Portrait de Tegan Maharaj

Tegan Maharaj

Membre académique principal
Professeure adjointe en apprentissage automatique, HEC Montréal, Département de sciences de la décision
Sujets de recherche
Apprentissage de représentations
Apprentissage multimodal
Apprentissage profond
Systèmes dynamiques
Théorie de l'apprentissage automatique

Biographie

Je suis professeure adjointe au Département de sciences de la décision à HEC Montréal. Mes objectifs de recherche sont de contribuer à la compréhension et aux techniques de la science du développement responsable de l’IA, tout en appliquant utilement l’IA à des problèmes écologiques à fort impact liés au changement climatique, à l’épidémiologie, à l’alignement de l’IA et à l’évaluation des impacts écologiques. Mes travaux récents portent sur deux thèmes : l’utilisation de modèles profonds pour l’analyse des politiques et l’atténuation des risques; et la conception de données ou d’environnements de tests unitaires pour évaluer empiriquement le comportement d’apprentissage ou simuler le déploiement d’un système d’IA. N’hésitez pas à me contacter pour toute collaboration dans ces domaines.

Je suis généralement intéressée par l’étude de ce qui « entre » dans les modèles profonds : non seulement les données, mais l’environnement d’apprentissage plus globalement, qui comprend la conception/spécification des tâches, la fonction de perte et la régularisation, ainsi que le contexte sociétal du déploiement, notamment les considérations de confidentialité, les tendances et les incitatifs, les normes et les préjugés humains. Je suis préoccupée et passionnée par l’éthique de l’IA, la sécurité et l’application de l’apprentissage machine à la gestion de l’environnement, à la santé et au bien-être social.

Étudiants actuels

Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e :

