Publications

Fairness-Aware Structured Pruning in Transformers
Abdelrahman Zayed
Goncalo Mordido
Samira Shabanian
Ioana Baldini
A. Chandar
2023-12-23
ArXiv (preprint)
doi.org
arxiv.org
When Nash Meets Stackelberg
Margarida Carvalho
Gabriele Dragotto
Felipe Feijoo
Andrea Lodi
Sriram Sankaranarayanan
2023-12-21
Management Science (published)
doi.org
arxiv.org
CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data
Louis Mullie
Jonathan Afilalo
Patrick Archambault
Rima Bouchakri
Kip Brown
David L. Buckeridge
Yiorgos Alexandros Cavayas
Alexis F. Turgeon
Denis Martineau
François Lamontagne
Martine Lebrasseur
Renald Lemieux
Jeffrey Li
Michaël Sauthier
Pascal St-Onge
An Tang
William Witteman
Michael Chassé
Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing appr… (see more)oaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.
2023-12-20
J. Am. Medical Informatics Assoc. (published)
doi.org
A landmark environmental law looks ahead
Robert L. Fischman
J. B. Ruhl
Brenna R. Forester
Tanya M. Lama
Marty Kardos
Grethel Aguilar Rojas
Nicholas A. Robinson
Patrick D. Shirey
Gary A. Lamberti
Amy W. Ando
Stephen Palumbi
Michael Wara
Mark W. Schwartz
Matthew A. Williamson
Tanya Berger-Wolf
Sara Beery
David Rolnick
Justin Kitzes
David Thau
Devis Tuia … (see 8 more)
Daniel Rubenstein
Caleb R. Hickman
Julie Thorstenson
Gregory E. Kaebnick
James P. Collins
Athmeya Jayaram
Thomas Deleuil
Ying Zhao
In late December 1973, the United States enacted what some would come to call “the pitbull of environmental laws.” In the 50 years since… (see more), the formidable regulatory teeth of the Endangered Species Act (ESA) have been credited with considerable successes, obliging agencies to draw upon the best available science to protect species and habitats. Yet human pressures continue to push the planet toward extinctions on a massive scale. With that prospect looming, and with scientific understanding ever changing, Science invited experts to discuss how the ESA has evolved and what its future might hold. —Brad Wible
2023-12-20
Science (published)
doi.org
Extended Lyman-alpha emission towards the SPT2349-56 protocluster at $z=4.3$
Yordanka Apostolovski
Manuel Aravena
Timo Anguita
Matthieu Béthermin
James R. Burgoyne
Scott Chapman
C. Breuck
Anthony R Gonzalez
Max Gronke
Lucia Guaita
Yashar Hezaveh
Ryley Hill
Sreevani Jarugula
E. Johnston
M. Malkan
Desika Narayanan
Cassie Reuter
Manuel Solimano
Justin Spilker
Nikolaus Sulzenauer … (see 3 more)
Joaquin Vieira
David Vizgan
Axel Weiß
Deep spectroscopic surveys with the Atacama Large Millimeter/submillimeter Array (ALMA) have revealed that some of the brightest infrared so… (see more)urces in the sky correspond to concentrations of submillimeter galaxies (SMGs) at high redshift. Among these, the SPT2349-56 protocluster system is amongst the most extreme examples given its high source density and integrated star formation rate. We conducted a deep Lyman-alpha line emission survey around SPT2349-56 using the Multi-Unit Spectroscopic