Publications

Surrogate Minimization: An Optimization Algorithm for Training Large Neural Networks with Model Parallelism
Reza Asad
Reza Babanezhad Harikandeh
Issam Hadj Laradji
Nicolas Roux
Sharan Vaswani
2023-10-25
NeurIPS.cc/2023/Workshop/OPT (poster)
openreview.net
openreview.net
The value of standards for health datasets in artificial intelligence-based applications
Anmol Arora
Joseph E. Alderman
Joanne Palmer
Shaswath Ganapathi
Elinor Laws
Melissa D. McCradden
Lauren Oakden-Rayner
Stephen R. Pfohl
Marzyeh Ghassemi
Francis McKay
Darren Treanor
Negar Rostamzadeh
Bilal Mateen
Jacqui Gath
Adewole O. Adebajo
Stephanie Kuku
Rubeta Matin
Katherine Heller
Elizabeth Sapey
Neil J. Sebire … (voir 4 de plus)
Heather Cole-Lewis
Melanie Calvert
Alastair Denniston
Xiaoxuan Liu
2023-10-25
Nature Medicine (publié)
doi.org
PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design
Chuanrui Wang
Bozitao Zhong
Zuobai Zhang
Narendra Chaudhary
Sanchit Misra
Jian Tang
Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years. However, a univers… (voir plus)ally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the
2023-10-24
NeurIPS.cc/2023/Workshop/AI4D3 (poster)
openreview.net
openreview.net
Enjeux de l’adaptation à la chaleur en ville et action publique : apports de l’interdisciplinarité et de la recherche-action - Cas de la métropole toulousaine
G. Bretagne
Julia Hidalgo
Sinda Haouès-Jouve
Guillaume Dumas
Lise Debrye
Aurélie Hanna
Valéry Masson
Le contexte législatif national, comme les attentes citoyennes exprimées pour plus d’informations et d’actions relatives aux enjeux cl… (voir plus)imatiques, ont progressivement incité à la territorialisation des politiques climatiques et énergétiques locales, ainsi qu’à l’émergence de l’enjeu d’adaptation climatique sur les territoires. Cette dynamique de spatialisation des enjeux climatiques trouve sa déclinaison à l’échelle de la métropole toulousaine depuis plus de 10 ans, du fait d’enjeux multiples sur le territoire : géographiques, climatiques et urbains. Les travaux de recherche menés localement autour des thématiques Ville, Environnement et Climat ont répondu au contexte favorable d’interdisciplinarité et de collaboration avec les acteurs urbains, soutenues par plusieurs appels à projets de recherche nationaux et européens. Deux objectifs majeurs sont affichés : coconstruire une connaissance afin de caractériser les enjeux climatiques et énergétiques propres au territoire toulousain, et proposer un accompagnement spécifique auprès des acteurs urbains pour mieux faire comprendre et objectiver les enjeux locaux, afin d’intégrer ces derniers dans les politiques et les actions publiques locales. Le présent article propose de revenir sur la synergie permise par cette collaboration, en s’attachant d’une part à présenter le processus de travail interdisciplinaire mis en place et, d’autre part, à montrer les productions de données et d’expertises qui en ont résulté.
2023-10-23
Climatologie (publié)
doi.org
Gradient Masked Averaging for Federated Learning
Irene Tenison
Sai Aravind Sreeramadas
Vaikkunth Mugunthan
Edouard Oyallon
Irina Rish
Eugene Belilovsky
Federated learning (FL) is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a u… (voir plus)nified global model without the need to share data amongst each other. A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms. Standard FL algorithms involve averaging of model parameters or gradient updates to approximate the global model at the server. However, we argue that in heterogeneous settings, averaging can result in information loss and lead to poor generalization due to the bias induced by dominant client gradients. We hypothesize that to generalize better across non-i.i.d datasets, the algorithms should focus on learning the invariant mechanism that is constant while ignoring spurious mechanisms that differ across clients. Inspired from recent works in Out-of-Distribution generalization, we propose a gradient masked averaging approach for FL as an alternative to the standard averaging of client updates. This aggregation technique for client updates can be adapted as a drop-in replacement in most existing federated algorithms. We perform extensive experiments on multiple FL algorithms with in-distribution, real-world, feature-skewed out-of-distribution, and quantity imbalanced datasets and show that it provides consistent improvements, particularly in the case of heterogeneous clients.