Publications

The Singapore Consensus on Global AI Safety Research Priorities
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Imane Bello
Malo Bourgon
Mark Brakel
Sim'eon Campos
Duncan Cass-Beggs
Jiahao Chen … (voir 67 de plus)
Rumman Chowdhury
Kuan Chua Seah
Jeff Clune
Juntao Dai
Agnès Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah D. Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low Kian Hsiang
Minlie Huang
Sami Jawhar
Jingyu Wang
Adam Tauman Kalai
Meindert Kamphuis
Mohan S. Kankanhalli
Subhash Kantamneni
Mathias Bonde Kirk
Thomas Kwa
Jeffrey Ladish
Kwok-Yan Lam
Wan Lee Sie
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nick Moës
Simon Möller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Sean O hEigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
Saad Siddiqui
Aradhana Sinha
Martín Soto
Cheston Tan
Dong Ting
William Tjhi
Robert Trager
Brian Tse
H. AnthonyTungK.
Vanessa Wilfred
John Willes
Denise Wong
Wei Xu
Rongwu Xu
Yi Zeng
HongJiang Zhang
Djordje Zikelic
Rapidly improving AI capabilities and autonomy hold significant promise of transformation, but are also driving vigorous debate on how to en… (voir plus)sure that AI is safe, i.e., trustworthy, reliable, and secure. Building a trusted ecosystem is therefore essential -- it helps people embrace AI with confidence and gives maximal space for innovation while avoiding backlash. The "2025 Singapore Conference on AI (SCAI): International Scientific Exchange on AI Safety" aimed to support research in this space by bringing together AI scientists across geographies to identify and synthesise research priorities in AI safety. This resulting report builds on the International AI Safety Report chaired by Yoshua Bengio and backed by 33 governments. By adopting a defence-in-depth model, this report organises AI safety research domains into three types: challenges with creating trustworthy AI systems (Development), challenges with evaluating their risks (Assessment), and challenges with monitoring and intervening after deployment (Control).
The Singapore Consensus on Global AI Safety Research Priorities
Luke Ong
Stuart Russell
Dawn Song
Max Tegmark
Lan Xue
Ya-Qin Zhang
Stephen Casper
Wan Sie Lee
Vanessa Wilfred
Vidhisha Balachandran
Fazl Barez
Michael Belinsky
Imane Bello
Malo Bourgon
Mark Brakel
Sim'eon Campos
Duncan Cass-Beggs … (voir 67 de plus)
Jiahao Chen
Rumman Chowdhury
Kuan Chua Seah
Jeff Clune
Juntao Dai
Agnès Delaborde
Nouha Dziri
Francisco Eiras
Joshua Engels
Jinyu Fan
Adam Gleave
Noah D. Goodman
Fynn Heide
Johannes Heidecke
Dan Hendrycks
Cyrus Hodes
Bryan Low Kian Hsiang
Minlie Huang
Sami Jawhar
Jingyu Wang
Adam Tauman Kalai
Meindert Kamphuis
Mohan S. Kankanhalli
Subhash Kantamneni
Mathias Bonde Kirk
Thomas Kwa
Jeffrey Ladish
Kwok-Yan Lam
Wan Lee Sie
Taewhi Lee
Xiaojian Li
Jiajun Liu
Chaochao Lu
Yifan Mai
Richard Mallah
Julian Michael
Nick Moës
Simon Möller
Kihyuk Nam
Kwan Yee Ng
Mark Nitzberg
Besmira Nushi
Sean O hEigeartaigh
Alejandro Ortega
Pierre Peigné
James Petrie
Nayat Sanchez-Pi
Sarah Schwettmann
Buck Shlegeris
Saad Siddiqui
Aradhana Sinha
Martín Soto
Cheston Tan
Dong Ting
William Tjhi
Robert Trager
Brian Tse
H. AnthonyTungK.
John Willes
Denise Wong
Wei Xu
Rongwu Xu
Yi Zeng 0005
HongJiang Zhang
Djordje Zikelic
The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track
Eshta Bhardwaj
Harshit Gujral
Siyi Wu
Ciara Zogheib
Christoph Becker
Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not… (voir plus) millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a literature review of data curation principles. We use the framework to assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023. We summarize key findings and trends. Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management. We suggest targeted strategies and resources to improve documentation in these areas and provide recommendations for the NeurIPS peer-review process that prioritize rigorous data curation in ML. Finally, we provide results in the format of a dataset that showcases aspects of recommended data curation practices. Our rubric and results are of interest for improving data curation practices broadly in the field of ML as well as to data curation and science and technology studies scholars studying practices in ML. Our aim is to support continued improvement in interdisciplinary research on dataset practices, ultimately improving the reusability and reproducibility of new datasets and benchmarks, enabling standardized and informed human oversight, and strengthening the foundation of rigorous and responsible ML research.
Quantifying Likeness: A Simple Machine Learning Approach to Identifying Copyright Infringement in (AI-Generated) Artwork
Michaela Drouillard
Ryan Spencer
Nikée Nantambu-Allen
Through study of legal precedent, we propose a pragmatic way to quantify copyright infringement, via stylistic similarity, in AI-generated a… (voir plus)rtwork. Copyright infringement by AI systems is a topic of rapidly-increasing importance as generative AI becomes more widespread and commercial. In contrast to typical work in this field, more in line with a realistic legal setting, our approach quantifies similarity of a set of potentially-infringing "defendant" artworks to a set of copyrighted "plaintiff" artworks. We develop our approach by making use of one of the most litigated artistic creations of this century -- Mickey Mouse. We curate a dataset using Mickey as the plaintiff, and perform hyperparameter search, scaling, and robustness analyses with various defendent artworks from real legal cases to find settings that generalize well. We operationalize similarity via a simple discrimintative task which can be accomplished in a low-resource setting by non-experts -- our aim is to provide a `plug and play' method that is feasible for artists and/or legal experts to use with their own plaintiff sets of artworks. We further demonstrate the viability of our approach by quantifying similarity in a second curated dataset of Maria Prymachenko's art vs. AI-generated images. We conclude by discussing uses of our work in both legal and other settings, including provision of artist compensation.
The State of Data Curation at NeurIPS: An Assessment of Dataset Development Practices in the Datasets and Benchmarks Track
Eshta Bhardwaj
Harshit Gujral
Siyi Wu
Ciara Zogheib
Christoph Becker
