we argue that the standard evaluation strategy for dynamic link prediction overlooks the sparsity and recurrence patterns inherent in dynamic networks. Specifically, the current strategy suffers from issues such as evaluating models on a balanced set of positive and negative edges, neglecting the reassessment of frequently recurring positive edges, and lacking a comprehensive evaluation of both recurring and new edges.To address these limitations, we propose a novel evaluation strategy called EXHAUSTIVE, which takes into account all relevant negative edges and separately assesses the performance on recurring and new edges. Using our proposed evaluation strategy, we compare the performance of five state-of-the-art dynamic graph learning models on seven benchmark datasets. Compared to the previous common evaluation strategy, we observe an average drop of 62% in Average Precision for dynamic link prediction. Additionally, the ranking of the models also changes under the new evaluation setting. Furthermore, we demonstrate that while all models perform considerably worse when predicting new edges compared to recurring ones, the best performing models differ between the two scenarios. This highlights the importance of employing the proposed evaluation strategy for both the assessment and design of dynamic link prediction models. By adopting our novel evaluation strategy, researchers can obtain a more accurate understanding of model performance in dynamic link prediction, leading to improved evaluation and design of such models.

2023-12-04

2023 IEEE International Conference on Data Mining Workshops (ICDMW) (published)

SWEET - Weakly Supervised Person Name Extraction for Fighting Human Trafficking

Javin Liu

Hao Yu

Vidya Sujaya

Pratheeksha Nair

Kellin Pelrine

In this work, we propose a weak supervision pipeline SWEET: Supervise Weakly for Entity Extraction to fight Trafficking for extracting perso… (see more)n names from noisy escort advertisements. Our method combines the simplicity of rule-matching (through antirules, i.e., negated rules) and the generalizability of large language models fine-tuned on benchmark, domain-specific and synthetic datasets, treating them as weak labels. One of the major challenges in this domain is limited labeled data. SWEET addresses this by obtaining multiple weak labels through labeling functions and effectively aggregating them. SWEET outperforms the previous supervised SOTA method for this task by 9% F1 score on domain data and better generalizes to common benchmark datasets. Furthermore, we also release HTGEN, a synthetically generated dataset of escort advertisements (built using ChatGPT) to facilitate further research within the community.

2023-12-01

Findings of the Association for Computational Linguistics: EMNLP 2023 (published)

Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4

Kellin Pelrine

Anne Imouza

Meilina Reksoprodjo

Camille Thibault

Caleb Gupta

Joel Christoph

Jean-François Godbout

Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on… (see more) generalization, uncertainty, and how to leverage recent large language models, in order to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible. We first demonstrate that GPT-4 can outperform prior methods in multiple settings and languages. Next, we explore generalization, revealing that GPT-4 and RoBERTa-large exhibit differences in failure modes. Third, we propose techniques to handle uncertainty that can detect impossible examples and strongly improve outcomes. We also discuss results on other language models, temperature, prompting, versioning, explainability, and web retrieval, each one providing practical insights and directions for future research. Finally, we publish the LIAR-New dataset with novel paired English and French misinformation data and Possibility labels that indicate if there is sufficient context for veracity evaluation. Overall, this research lays the groundwork for future tools that can drive real-world progress to combat misinformation.

2023-10-07

EMNLP/2023/Conference (published)