Explorer (MUSE) at the Very Large Telescope (VLT) in order to characterize this uniquely dense environment. Taking advantage of the deep three-dimensional nature of this survey, we performed a sensitive search for Lyman-alpha emitters (LAEs) toward the core and northern extension of the protocluster, which correspond to the brightest infrared regions in this field. Using a smoothed narrowband image extracted from the MUSE datacube around the protocluster redshift, we searched for possible extended structures. We identify only three LAEs at
2023-12-19
Astronomy & Astrophysics (published)
doi.org
arxiv.org
Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game
Ardavan S. Nobandegani
Irina Rish
Thomas Shultz
Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theore… (see more)tical analysis of the
2023-12-19
ArXiv (preprint)
doi.org
arxiv.org
Diagnosis and management of autoimmune diseases in the ICU
Guillaume Dumas
Yaseen M. Arabi
Raquel Bartz
Otavio Ranzani
Franziska Scheibe
Michael Darmon
Julie Helms
2023-12-18
Intensive Care Medicine (published)
doi.org
An empirical study of testing machine learning in the wild
Moses Openja
Foutse Khomh
Armstrong Foundjem
Zhen Ming
Mouna Abidi
Ahmed E. Hassan
Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive natur… (see more)e, ensuring the quality of these systems remains a significant challenge for the research community. Unlike traditional software built deductively by writing explicit rules, ML/DL systems infer rules from training data. Recent research in ML/DL quality assurance has adapted concepts from traditional software testing, such as mutation testing, to improve reliability. However, it is unclear if these proposed testing techniques are adopted in practice, or if new testing strategies have emerged from real-world ML deployments. There is little empirical evidence about the testing strategies. To fill this gap, we perform the first fine-grained empirical study on ML testing in the wild to identify the ML properties being tested, the testing strategies, and their implementation throughout the ML workflow. We conducted a mixed-methods study to understand ML software testing practices. We analyzed test files and cases from 11 open-source ML/DL projects on GitHub. Using open coding, we manually examined the testing strategies, tested ML properties, and implemented testing methods to understand their practical application in building and releasing ML/DL software systems. Our findings reveal several key insights: 1.) The most common testing strategies, accounting for less than 40%, are Grey-box and White-box methods, such as Negative Testing, Oracle Approximation and Statistical Testing. 2.) A wide range of 17 ML properties are tested, out of which only 20% to 30% are frequently tested, including Consistency, Correctness}, and Efficiency. 3.) Bias and Fairness is more tested in Recommendation, while Security & Privacy is tested in Computer Vision (CV) systems, Application Platforms, and Natural Language Processing (NLP) systems.
