Publications

Behavioral Cloning for Crystal Design
Prashant Govindarajan
Santiago Miret
Jarrid Rector-Brooks
Mariano Phielipp
Janarthanan Rajendran
Sarath Chandar Anbil Parthipan
Solid-state materials, which are made up of periodic 3D crystal structures, are particularly useful for a variety of real-world applications… (see more) such as batteries, fuel cells and catalytic materials. Designing solid-state materials, especially in a robust and automated fashion, remains an ongoing challenge. To further the automated design of crystalline materials, we propose a method to learn to design valid crystal structures given a crystal skeleton. By incorporating Euclidean equivariance into a policy network, we portray the problem of designing new crystals as a sequential prediction task suited for imitation learning. At each step, given an incomplete graph of a crystal skeleton, an agent assigns an element to a specific node. We adopt a behavioral cloning strategy to train the policy network on data consisting of curated trajectories generated from known crystals.
2023-03-17
ICLR.cc/2023/Workshop/ML4Materials (poster)
openreview.net
openreview.net
Transitions between cognitive topographies: contributions of network structure, neuromodulation, and disease
Andrea I. Luppi
S. Parker Singleton
Justine Y. Hansen
Danilo Bzdok
Amy Kuceyeski
Richard F. Betzel
Bratislav Mišić
2023-03-17
bioRxiv (preprint)
doi.org
Instance-Conditioned GAN Data Augmentation for Representation Learning
Pietro Astolfi
Arantxa Casanova
Jakob Verbeek
Pascal Vincent
Adriana Romero Soriano
Michal Drozdzal
2023-03-16
ArXiv (preprint)
doi.org
arxiv.org
Investigating Failures to Generalize for Coreference Resolution Models
Ian Porada
Alexandra Olteanu
Kaheer Suleman
Adam Trischler
Jackie Cheung
Coreference resolution models are often evaluated on multiple datasets. Datasets vary, however, in how coreference is realized -- i.e., how … (see more)the theoretical concept of coreference is operationalized in the dataset -- due to factors such as the choice of corpora and annotation guidelines. We investigate the extent to which errors of current coreference resolution models are associated with existing differences in operationalization across datasets (OntoNotes, PreCo, and Winogrande). Specifically, we distinguish between and break down model performance into categories corresponding to several types of coreference, including coreferring generic mentions, compound modifiers, and copula predicates, among others. This break down helps us investigate how state-of-the-art models might vary in their ability to generalize across different coreference types. In our experiments, for example, models trained on OntoNotes perform poorly on generic mentions and copula predicates in PreCo. Our findings help calibrate expectations of current coreference resolution models; and, future work can explicitly account for those types of coreference that are empirically associated with poor generalization when developing models.
2023-03-16
ArXiv (preprint)
doi.org
arxiv.org
Analysis of gene expression and use of connectivity mapping to identify drugs for treatment of human glomerulopathies
Chen-Fang Chung
Joan Papillon
José R. Navarro-Betancourt
Julie Guillemette
Ameya Bhope
Amin Emad
Andrey V. Cybulsky
2023-03-13
Frontiers in Medicine (published)
doi.org
Applying the column generation method to the intensity modulated high dose rate brachytherapy inverse planning problem
Majd Antaki
Marc-André Renaud
Marc Morcos
Jan Seuntjens
Shirin A. Enger
2023-03-13
Physics in Medicine & Biology (published)
doi.org
Optimization of the location and design of urban green spaces
Caroline Leboeuf
Margarida Carvalho
Yan Kestens
Benoit Thierry
2023-03-13
ArXiv (preprint)
doi.org
arxiv.org
Proactive Contact Tracing
Prateek Gupta
Tegan Maharaj
Martin Weiss
Nasim Rahaman
Hannah Alsdurf
Nanor Minoyan
Soren Harnois-Leblanc
Joanna Merckx
andrew williams
Victor Schmidt
Pierre-Luc St-Charles
Akshay Patel
Yang Zhang
David Buckeridge
Chris Pal
Bernhard Schölkopf
Yoshua Bengio
2023-03-13
PLOS Digital Health (published)
doi.org
A Systematic Study of Joint Representation Learning on Protein Sequences and Structures
Zuobai Zhang
Chuanrui Wang
Minghao Xu
Vijil Chenthamarakshan
Aurelie Lozano
Payel Das
Jian Tang
Learning effective protein representations is critical in a variety of tasks in biology such as predicting protein functions. Recent sequenc… (see more)e representation learning methods based on Protein Language Models (PLMs) excel in sequence-based tasks, but their direct adaptation to tasks involving protein structures remains a challenge. In contrast, structure-based methods leverage 3D structural information with graph neural networks and geometric pre-training methods show potential in function prediction tasks, but still suffers from the limited number of available structures. To bridge this gap, our study undertakes a comprehensive exploration of joint protein representation learning by integrating a state-of-the-art PLM (ESM-2) with distinct structure encoders (GVP, GearNet, CDConv). We introduce three representation fusion strategies and explore different pre-training techniques. Our method achieves significant improvements over existing sequence- and structure-based methods, setting new state-of-the-art for function annotation. This study underscores several important design choices for fusing protein sequence and structure information. Our implementation is available at https://github.com/DeepGraphLearning/ESM-GearNet.
