Publications

CD3ζ ITAMs enable ligand discrimination and antagonism by inhibiting TCR signaling in response to low-affinity peptides
Guillaume Gaud
Sooraj R. Achar
François X. P. Bourassa
John S. Davies
Teri Hatzihristidis
Seeyoung Choi
Taisuke Kondo
Selamawit Gossa
Jan Lee
Paul Juneau
Naomi Taylor
Christian S. Hinrichs
Dorian B. McGavern
Paul François
Grégoire Altan-Bonnet
Paul E. Love
2023-11-09
Nature Immunology (publié)
doi.org
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize
Ryan D'Orazio
Nicolas Loizou
Issam Hadj Laradji
Ioannis Mitliagkas
We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization. I… (voir plus)n relatively smooth convex optimization we provide new convergence guarantees for SMD with a constant stepsize. For smooth convex optimization we propose a new adaptive stepsize scheme --- the mirror stochastic Polyak stepsize (mSPS). Notably, our convergence results in both settings do not make bounded gradient assumptions or bounded variance assumptions, and we show convergence to a neighborhood that vanishes under interpolation. Consequently, these results correspond to the first convergence guarantees under interpolation for the exponentiated gradient algorithm for fixed or adaptive stepsizes. mSPS generalizes the recently proposed stochastic Polyak stepsize (SPS) (Loizou et al. 2021) to mirror descent and remains both practical and efficient for modern machine learning applications while inheriting the benefits of mirror descent. We complement our results with experiments across various supervised learning tasks and different instances of SMD, demonstrating the effectiveness of mSPS.
2023-11-08
TMLR (accepté)
openreview.net
openreview.net
Capture the Flag: Uncovering Data Insights with Large Language Models
Issam Hadj Laradji
Perouz Taslakian
Sai Rajeswar
Valentina Zantedeschi
Alexandre Lacoste
Nicolas Chapados
David Vazquez
Chris Pal
Alexandre Drouin
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (publié)
doi.org
openreview.net
Multi-resolution Time-Series Transformer for Long-term Forecasting
Yitian Zhang
Liheng Ma
Soumyasundar Pal
Yingxue Zhang
Mark Coates
2023-11-07
ArXiv (prépublication)
doi.org
arxiv.org
The Unsolved Challenges of LLMs as Generalist Web Agents: A Case Study
Rim Assouel
Tom Marty
Massimo Caccia
Issam Hadj Laradji
Alexandre Drouin
Sai Rajeswar
Hector Palacios
Quentin Cappart
David Vazquez
Nicolas Chapados
Maxime Gasse
Alexandre Lacoste
2023-11-07
NeurIPS.cc/2023/Workshop/FMDM (publié)
openreview.net
openreview.net
30×30 biodiversity gains rely on national coordination
Isaac Eckert
Andrea Brown
Dominique Caron
Federico Riva
Laura J. Pollock
2023-11-06
Nature Communications (publié)
doi.org
Coordination among leaf and fine root traits across a strong natural soil fertility gradient
Xavier Guilbeault-Mayers
Hans Lambers
Étienne Laliberté
2023-11-05
bioRxiv (prépublication)
doi.org
Player-Guided AI outperforms standard AI in Sequence Alignment Puzzles
Renata Mutalova
Roman Sarrazin-Gendron
Parham Ghasemloo Gheidari
Eddie Cai
Gabriel Richard
Sébastien Caisse
Rob Knight
Mathieu Blanchette
Attila Szantner
Jérôme Waldispühl
2023-11-05
International Conference on Climate Informatics (publié)
doi.org
The feature landscape of visual cortex
Rudi Tong
Ronan da Silva
Dongyan Lin
Arna Ghosh
James Wilsenach
Erica Cianfarano
Pouya Bashivan
Blake Richards
Stuart Trenholm
Understanding computations in the visual system requires a characterization of the distinct feature preferences of neurons in different visu… (voir plus)al cortical areas. However, we know little about how feature preferences of neurons within a given area relate to that area’s role within the global organization of visual cortex. To address this, we recorded from thousands of neurons across six visual cortical areas in mouse and leveraged generative AI methods combined with closed-loop neuronal recordings to identify each neuron’s visual feature preference. First, we discovered that the mouse’s visual system is globally organized to encode features in a manner invariant to the types of image transformations induced by self-motion. Second, we found differences in the visual feature preferences of each area and that these differences generalized across animals. Finally, we observed that a given area’s collection of preferred stimuli (‘own-stimuli’) drive neurons from the same area more effectively through their dynamic range compared to preferred stimuli from other areas (‘other-stimuli’). As a result, feature preferences of neurons within an area are organized to maximally encode differences among own-stimuli while remaining insensitive to differences among other-stimuli. These results reveal how visual areas work together to efficiently encode information about the external world.
2023-11-05
bioRxiv (prépublication)
doi.org
Score-Based Likelihood Characterization for Inverse Problems in the Presence of Non-Gaussian Noise
Ronan Legin
Alexandre Adam
Yashar Hezaveh
Laurence Perreault-Levasseur
Likelihood analysis is typically limited to normally distributed noise due to the difficulty of determining the probability density function… (voir plus) of complex, high-dimensional, non-Gaussian, and anisotropic noise. This work presents Score-based LIkelihood Characterization (SLIC), a framework that resolves this issue by building a data-driven noise model using a set of noise realizations from observations. We show that the approach produces unbiased and precise likelihoods even in the presence of highly non-Gaussian correlated and spatially varying noise. We use diffusion generative models to estimate the gradient of the probability density of noise with respect to data elements. In combination with the Jacobian of the physical model of the signal, we use Langevin sampling to produce independent samples from the unbiased likelihood. We demonstrate the effectiveness of the method using real data from the Hubble Space Telescope and James Webb Space Telescope.
2023-11-03
NeurIPS.cc/2023/Workshop/Deep_Inverse (poster)
openreview.net
openreview.net
Empowering Clinicians with MeDT: A Framework for Sepsis Treatment
Aamer Abdul Rahman
Pranav Agarwal
Vincent Michalski
Rita Noumeir
Samira Ebrahimi Kahou
2023-11-02
NeurIPS.cc/2023/Workshop/GCRL (publié)
openreview.net
openreview.net
Goal Misgeneralization as Implicit Goal Conditioning
Diego Dorn
Neel Alex
David Scott Krueger
2023-11-02
NeurIPS.cc/2023/Workshop/GCRL (publié)
openreview.net
openreview.net