2023-12-18
ArXiv (preprint)
doi.org
arxiv.org
Gemini: A Family of Highly Capable Multimodal Models
Gemini Team Google Rohan Anil
Sebastian Borgeaud
Yonghui Wu
Jean-Baptiste Alayrac
Jiahui Yu
Radu Soricut
J. Schalkwyk
Andrew M. Dai
Anja Hauth
Katie Millican
David Silver
Slav Petrov
Melvin Johnson
Ioannis Antonoglou
Julian Schrittwieser
Amelia Glaese
Jilin Chen
Emily Pitler
Timothy P Lillicrap
Angeliki Lazaridou … (see 480 more)
Orhan Firat
James L. Molloy
Michael Acheson Isard
Paul R. Barham
Tom Hennigan
Benjamin Lee
Fabio Viola
Malcolm Reynolds
Yuanzhong Xu
Ryan Doherty
Eli Collins
Clemens Meyer
Eliza Rutherford
Erica Moreira
Kareem W. Ayoub
Megha Goel
George Tucker
Enrique Piqueras
M. Krikun
Iain Barr
Nikolay Savinov
Ivo Danihelka
Becca Roelofs
Anais White
Anders Johan Andreassen
Tamara von Glehn
Laksh-man Yagati
Mehran Kazemi
Lucas Gonzalez
Misha Khalman
Jakub Sygnowski
Alexandre Fréchette
Charlotte Smith
Laura Culp
Lev Proleev
Yi Luan
X. T. Chen
James Lottes
Nathan Schucher
Federico Lebron
Alban Rrustemi
Natalie Clay
Phil Crone
Tomas Kocisky
Jeffrey Zhao
Bartek Perz
Dian Yu
Heidi Howard
Adam E. Bloniarz
Jack W. Rae
Han Lu
Laurent Sifre
Marcello Maggioni
Fred Alcober
Dan Garrette
Megan Barnes
Shantanu Thakoor
Jacob Austin
Gabriel Barth-Maron
William Wong
Rishabh Joshi
Rahma Chaabouni
Deeni Fatiha
Arun Ahuja
Ruibo Liu
Yunxuan Li
Sarah Cogan
Jeremy Chen
Chao Jia
Chenjie Gu
Qiao Zhang
Jordan Grimstad
Ale Jakse Hartman
Martin J. Chadwick
Gaurav Singh Tomar
Xavier Garcia
Evan Senter
Emanuel Taropa
Thanumalayan Sankaranarayana Pillai
Jacob Devlin
Michael Laskin
Diego de Las Casas
Dasha Valter
Connie Tao
Lorenzo Blanco
Adrià Puigdomènech Badia
David Reitter
Mianna Chen
Jenny Brennan
Clara E. Rivera
Sergey Brin
Shariq Iqbal
Gabriela Surita
Jane Labanowski
Abhishek Rao
Stephanie Winkler
Emilio Parisotto
Yiming Gu
Kate Olszewska
Yujing Zhang
Ravichandra Addanki
Antoine Miech
Annie Louis
Laurent El Shafey
Denis Teplyashin
Geoff Brown
Elliot Catt
Nithya Attaluri
Jan Balaguer
Jackie Xiang
Pidong Wang
Zoe Ashwood
Anton Briukhov
Alex Webson
Sanjay Ganapathy
Smit Sanghavi
Ajay Kannan
Ming-Wei Chang
Axel Stjerngren
Josip Djolonga
Yuting Sun
Ankur Bapna
Matthew Aitchison
Pedram Pejman
Henryk Michalewski
Tianhe Yu
Cindy Wang
J Christopher Love
Junwhan Ahn
Dawn Bloxwich
Kehang Han
Peter Conway Humphreys
Thibault Sellam
James Bradbury
Varun Godbole
Sina Samangooei
Bogdan Damoc
Alex Kaskasoli
S'ebastien M. R. Arnold
Vijay Vasudevan
Shubham Agrawal
Jason Riesa
Dmitry Lepikhin
Richard Tanburn
Srivatsan Srinivasan
Hyeontaek Lim
Sarah Hodkinson
Pranav Shyam
Johan Ferret
Steven Hand
Ankush Garg
T. Paine
Jian Li
Yujia Li
Minh Giang
Alexander Neitz
Zaheer Abbas
Sarah York
Machel Reid
Elizabeth Cole
Aakanksha Chowdhery
Dipanjan Das
Dominika Rogozi'nska
Vitaly Nikolaev
Pablo G. Sprechmann
Zachary Nado
Lukáš Žilka
Flavien Prost
Luheng He
Marianne Monteiro
Gaurav Mishra
Christoper A. Welty
Joshua Newlan
Dawei Jia
Miltiadis Allamanis
Clara Huiyi Hu
Raoul de Liedekerke