2023-03-11
ArXiv (preprint)
arxiv.org
arxiv.org
The Critical Node Game
Gabriele Dragotto
Amine Boukhtouta
Andrea Lodi
Mehdi Taobane
Cloud networks are the backbone of the modern distributed internet infrastructure as they provision most of the on-demand resources organiza… (see more)tions and individuals use daily. However, any abrupt cyber-attack could disrupt the provisioning of some of the cloud resources fulfilling the needs of customers, industries, and governments. In this work, we introduce a game-theoretic model that assesses the cyber-security risk of cloud networks and informs security experts on the optimal security strategies. Our approach combines game theory, combinatorial optimization, and cyber-security and aims at minimizing the unexpected network disruptions caused by malicious cyber-attacks under uncertainty. Methodologically, our approach consists of a simultaneous and non-cooperative attacker-defender game where each player solves a combinatorial optimization problem parametrized in the variables of the other player. Practically, our approach enables security experts to (i.) assess the security posture of the cloud network, and (ii.) dynamically adapt the level of cyber-protection deployed on the network. We provide a detailed analysis of a real-world cloud network and demonstrate the efficacy of our approach through extensive computational tests.
2023-03-10
ArXiv (preprint)
doi.org
arxiv.org
Learning Hybrid Interpretable Models: Theory, Taxonomy, and Methods
Julien Ferry
gabriel laberge
Ulrich Aivodji
A hybrid model involves the cooperation of an interpretable model and a complex black box. At inference, any input of the hybrid model is as… (see more)signed to either its interpretable or complex component based on a gating mechanism. The advantages of such models over classical ones are two-fold: 1) They grant users precise control over the level of transparency of the system and 2) They can potentially perform better than a standalone black box since redirecting some of the inputs to an interpretable model implicitly acts as regularization. Still, despite their high potential, hybrid models remain under-studied in the interpretability/explainability literature. In this paper, we remedy this fact by presenting a thorough investigation of such models from three perspectives: Theory, Taxonomy, and Methods. First, we explore the theory behind the generalization of hybrid models from the Probably-Approximately-Correct (PAC) perspective. A consequence of our PAC guarantee is the existence of a sweet spot for the optimal transparency of the system. When such a sweet spot is attained, a hybrid model can potentially perform better than a standalone black box. Secondly, we provide a general taxonomy for the different ways of training hybrid models: the Post-Black-Box and Pre-Black-Box paradigms. These approaches differ in the order in which the interpretable and complex components are trained. We show where the state-of-the-art hybrid models Hybrid-Rule-Set and Companion-Rule-List fall in this taxonomy. Thirdly, we implement the two paradigms in a single method: HybridCORELS, which extends the CORELS algorithm to hybrid modeling. By leveraging CORELS, HybridCORELS provides a certificate of optimality of its interpretable component and precise control over transparency. We finally show empirically that HybridCORELS is competitive with existing hybrid models, and performs just as well as a standalone black box (or even better) while being partly transparent.
2023-03-08
ArXiv (preprint)
doi.org
arxiv.org
Transfer Entropy Bottleneck: Learning Sequence to Sequence Information Transfer
Damjan Kalajdzievski
Ximeng Mao
Pascal Fortier-Poisson
Guillaume Lajoie
Blake Richards
When presented with a data stream of two statistically dependent variables, predicting the future of one of the variables (the target stream… (see more)) can benefit from information about both its history and the history of the other variable (the source stream). For example, fluctuations in temperature at a weather station can be predicted using both temperatures and barometric readings. However, a challenge when modelling such data is that it is easy for a neural network to rely on the greatest joint correlations within the target stream, which may ignore a crucial but small information transfer from the source to the target stream. As well, there are often situations where the target stream may have previously been modelled independently and it would be useful to use that model to inform a new joint model. Here, we develop an information bottleneck approach for conditional learning on two dependent streams of data. Our method, which we call Transfer Entropy Bottleneck (TEB), allows one to learn a model that bottlenecks the directed information transferred from the source variable to the target variable, while quantifying this information transfer within the model. As such, TEB provides a useful new information bottleneck approach for modelling two statistically dependent streams of data in order to make predictions about one of them.
2023-03-08
TMLR (accepted)
doi.org
openreview.net