Publications

Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
Hadi Nekoei
Xutong Zhao
Janarthanan Rajendran
Miao Liu
Sarath Chandar
2023-11-20
Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)
doi.org
arxiv.org
Inferring dynamic regulatory interaction graphs from time series data with perturbations
Dhananjay Bhaskar
Daniel Sumner Magruder
Edward De Brouwer
Matheo Morales
Aarthi Venkat
Frederik Wenkel
Guy Wolf
Smita Krishnaswamy
2023-11-18
logconference.io/LOG/2023/Conference (poster)
doi.org
openreview.net
MUDiff: Unified Diffusion for Complete Molecule Generation
Chenqing Hua
Sitao Luan
Minkai Xu
Zhitao Ying
Rex Ying
Jie Fu
Stefano Ermon
Doina Precup
2023-11-18
logconference.io/LOG/2023/Conference (poster)
doi.org
openreview.net
The evidence mismatch in pediatric surgical practice
Marina Broomfield
Zena Agabani
Elena Guadagno
Dan Poenaru
Robert Baird
2023-11-18
Pediatric surgery international (Print) (publié)
doi.org
Differentiable visual computing for inverse problems and machine learning
Andrew Spielberg
Fangcheng Zhong
Konstantinos Rematas
Krishna Murthy
Cengiz Oztireli
Tzu-Mao Li
Derek Nowrouzezahrai
2023-11-17
Nature Machine Intelligence (publié)
doi.org
arxiv.org
From physics to sentience: Deciphering the semantics of the free-energy principle and evaluating its claims: Comment on "Path integrals, particular kinds, and strange things" by Karl Friston et al.
Zahra Sheikhbahaee
Adam Safron
Casper Hesp
Guillaume Dumas
2023-11-17
ArXiv (prépublication)
doi.org
arxiv.org
Leveraging Function Space Aggregation for Federated Learning at Scale
Nikita Dhawan
Nicole Elyse Mitchell
Zachary Charles
Zachary Garrett
Gintare Karolina Dziugaite
The federated learning paradigm has motivated the development of methods for aggregating multiple client updates into a global server model,… (voir plus) without sharing client data. Many federated learning algorithms, including the canonical Federated Averaging (FedAvg), take a direct (possibly weighted) average of the client parameter updates, motivated by results in distributed optimization. In this work, we adopt a function space perspective and propose a new algorithm, FedFish, that aggregates local approximations to the functions learned by clients, using an estimate based on their Fisher information. We evaluate FedFish on realistic, large-scale cross-device benchmarks. While the performance of FedAvg can suffer as client models drift further apart, we demonstrate that FedFish is more robust to longer local training. Our evaluation across several settings in image and language benchmarks shows that FedFish outperforms FedAvg as local training epochs increase. Further, FedFish results in global networks that are more amenable to efficient personalization via local fine-tuning on the same or shifted data distributions. For instance, federated pretraining on the C4 dataset, followed by few-shot personalization on Stack Overflow, results in a 7% improvement in next-token prediction by FedFish over FedAvg.
2023-11-17
ArXiv (prépublication)
doi.org
arxiv.org
Generalizable Imitation Learning Through Pre-Trained Representations
Wei-Di Chang
Francois Hogan
David Meger
Gregory Dudek
In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abil… (voir plus)ities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.
2023-11-15
ArXiv (prépublication)
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 Fung
Jonathan Poon
2023-11-14
ACM Transactions on Intelligent Systems and Technology (publié)
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 … (voir 133 de plus)
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
Marcous 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… (voir plus)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.
2023-11-14
Molecular informatics (publié)
doi.org
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
Arjun Vaithilingam Sudhakar
Prasanna Parthasarathi
Janarthanan Rajendran
Sarath Chandar
2023-11-13
ArXiv (prépublication)
doi.org
arxiv.org
Bridging the Gap Between Offline and Online Reinforcement Learning Evaluation Methodologies
Shiva Kanth Sujit
Pedro Braga
Jorg Bornschein
Samira Ebrahimi Kahou
Reinforcement learning (RL) has shown great promise with algorithms learning in environments with large state and action spaces purely from … (voir plus)scalar reward signals. A crucial challenge for current deep RL algorithms is that they require a tremendous amount of environment interactions for learning. This can be infeasible in situations where such interactions are expensive, such as in robotics. Offline RL algorithms try to address this issue by bootstrapping the learning process from existing logged data without needing to interact with the environment from the very beginning. While online RL algorithms are typically evaluated as a function of the number of environment interactions, there isn't a single established protocol for evaluating offline RL methods. In this paper, we propose a sequential approach to evaluate offline RL algorithms as a function of the training set size and thus by their data efficiency. Sequential evaluation provides valuable insights into the data efficiency of the learning process and the robustness of algorithms to distribution changes in the dataset while also harmonizing the visualization of the offline and online learning phases. Our approach is generally applicable and easy to implement. We compare several existing offline RL algorithms using this approach and present insights from a variety of tasks and offline datasets.
2023-11-10
TMLR (accepté)
doi.org
openreview.net