Justin Gilmer
Carl Saroufim
Shruti Rijhwani
Shaobo Hou
Disha Shrivastava
Anirudh Baddepudi
Alex Goldin
Adnan Ozturel
Albin Cassirer
Yunhan Xu
Daniel Sohn
Devendra Singh Sachan
Reinald Kim Amplayo
Craig Swanson
Dessie Petrova
Shashi Narayan
Arthur Guez
Siddhartha Brahma
Jessica Landon
Miteyan Patel
Ruizhe Zhao
Kevin Villela
Luyu Wang
Wenhao Jia
Matthew Rahtz
Mai Gim'enez
Legg Yeung
Hanzhao Lin
James Keeling
Petko Georgiev
Diana Mincu
Boxi Wu
Salem Haykal
Rachel Saputro
Kiran N. Vodrahalli
James Qin
Zeynep Cankara
Abhanshu Sharma
Nicholas Fernando
Will Hawkins
Behnam Neyshabur
Solomon Kim
Adrian Hutter
Priyanka Agrawal
Alex Castro-Ros
George van den Driessche
Tao Wang
Fan Yang
Shuo-yiin Chang
Paul Komarek
Ross McIlroy
Mario Luvci'c
Guodong Zhang
Wael Farhan
Michael Sharman
Paul Natsev
Paul Michel
Yong Cheng
Yamini Bansal
Siyuan Qiao
Kris Cao
Siamak Shakeri
Christina Butterfield
Justin Chung
Paul Kishan Rubenstein
Shivani Agrawal
Arthur Mensch
Kedar Soparkar
Karel Lenc
Timothy Chung
Aedan Pope
Lorenzo Maggiore
Jackie Kay
Priya Jhakra
Shibo Wang
Joshua Maynez
Mary Phuong
Taylor Tobin
Andrea Tacchetti
Maja Trebacz
Kevin Robinson
Yash Katariya
Sebastian Riedel
Paige Bailey
Kefan Xiao
Nimesh Ghelani
Lora Aroyo
Ambrose Slone
Neil Houlsby
Xuehan Xiong
Zhen Yang
Elena Gribovskaya
Jonas Adler
Mateo Wirth
Lisa Lee
Music Li
Thais Kagohara
Jay Pavagadhi
Sophie Bridgers
Anna Bortsova
Sanjay Ghemawat
Zafarali Ahmed
Tianqi Liu
Richard Powell
Vijay Bolina
Mariko Iinuma
Polina Zablotskaia
James Besley
Da-Woon Chung
Timothy Dozat
Ramona Comanescu
Xiance Si
Jeremy Greer
Guolong Su
M. Polacek
Raphael Lopez Kaufman
Simon Tokumine
Hexiang Hu
Elena Buchatskaya
Yingjie Miao
Mohamed Elhawaty
Aditya Siddhant
Nenad Tomasev
Jinwei Xing
Christina Greer
Helen Miller
Shereen Ashraf
Aurko Roy
Zizhao Zhang
Ada Ma
Angelos Filos
Milos Besta
Rory Blevins
Ted Klimenko
Chih-Kuan Yeh
Soravit Changpinyo
Jiaqi Mu
Oscar Chang
Mantas Pajarskas
Carrie Muir
Vered Cohen
Charline Le Lan
Krishna S Haridasan
Amit Marathe
Steven Stenberg Hansen
Sholto Douglas
Rajkumar Samuel
Mingqiu Wang
Sophia Austin
Chang Lan
Jiepu Jiang
Justin Chiu
Jaime Alonso Lorenzo
Lars Lowe Sjosund
S'ebastien Cevey
Zach Gleicher
Thi Avrahami
Anudhyan Boral
Hansa Srinivasan
Vittorio Selo
Rhys May
Konstantinos Aisopos
L'eonard Hussenot
Livio Baldini Soares
Kate Baumli
Michael B. Chang
Adria Recasens
Benjamin Caine
Alexander Pritzel
Filip Pavetic
Fabio Pardo
Anita Gergely
Justin Frye
Vinay Venkatesh Ramasesh
Dan Horgan
Kartikeya Badola
Nora Kassner
Subhrajit Roy
Ethan Dyer
V'ictor Campos
Alex Tomala
Yunhao Tang
Dalia El Badawy
Elspeth White
Basil Mustafa
Oran Lang
Abhishek Jindal
Sharad Mandyam Vikram
Zhitao Gong
Sergi Caelles
Ross Hemsley
Gregory Thornton
Fangxiaoyu Feng
Wojciech Stokowiec
Ce Zheng
Phoebe Thacker
cCauglar Unlu
Zhishuai Zhang
Mohammad Saleh
James Svensson
Maxwell L. Bileschi
Piyush Patil
Ankesh Anand
Roman Ring
Katerina Tsihlas
Arpi Vezer
Marco Selvi
Toby Shevlane
Mikel Rodriguez
Tom Kwiatkowski
Samira Daruki
