Publications

Responsible AI Research Needs Impact Statements Too
A.R. Olteanu
Michael Ekstrand
Carlos Castillo
Jina Suh
All types of research, development, and policy work can have unintended, adverse consequences - work in responsible artificial intelligence … (see more)(RAI), ethical AI, or ethics in AI is no exception.
2023-11-19
ArXiv (preprint)
doi.org
arxiv.org
Substituting Data Annotation with Balanced Neighbourhoods and Collective Loss in Multi-label Text Classification
Muberra Ozmen
Joseph Cotnareanu
Mark J. Coates
Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text, and has a wide range of application domains… (see more). Most existing approaches require an enormous amount of annotated data to learn a classifier and/or a set of well-defined constraints on the label space structure, such as hierarchical relations which may be complicated to provide as the number of labels increases. In this paper, we study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph, driven with a collective loss function that injects the information of expected label frequency and average multi-label cardinality of predictions. The experiments show that the proposed framework achieves effective performance under low supervision settings with almost imperceptible computational and memory overheads added to the usage of pre-trained language model outperforming its initial performance by 70% in terms of example-based F1 score.
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (published)
proceedings.mlr.press
proceedings.mlr.press
Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges
Massimo Caccia
Jonas Mueller
Taesup Kim
Laurent Charlin
Rasool Fakoor
2023-11-19
Proceedings of The 2nd Conference on Lifelong Learning Agents (published)
proceedings.mlr.press
proceedings.mlr.press
Towards Few-Shot Coordination: Revisiting Ad-Hoc Teamplay Challenge in the Game of Hanabi
Hadi Nekoei
Xutong Zhao
Janarthanan Rajendran
Miao Liu
Sarath Chandar
Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in rece… (see more)nt years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners. To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods. In particular, we created a diverse set of pre-trained agents and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance. After evaluating several SOTA algorithms using our framework, our experiments reveal that naive Independent Q-Learning (IQL) agents in most cases adapt as quickly as the SOTA ZSC algorithm Off-Belief Learning (OBL). This finding raises an interesting research question: How to design MARL algorithms with high ZSC performance and capability of fast adaptation to unseen partners. As a first step, we studied the role of different hyper-parameters and design choices on the adaptability of current MARL algorithms. Our experiments show that two categories of hyper-parameters controlling the training data diversity and optimization process have a significant impact on the adaptability of Hanabi agents.
2023-11-19
Conference on Lifelong Learning Agents (published)
doi.org
proceedings.mlr.press
Assessing the Security of GitHub Copilot's Generated Code - A Targeted Replication Study
Vahid Majdinasab
Michael Joshua Bishop
Shawn Rasheed
Arghavan Moradi Dakhel
Amjed Tahir
Foutse Khomh
2023-11-17
ArXiv (preprint)
doi.org
arxiv.org
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua
Sitao Luan
Minkai Xu
Zhitao Ying
Rex Ying
Jie Fu
Stefano Ermon
Doina Precup
2023-11-17
logconference.io/LOG/2023/Conference (poster)
doi.org
proceedings.mlr.press
The evidence mismatch in pediatric surgical practice
Marina Broomfield
Zena Agabani
Elena Guadagno
Dan Poenaru
Robert Baird
2023-11-17
Pediatric surgery international (Print) (published)
doi.org
Aperiodic activity as a central neural feature of hypnotic susceptibility outside of hypnosis
Mathieu Landry
Jason da Silva Castanheira
Catherine Boisvert
Floriane Rousseaux
Jérôme Sackur
Amir Raz
Philippe Richebé
David Ogez
Pierre Rainville
Karim Jerbi
Hypnotic phenomena reflect the ability to alter one’s subjective experiences based on targeted verbal suggestions. This ability varies gre… (see more)atly in the population. The brain correlates to explain this variability remain elusive. Addressing this gap, our study employs machine learning to predict hypnotic susceptibility. By recording electroencephalography (EEG) before and after a hypnotic induction and analyzing diverse neurophysiological features, we were able to determine that several features differentiate between high and low hypnotic susceptible individuals both at baseline and during hypnosis. Our analysis revealed that the paramount discriminative feature is non-oscillatory EEG activity before the induction—a new finding in the field. This outcome aligns with the idea that hypnotic susceptibility represents a latent trait observable through a plain five-minutes resting-state EEG.
2023-11-16
bioRxiv (preprint)
doi.org
Differentiable visual computing for inverse problems and machine learning
Andrew Spielberg
Fangcheng Zhong
Konstantinos Rematas
Krishna Murthy
Cengiz Oztireli
Tzu-Mao Li
D. Nowrouzezahrai
2023-11-16
Nature Machine Intelligence (published)
doi.org
arxiv.org
Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding
Chin Wang Cheong
Kejing Yin
William K. Cheung
Benjamin C. M. Fung
Jonathan Poon
2023-11-13
ACM Transactions on Intelligent Systems and Technology (published)
doi.org
A community effort in SARS-CoV-2 drug discovery.
