Publications

Assessing Critical Thinking Skills in Data Literacy: A Digital Performance Assessment
Ying Cui
Xiaoxiao Liu
Fu Chen
Alina Lutsyk
Jaqueline P. Leighton
Maria Cutumisu
2025-04-15
Zeitschrift für Pädagogik (publié)
doi.org
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
Arjun Subramonian
Elvis Dohmatob
2025-04-14
ArXiv (prépublication)
arxiv.org
arxiv.org
IL1RAP is an immunotherapeutic target for normal karyotype triple-mutated acute myeloid leukemia
Arnaud Metois
Marie-Eve Bordeleau
Louis Theret
Azadeh Hajmirza
Ossama Moujaber
Jean-Francois Spinella
Jalila Chagraoui
Nadine Mayotte
Isabel Boivin
Éric Audemard
Léo Aubert
Véronique Lisi
Banafsheh Khakipoor
Azer Farah
Eric Bonneil
Alma Robert
Julie Lippens
Anna Moraitis
Francois Béliveau
Albert Feghaly … (voir 10 de plus)
Geneviève Boucher
Richard Marcotte
Patrick Gendron
Pierre Thibault
Sébastien Lemieux
Guillaume Richard-Carpentier
Vincent-Philippe Lavallee
Josée Hébert
Philippe Roux
Guy Sauvageau
2025-04-14
Biomarker Research (publié)
doi.org
Logging requirement for continuous auditing of responsible machine learning-based applications
Patrick Loic Foalem
Leuson Da Silva
Foutse Khomh
Heng Li
Ettore Merlo
2025-04-14
Empirical Software Engineering (publié)
doi.org
arxiv.org
Open Problems in Technical AI Governance
Anka Reuel
Benjamin Bucknall
Stephen Casper
Timothy Fist
Lisa Soder
Onni Aarne
Lewis Hammond
Lujain Ibrahim
Alan Chan
Peter Wills
Markus Anderljung
Ben Garfinkel
Lennart Heim
Andrew Trask
Gabriel Mukobi
Rylan Schaeffer
Mauricio Baker
Sara Hooker
Irene Solaiman
Sasha Luccioni … (voir 14 de plus)
Alexandra Luccioni
Nitarshan Rajkumar
Nicolas Moës
Jeffrey Ladish
David Bau
Paul Bricman
Neel Guha
Jessica Newman
Yoshua Bengio
Tobin South
Alex Pentland
Sanmi Koyejo
Mykel Kochenderfer
Robert Trager
AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the… (voir plus) barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technical analysis and tools for supporting the effective governance of AI, seeks to address such challenges. It can help to (a) identify areas where intervention is needed, (b) identify and assess the efficacy of potential governance actions, and (c) enhance governance options by designing mechanisms for enforcement, incentivization, or compliance. In this paper, we explain what technical AI governance is, why it is important, and present a taxonomy and incomplete catalog of its open problems. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
2025-04-14
TMLR (accepté)
doi.org
openreview.net
Predicting College Enrollment for Low-Socioeconomic-Status Students Using Machine Learning Approaches
Surina He
Mehrdad Yousefpoori-Naeim
Ying Cui
Maria Cutumisu
2025-04-12
Big Data and Cognitive Computing (publié)
doi.org
Semantic Commit: Helping Users Update Intent Specifications for AI Memory at Scale
Priyan Vaithilingam
Munyeong Kim
Frida-Cecilia Acosta-Parenteau
Daniel Lee
Amine Mhedhbi
Elena L. Glassman
Ian Arawjo
2025-04-12
ArXiv (prépublication)
arxiv.org
arxiv.org
BindGPT: A Scalable Framework for 3D Molecular Design via Language Modeling and Reinforcement Learning
Artem Zholus
Maksim Kuznetsov
Roman Schutski
Shayakhmetov Rim
Daniil Polykovskiy
Sarath Chandar
Alex Zhavoronkov
Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding … (voir plus)of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample.
2025-04-11
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring
Nikolaos Ioannis Bountos
Arthur Ouaknine
Ioannis Papoutsis
David Rolnick
2025-04-11
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
Genetic modulation of brain dynamics in neurodevelopmental disorders: the impact of copy number variations on resting-state EEG
Adrien Dubois
Elisabeth Audet-Duchesne
Inga Sophia Knoth
Charles-Olivier Martin
Khadije Jizi
Petra Tamer
Nadine Younis
Sébastien Jacquemont
Guillaume Dumas
Sarah Lippé
2025-04-11
Translational Psychiatry (publié)
doi.org
A Layer Selection Approach to Test Time Adaptation
Sabyasachi Sahoo
Mostafa ElAraby
Jonas Ngnawe
Yann Batiste Pequignot
Frederic Precioso
Christian Gagné
Test Time Adaptation (TTA) addresses the problem of distribution shift by adapting a pretrained model to a new domain during inference. When… (voir plus) faced with challenging shifts, most methods collapse and perform worse than the original pretrained model. In this paper, we find that not all layers are equally receptive to the adaptation, and the layers with the most misaligned gradients often cause performance degradation. To address this, we propose GALA, a novel layer selection criterion to identify the most beneficial updates to perform during test time adaptation. This criterion can also filter out unreliable samples with noisy gradients. Its simplicity allows seamless integration with existing TTA loss functions, thereby preventing degradation and focusing adaptation on the most trainable layers. This approach also helps to regularize adaptation to preserve the pretrained features, which are crucial for handling unseen domains. Through extensive experiments, we demonstrate that the proposed layer selection framework improves the performance of existing TTA approaches across multiple datasets, domain shifts, model architectures, and TTA losses.
2025-04-11
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
openreview.net
StarVector: Generating Scalable Vector Graphics Code from Images and Text
Juan A. Rodriguez
Abhay Puri
Shubham Agarwal
Issam Hadj Laradji
Pau Rodriguez
Sai Rajeswar
David Vazquez
Chris Pal
Marco Pedersoli
2025-04-11
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org