Keran Rong
Allan Dafoe
Nicholas Fitzgerald
Keren Gu-Lemberg
Mina Khan
Lisa Anne Hendricks
Marie Pellat
Vladimir Feinberg
James Cobon-Kerr
Tara N. Sainath
Maribeth Rauh
Sayed Hadi Hashemi
Richard Ives
Yana Hasson
YaGuang Li
Eric Noland
Yuan Cao
Nathan Byrd
Le Hou
Qingze Wang
Thibault Sottiaux
Michela Paganini
Jean-Baptiste Lespiau
Alexandre Moufarek
Samer Hassan
Kaushik Shivakumar
Joost Van Amersfoort
Amol Mandhane
Pratik M. Joshi
Anirudh Goyal
Matthew Tung
Andy Brock
Hannah Rachel Sheahan
Vedant Misra
Cheng Li
Nemanja Raki'cevi'c
Mostafa Dehghani
Fangyu Liu
Sid Mittal
Junhyuk Oh
Seb Noury
Eren Sezener
Fantine Huot
Matthew Lamm
Nicola De Cao
Charlie Chen
Gamaleldin Elsayed
Ed Huai-hsin Chi
Mahdis Mahdieh
Ian F. Tenney
Nan Hua
Ivan Petrychenko
Patrick Kane
Dylan Scandinaro
Rishub Jain
Jonathan Uesato
Romina Datta
Adam Sadovsky
Oskar Bunyan
Dominik Rabiej
Shimu Wu
John Zhang
Gautam Vasudevan
Edouard Leurent
Mahmoud Alnahlawi
Ionut-Razvan Georgescu
Nan Wei
Ivy Zheng
Betty Chan
Pam G Rabinovitch
Piotr Stańczyk
Ye Zhang
David Steiner
Subhajit Naskar
Michael Azzam
Matthew Johnson
Adam Paszke
Chung-Cheng Chiu
Jaume Sanchez Elias
Afroz Mohiuddin
Faizan Muhammad
Jin Miao
Andrew Lee
Nino Vieillard
Sahitya Potluri
Jane Park
Elnaz Davoodi
Jiageng Zhang
Jeff Stanway
Drew Garmon
Abhijit Karmarkar
Zhe Dong
2023-12-18
ArXiv (preprint)
doi.org
arxiv.org
Giant Correlated Gap and Possible Room-Temperature Correlated States in Twisted Bilayer MoS_{2}.
Fanfan Wu
Qiaoling Xu
Qinqin Wang
Yanbang Chu
Li Li
Jian Tang
Jieying Liu
Jinpeng Tian
Yiru Ji
Le Liu
Yalong Yuan
Zhiheng Huang
Jiaojiao Zhao
Xiaozhou Zan
Kenji Watanabe
Takashi Taniguchi
Dongxia Shi
Gangxu Gu
Yang Xu
Lede Xian … (see 3 more)
Wei Yang
Luojun Du
Guangyu Zhang
Moiré superlattices have emerged as an exciting condensed-matter quantum simulator for exploring the exotic physics of strong electronic co… (see more)rrelations. Notable progress has been witnessed, but such correlated states are achievable usually at low temperatures. Here, we report evidence of possible room-temperature correlated electronic states and layer-hybridized SU(4) model simulator in AB-stacked MoS_{2} homobilayer moiré superlattices. Correlated insulating states at moiré band filling factors v=1, 2, 3 are unambiguously established in twisted bilayer MoS_{2}. Remarkably, the correlated electronic state at v=1 shows a giant correlated gap of ∼126  meV and may persist up to a record-high critical temperature over 285 K. The realization of a possible room-temperature correlated state with a large correlated gap in twisted bilayer MoS_{2} can be understood as the cooperation effects of the stacking-specific atomic reconstruction and the resonantly enhanced interlayer hybridization, which largely amplify the moiré superlattice effects on electronic correlations. Furthermore, extreme large nonlinear Hall responses up to room temperature are uncovered near correlated electronic states, demonstrating the quantum geometry of moiré flat conduction band.