Johannes Schimunek
Philipp Seidl
Katarina Elez
Tim Hempel
Tuan Le
Frank Noé
Simon Olsson
Lluís Raich
Robin Winter
Hatice Gokcan
Filipp Gusev
Evgeny M. Gutkin
Olexandr Isayev
Maria G. Kurnikova
Chamali H. Narangoda
Roman Zubatyuk
Ivan P. Bosko
Konstantin V. Furs
Anna D. Karpenko
Yury V. Kornoushenko … (see 133 more)
Mikita Shuldau
Artsemi Yushkevich
Mohammed B. Benabderrahmane
Patrick Bousquet‐Melou
Ronan Bureau
Beatrice Charton
Bertrand C. Cirou
Gérard Gil
William J. Allen
Suman Sirimulla
Stanley Watowich
Nick Antonopoulos
Nikolaos Epitropakis
Agamemnon Krasoulis
Vassilis Pitsikalis
Stavros Theodorakis
Igor Kozlovskii
Anton Maliutin
Alexander Medvedev
Petr Popov
Mark Zaretckii
Hamid Eghbal‐Zadeh
Christina Halmich
Sepp Hochreiter
Andreas Mayr
Peter Ruch
Michael Widrich
Francois Berenger
Ashutosh Kumar
Yoshihiro Yamanishi
Kam Y. J. Zhang
Emmanuel Bengio
Yoshua Bengio
Moksh J. Jain
Maksym Korablyov
Cheng-Hao Liu
Gilles Marcou
M. Gilles
Enrico Glaab
Kelly Barnsley
Suhasini M. Iyengar
Mary Jo Ondrechen
V. Joachim Haupt
Florian Kaiser
Michael Schroeder
Luisa Pugliese
Simone Albani
Christina Athanasiou
Andrea Beccari
Paolo Carloni
Giulia D'Arrigo
Eleonora Gianquinto
Jonas Goßen
Anton Hanke
Benjamin P. Joseph
Daria B. Kokh
Sandra Kovachka
Candida Manelfi
Goutam Mukherjee
Abraham Muñiz‐Chicharro
Francesco Musiani
Ariane Nunes‐Alves
Giulia Paiardi
Giulia Rossetti
S. Kashif Sadiq
Francesca Spyrakis
Carmine Talarico
Alexandros Tsengenes
Rebecca C. Wade
Conner Copeland
Jeremiah Gaiser
Daniel R. Olson
Amitava Roy
Vishwesh Venkatraman
Travis J. Wheeler
Haribabu Arthanari
Klara Blaschitz
Marco Cespugli
Vedat Durmaz
Konstantin Fackeldey
Patrick D. Fischer
Christoph Gorgulla
Christian Gruber
Karl Gruber
Michael Hetmann
Jamie E. Kinney
Krishna M. Padmanabha Das
Shreya Pandita
Amit Singh
Georg Steinkellner
Guilhem Tesseyre
Gerhard Wagner
Zi‐Fu Wang
Ryan J. Yust
Dmitry S. Druzhilovskiy
Dmitry A. Filimonov
Pavel V. Pogodin
Vladimir Poroikov
Anastassia V. Rudik
Leonid A. Stolbov
Alexander V. Veselovsky
Maria De Rosa
Giada De Simone
Maria R. Gulotta
Jessica Lombino
Nedra Mekni
Ugo Perricone
Arturo Casini
Amanda Embree
D. Benjamin Gordon
David Lei
Katelin Pratt
Christopher A. Voigt
Kuang‐Yu Chen
Yves Jacob
Tim Krischuns
Pierre Lafaye
Agnès Zettor
M. Luis Rodríguez
Kris M. White
Daren Fearon
Frank Von Delft
Martin A. Walsh
Dragos Horvath
Charles L. Brooks
Babak Falsafi
Bryan Ford
Adolfo García‐Sastre
Sang Yup Lee
Nadia Naffakh
Alexandre Varnek
Günter Klambauer
Thomas M. Hermans
The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availabili… (see more)ty of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.
2023-11-13
Molecular Informatics (published)
doi.org
Correlation between Preoperative MRI Parameters and Oswestry Disability Index in Patients with Lumbar Spinal Stenosis: A Retrospective Study
Radu Caprariu
Manuel D. Oprea
Dan V. Poenaru
Diana Andrei
Background and Objectives: Lumbar spinal stenosis (LSS) is a degenerative condition posing significant challenges in clinical management. De… (see more)spite the use of radiological parameters and patient-reported outcome measures like the Oswestry Disability Index (ODI) for evaluation, there is limited understanding of their interrelationship. This study aimed to investigate the correlation between preoperative MRI parameters and ODI scores in patients with LSS undergoing surgical treatment. Materials and Methods: A retrospective analysis was conducted on 86 patients diagnosed with LSS over a 5-year period. Preoperative MRI measurements, including the cross-sectional area of the psoas muscle, lumbar canal stenosis, neural foramina area, and facet joint osteoarthritis, were assessed. ODI scores were collected preoperatively and at a 1-year follow-up. Statistical analyses were performed using IBM SPSS Statistics software (version 26). Results: Weak to moderate correlations were observed between certain MRI parameters and ODI scores. The initial ODI score had a weak positive correlation with the severity of lumbar canal stenosis according to Schizas criteria (rho = 0.327, p = 0.010) and a moderate negative correlation with the relative cross-sectional area of the psoas muscle (rho = −0.498, p = 0.000). At 1-year follow-up, the ODI had a weak negative correlation with the relative cross-sectional area of the psoas muscle (rho = −0.284, p = 0.026). Conclusions: While the severity of LSS showed a weak correlation with initial ODI, it was not a predictor of 1-year postoperative ODI. Furthermore, although the cross-sectional area of the thecal sac, the sagittal area of the neural foramen, and the grade of facet joint osteoarthritis influence the imagistic severity, none of them correlate with ODI. These findings underscore the need for a comprehensive model that integrates multiple imaging and clinical parameters for a holistic understanding of LSS and its functional outcomes.
2023-11-13
Medicina (published)
doi.org