Data curation is a field with origins in librarianship and archives, whose scholarship and thinking on data issues go back centuries, if not… (voir plus) millennia. The field of machine learning is increasingly observing the importance of data curation to the advancement of both applications and fundamental understanding of machine learning models - evidenced not least by the creation of the Datasets and Benchmarks track itself. This work provides an analysis of dataset development practices at NeurIPS through the lens of data curation. We present an evaluation framework for dataset documentation, consisting of a rubric and toolkit developed through a literature review of data curation principles. We use the framework to assess the strengths and weaknesses in current dataset development practices of 60 datasets published in the NeurIPS Datasets and Benchmarks track from 2021-2023. We summarize key findings and trends. Results indicate greater need for documentation about environmental footprint, ethical considerations, and data management. We suggest targeted strategies and resources to improve documentation in these areas and provide recommendations for the NeurIPS peer-review process that prioritize rigorous data curation in ML. Finally, we provide results in the format of a dataset that showcases aspects of recommended data curation practices. Our rubric and results are of interest for improving data curation practices broadly in the field of ML as well as to data curation and science and technology studies scholars studying practices in ML. Our aim is to support continued improvement in interdisciplinary research on dataset practices, ultimately improving the reusability and reproducibility of new datasets and benchmarks, enabling standardized and informed human oversight, and strengthening the foundation of rigorous and responsible ML research.
Implicit meta-learning may lead language models to trust more reliable sources
Dmitrii Krasheninnikov
Egor Krasheninnikov
Bruno Mlodozeniec
We demonstrate that large language models (LLMs) may learn indicators of document usefulness and modulate their updates accordingly. We intr… (voir plus)oduce random strings ("tags") as indicators of usefulness in a synthetic fine-tuning dataset. Fine-tuning on this dataset leads to **implicit meta-learning (IML)**: in further fine-tuning, the model updates to make more use of text that is tagged as useful. We perform a thorough empirical investigation of this phenomenon, finding (among other things) that (i) it occurs in both pretrained LLMs and those trained from scratch, as well as on a vision task, and (ii) larger models and smaller batch sizes tend to give more IML. We also use probing to examine how IML changes the way models store knowledge in their parameters. Finally, we reflect on what our results might imply about the capabilities, risks, and controllability of future AI systems.
Machine Learning Data Practices through a Data Curation Lens: An Evaluation Framework
Eshta Bhardwaj
Harshit Gujral
Siyi Wu
Ciara Zogheib
Christoph Becker
Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and… (voir plus) shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.
Methods, Applications, and Directions of Learning-to-Rank in NLP Research
Justin Lee
Gabriel Bernier-Colborne
Sowmya Vajjala
Learning-to-rank (LTR) algorithms aim to order a set of items according to some criteria. They are at the core of applications such as web s… (voir plus)earch and social media recommendations, and are an area of rapidly increasing interest, with the rise of large language models (LLMs) and the widespread impact of these technologies on society. In this paper, we survey the diverse use cases of LTR methods in natural language processing (NLP) research, looking at previously under-studied aspects such as multilingualism in LTR applications and statistical significance testing for LTR problems. We also consider how large language models are changing the LTR landscape. This survey is aimed at NLP researchers and practitioners interested in understanding the formalisms and best practices regarding the application of LTR approaches in their research.
Machine learning data practices through a data curation lens: An evaluation framework
Eshta Bhardwaj
Harshit Gujral
Siyi Wu
Ciara Zogheib
Christoph Becker
Studies of dataset development in machine learning call for greater attention to the data practices that make model development possible and… (voir plus) shape its outcomes. Many argue that the adoption of theory and practices from archives and data curation fields can support greater fairness, accountability, transparency, and more ethical machine learning. In response, this paper examines data practices in machine learning dataset development through the lens of data curation. We evaluate data practices in machine learning as data curation practices. To do so, we develop a framework for evaluating machine learning datasets using data curation concepts and principles through a rubric. Through a mixed-methods analysis of evaluation results for 25 ML datasets, we study the feasibility of data curation principles to be adopted for machine learning data work in practice and explore how data curation is currently performed. We find that researchers in machine learning, which often emphasizes model development, struggle to apply standard data curation principles. Our findings illustrate difficulties at the intersection of these fields, such as evaluating dimensions that have shared terms in both fields but non-shared meanings, a high degree of interpretative flexibility in adapting concepts without prescriptive restrictions, obstacles in limiting the depth of data curation expertise needed to apply the rubric, and challenges in scoping the extent of documentation dataset creators are responsible for. We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods can inform machine learning data practices.
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar
Abulhair Saparov
Javier Rando
Daniel Paleka
Miles Turpin
Peter Hase
Ekdeep Singh Lubana
Erik Jenner
Stephen Casper
Oliver Sourbut
Benjamin L. Edelman
Zhaowei Zhang
Mario Günther
Anton Korinek
Jose Hernandez-Orallo
Lewis Hammond
Eric J Bigelow
Alexander Pan
Lauro Langosco
Tomasz Korbak … (voir 18 de plus)
Heidi Chenyu Zhang
Ruiqi Zhong
Sean O hEigeartaigh
Gabriel Recchia
Giulio Corsi
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
Jakob Nicolaus Foerster
Florian Tramèr
He He
Atoosa Kasirzadeh
Yejin Choi
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (voir plus)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar
Abulhair Saparov
Javier Rando
Daniel Paleka
Miles Turpin
Peter Hase
Ekdeep Singh Lubana
Erik Jenner
Stephen Casper
Oliver Sourbut
Benjamin L. Edelman
Zhaowei Zhang
Mario Günther
Anton Korinek
Jose Hernandez-Orallo
Lewis Hammond
Eric J Bigelow
Alexander Pan
Lauro Langosco
Tomasz Korbak … (voir 18 de plus)
Heidi Chenyu Zhang
Ruiqi Zhong
Sean O hEigeartaigh
Gabriel Recchia
Giulio Corsi
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
Jakob Nicolaus Foerster
Florian Tramèr
He He
Atoosa Kasirzadeh
Yejin Choi
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (voir plus)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose
Foundational Challenges in Assuring Alignment and Safety of Large Language Models
Usman Anwar
Abulhair Saparov
Javier Rando
Daniel Paleka
Miles Turpin
Peter Hase
Ekdeep Singh Lubana
Erik Jenner
Stephen Casper
Oliver Sourbut
Benjamin L. Edelman
Zhaowei Zhang
Mario Günther
Anton Korinek
Jose Hernandez-Orallo
Lewis Hammond
Eric J Bigelow
Alexander Pan
Lauro Langosco
Tomasz Korbak … (voir 18 de plus)
Heidi Chenyu Zhang
Ruiqi Zhong
Sean O hEigeartaigh
Gabriel Recchia
Giulio Corsi
Markus Anderljung
Lilian Edwards
Danqi Chen
Samuel Albanie
Jakob Nicolaus Foerster
Florian Tramèr
He He
Atoosa Kasirzadeh
Yejin Choi
This work identifies 18 foundational challenges in assuring the alignment and safety of large language models (LLMs). These challenges are o… (voir plus)rganized into three different categories: scientific understanding of LLMs, development and deployment methods, and sociotechnical challenges. Based on the identified challenges, we pose