2023-10-23
Transactions on Machine Learning Research (accepté)
doi.org
openreview.net
Validation of ANG-1 and P-SEL as biomarkers of post-COVID-19 conditions using data from the Biobanque québécoise de la COVID-19 (BQC-19)
Eric Yamga
Antoine Soulé
Alain Piché
Amin Emad
Madeleine Durand
Simon Rousseau
2023-10-23
Clinical Proteomics (publié)
doi.org
Causal machine learning for single-cell genomics
Alejandro Tejada-Lapuerta
Paul Bertin
Stefan Bauer
Hananeh Aliee
Yoshua Bengio
Fabian J. Theis
2023-10-22
ArXiv (prépublication)
doi.org
arxiv.org
Distributional Robustness and Inequity Mitigation in Disaster Preparedness of Humanitarian Operations
Hongming Li
Erick Delage
Ning Zhu
Michael Pinedo
Shoufeng Ma
Problem definition: In this paper, we study a predisaster relief network design problem with uncertain demands. The aim is to determine the … (voir plus)prepositioning and reallocation of relief supplies. Motivated by the call of the International Federation of Red Cross and Red Crescent Societies (IFRC) to leave no one behind, we consider three important practical aspects of humanitarian operations: shortages, equity, and uncertainty. Methodology/results: We first employ a form of robust satisficing measure, which we call the shortage severity measure, to evaluate the severity of the shortage caused by uncertain demand in a context with limited distribution information. Because shortages often raise concerns about equity, we then formulate a mixed-integer lexicographic optimization problem with nonconvex objectives and design a new branch-and-bound algorithm to identify the exact solution. We also propose two approaches for identifying optimal postdisaster adaptable resource reallocation: an exact approach and a conservative approximation that is more computationally efficient. Our case study considers the 2010 Yushu earthquake, which occurred in northwestern China, and demonstrates the value of our methodology in mitigating geographical inequities and reducing shortages. Managerial implications: In our case study, we show that (i) incorporating equity in both predisaster deployment and postdisaster reallocation can produce substantially more equitable shortage prevention strategies while sacrificing only a reasonable amount of total shortage; (ii) increasing donations/budgets may not necessarily alleviate the shortage suffered by the most vulnerable individuals if equity is not fully considered; and (iii) exploiting disaster magnitude information when quantifying uncertainty can help alleviate geographical inequities caused by uncertain relief demands. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2016-05208], the National Natural Science Foundation of China [Grants 71971154, 72010107004, 72091214, and 72122015], and the Canada Research Chairs [Grant CRC-2018-00105]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1230 .
2023-10-22
Manufacturing & Service Operations Management (publié)
doi.org
Dosimetry of [18F]TRACK, the first PET tracer for imaging of TrkB/C receptors in humans
Alexander Thiel
Alexey Kostikov
Hailey Ahn
Youstina Daoud
Jean-Paul Soucy
Stephan Blinder
Carolin Jaworski
Carmen Wängler
Björn Wängler
Freimut Juengling
Shirin A. Enger
Ralf Schirrmacher
Background Reduced expression or impaired signalling of tropomyosin receptor kinases (Trk receptors) are found in a vast spectrum of CNS dis… (voir plus)orders. [^18F]TRACK is the first PET radioligand for TrkB/C with proven in vivo brain penetration and on-target specific signal. Here we report dosimetry data for [^18F]TRACK in healthy humans. 6 healthy participants (age 22–61 y, 3 female) were scanned on a General Electric Discovery PET/CT 690 scanner. [^18F]TRACK was synthesized with high molar activities (A_m = 250 ± 75 GBq/µmol), and a dynamic series of 12 whole-body scans were acquired after injection of 129 to 147 MBq of the tracer. Images were reconstructed with standard corrections using the manufacturer’s OSEM algorithm. Tracer concentration time-activity curves (TACs) were obtained using CT-derived volumes-of-interest. Organ-specific doses and the total effective dose were estimated using the Committee on Medical Internal Radiation Dose equation for adults and tabulated Source tissue values (S values). Results Average organ absorbed dose was highest for liver and gall bladder with 6.1E−2 (± 1.06E−2) mGy/MBq and 4.6 (± 1.18E−2) mGy/MBq, respectively. Total detriment weighted effective dose E_DW was 1.63E−2 ± 1.68E−3 mSv/MBq. Organ-specific TACs indicated predominantly hepatic tracer elimination. Conclusion Total and organ-specific effective doses for [^18F]TRACK are low and the dosimetry profile is similar to other ^18F-labelled radio tracers currently used in clinical settings.
2023-10-22
EJNMMI Radiopharmacy and Chemistry (publié)
doi.org
Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models
Alan Chan
Benjamin Bucknall
Herbie Bradley
David M. Krueger
2023-10-22
NeurIPS.cc/2023/Workshop/SoLaR (spotlight)
doi.org
openreview.net
A Novel Information-Theoretic Objective to Disentangle Representations for Fair Classification
Pierre Colombo
Nathan Noiry
Guillaume Staerman
Pablo Piantanida
2023-10-20
ArXiv (prépublication)
doi.org
arxiv.org
Fundamental Limits of Membership Inference Attacks on Machine Learning Models
Eric Aubinais
Elisabeth Gassiat
Pablo Piantanida
Membership inference attacks (MIA) can reveal whether a particular data point was part of the training dataset, potentially exposing sensiti… (voir plus)ve information about individuals. This article provides theoretical guarantees by exploring the fundamental statistical limitations associated with MIAs on machine learning models. More precisely, we first derive the statistical quantity that governs the effectiveness and success of such attacks. We then deduce that in a very general regression setting with overfitting algorithms, attacks may have a high probability of success. Finally, we investigate several situations for which we provide bounds on this quantity of interest. Our results enable us to deduce the accuracy of potential attacks based on the number of samples and other structural parameters of learning models. In certain instances, these parameters can be directly estimated from the dataset.
2023-10-19
ArXiv (prépublication)
doi.org
arxiv.org