2023-12-17
Physical Review Letters (published)
doi.org
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Shengchao Liu
Weili Nie
Chengpeng Wang
Jiarui Lu
Zhuoran Qiao
Ling Liu
Jian Tang
Chaowei Xiao
Animashree Anandkumar
There is increasing adoption of artificial intelligence in drug discovery. However, existing studies use machine learning to mainly utilize … (see more)the chemical structures of molecules but ignore the vast textual knowledge available in chemistry. Incorporating textual knowledge enables us to realize new drug design objectives, adapt to text-based instructions and predict complex biological activities. Here we present a multi-modal molecule structure–text model, MoleculeSTM, by jointly learning molecules’ chemical structures and textual descriptions via a contrastive learning strategy. To train MoleculeSTM, we construct a large multi-modal dataset, namely, PubChemSTM, with over 280,000 chemical structure–text pairs. To demonstrate the effectiveness and utility of MoleculeSTM, we design two challenging zero-shot tasks based on text instructions, including structure–text retrieval and molecule editing. MoleculeSTM has two main properties: open vocabulary and compositionality via natural language. In experiments, MoleculeSTM obtains the state-of-the-art generalization ability to novel biochemical concepts across various benchmarks. Machine learning methods in cheminformatics have made great progress in using chemical structures of molecules, but a large portion of textual information remains scarcely explored. Liu and colleagues trained MoleculeSTM, a foundation model that aligns the structure and text modalities through contrastive learning, and show its utility on the downstream tasks of structure–text retrieval, text-guided editing and molecular property prediction.
2023-12-17
Nature Machine Intelligence (unknown)
doi.org
arxiv.org
Unagi: Deep Generative Model for Deciphering Cellular Dynamics and In-Silico Drug Discovery in Complex Diseases
Jun Ding
Yumin Zheng
Jonas C. Schupp
Taylor S Adams
Geremy Clair
Aurelien Justet
Farida Ahangari
Xiting Yan
Paul Hansen
Marianne Carlon
Emanuela Cortesi
Marie Vermant
Robin Vos
De Sadeleer J Laurens
Ivan O Rosas
Ricardo Pineda
John Sembrat
Melanie Königshoff
John E McDonough
Bart M. Vanaudenaerde … (see 2 more)
Wim A Wuyts
Naftali Kaminski
Human diseases are characterized by intricate cellular dynamics. Single-cell sequencing provides critical insights, yet a persistent gap rem… (see more)ains in computational tools for detailed disease progression analysis and targeted in-silico drug interventions. Here, we introduce UNAGI, a deep generative neural network tailored to analyze time-series single-cell transcriptomic data. This tool captures the complex cellular dynamics underlying disease progression, enhancing drug perturbation modeling and discovery. When applied to a dataset from patients with Idiopathic Pulmonary Fibrosis (IPF), UNAGI learns disease-informed cell embeddings that sharpen our understanding of disease progression, leading to the identification of potential therapeutic drug candidates. Validation via proteomics reveals the accuracy of UNAGI’s cellular dynamics analyses, and the use of the Fibrotic Cocktail treated human Precision-cut Lung Slices confirms UNAGI’s predictions that Nifedipine, an antihypertensive drug, may have antifibrotic effects on human tissues. UNAGI’s versatility extends to other diseases, including a COVID dataset, demonstrating adaptability and confirming its broader applicability in decoding complex cellular dynamics beyond IPF, amplifying its utility in the quest for therapeutic solutions across diverse pathological landscapes.
2023-12-17
Research Square (published)
doi.org