Publications

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

2023-10-24

Clinical Proteomics (publié)

Causal machine learning for single-cell genomics

Alejandro Tejada-Lapuerta

Paul Bertin

Stefan Bauer

Hananeh Aliee

Yoshua Bengio

Fabian J. Theis

2023-10-23

ArXiv (prépublication)

Distributional Robustness and Inequity Mitigation in Disaster Preparedness of Humanitarian Operations

Hongming Li

Érick 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 .

2023-10-23

Manufacturing & Service Operations Management (publié)

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

2023-10-23

EJNMMI Radiopharmacy and Chemistry (publié)

Ghost on the Shell: An Expressive Representation of General 3D Shapes

Zhen Liu

Yao Feng

Yuliang Xiu

Weiyang Liu

Liam Paull

Michael J. Black

Bernhard Schölkopf

2023-10-23

ArXiv (prépublication)

Gradient Masked Averaging for Federated Learning

Irene Tenison

Sai Aravind Sreeramadas

Vaikkunth Mugunthan

Edouard Oyallon

Eugene Belilovsky

Irina Rish

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

TMLR (accepté)

Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models

Alan Chan

Benjamin Bucknall

Herbie Bradley

David Scott Krueger

2023-10-23

NeurIPS.cc/2023/Workshop/SoLaR (spotlight)

Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models

Alan Chan

Benjamin Bucknall

Herbie Bradley

David Scott Krueger

2023-10-23

NeurIPS.cc/2023/Workshop/SoLaR (spotlight)

"One-Size-Fits-All"? Examining Expectations around What Constitute"Fair"or"Good"NLG System Behaviors

Li Lucy

Su Lin Blodgett

Milad Shokouhi

Hanna M. Wallach

Alexandra Olteanu

Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to beh… (voir plus)ave identically for social groups, to adaptation, where behaviors should instead vary across them. To illuminate tensions around invariance and adaptation, we conduct five case studies, in which we perturb different types of identity-related language features (names, roles, locations, dialect, and style) in NLG system inputs. Through these cases studies, we examine people's expectations of system behaviors, and surface potential caveats of these contrasting yet commonly held assumptions. We find that motivations for adaptation include social norms, cultural differences, feature-specific information, and accommodation; in contrast, motivations for invariance include perspectives that favor prescriptivism, view adaptation as unnecessary or too difficult for NLG systems to do appropriately, and are wary of false assumptions. Our findings highlight open challenges around what constitute"fair"or"good"NLG system behaviors.

2023-10-23

ArXiv (prépublication)

STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent

Yewon Lee

Philip Huang

Yizhou Huang

Krishna Murthy

Andrew Zou Li

Fabian Damken

Eric Heiden

Kevin A. Smith

Derek Nowrouzezahrai

Fabio Ramos

Florian Shkurti

Carnegie-mellon University

M. I. O. Technology

Technische Universitat Darmstadt

Nvidia

McGill University

University of Sydney

Planning for many manipulation tasks, such as using tools or assembling parts, often requires both symbolic and geometric reasoning. Task an… (voir plus)d Motion Planning (TAMP) algorithms typically solve these problems by conducting a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. While performant, most existing algorithms are highly inefficient as their time complexity grows exponentially with the number of possible actions and objects. Additionally, they only find a single solution to problems in which many feasible plans may exist. To address these limitations, we propose a novel algorithm called Stein Task and Motion Planning (STAMP) that leverages parallelization and differentiable simulation to efficiently search for multiple diverse plans. STAMP relaxes discrete-and-continuous TAMP problems into continuous optimization problems that can be solved using variational inference. Our algorithm builds upon Stein Variational Gradient Descent, a gradient-based variational inference algorithm, and parallelized differentiable physics simulators on the GPU to efficiently obtain gradients for inference. Further, we employ imitation learning to introduce action abstractions that reduce the inference problem to lower dimensions. We demonstrate our method on two TAMP problems and empirically show that STAMP is able to: 1) produce multiple diverse plans in parallel; and 2) search for plans more efficiently compared to existing TAMP baselines.

2023-10-23

robot-learning.org/CoRL/2023/Workshop/LEAP (poster)

Towards contrast-agnostic soft segmentation of the spinal cord

Sandrine Bédard

Enamundram Naga Karthik

Charidimos Tsagkas

Emanuele Pravatà

Cristina Granziera

Andrew C. Smith

Kenneth Arnold Weber

Julien Cohen-Adad

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and … (voir plus)monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord. Using the Spine Generic Public Database of healthy participants (

2023-10-23

ArXiv (prépublication)