Publications

Automated prediction of extubation success in extremely preterm infants: the APEX multicenter study
Lara J. Kanbar
Wissam Shalish
Charles C. Onu
Samantha Latremouille
Lajos Kovacs
Martin Keszler
Sanjay Chawla
Karen A. Brown
Doina Precup
Robert E. Kearney
Guilherme M. Sant’Anna
2022-07-28
Pediatric Research (publié)
doi.org
BioCaster in 2021: automatic disease outbreaks detection from global news media
Zaiqiao Meng
Anya Okhmatovskaia
Maxime Polleri
Yannan Shen
Guido Powell
Zihao Fu
Iris Ganser
Meiru Zhang
Nicholas B. King
David Buckeridge
Nigel Collier
SUMMARY: BioCaster was launched in 2008 to provide an ontology-based text mining system for early disease detection from open news sources. … (voir plus)Following a 6-year break, we have re-launched the system in 2021. Our goal is to systematically upgrade the methodology using state-of-the-art neural network language models, whilst retaining the original benefits that the system provided in terms of logical reasoning and automated early detection of infectious disease outbreaks. Here, we present recent extensions such as neural machine translation in 10 languages, neural classification of disease outbreak reports and a new cloud-based visualization dashboard. Furthermore, we discuss our vision for further improvements, including combining risk assessment with event semantics and assessing the risk of outbreaks with multi-granularity. We hope that these efforts will benefit the global public health community. AVAILABILITY AND IMPLEMENTATION: BioCaster web-portal is freely accessible at http://biocaster.org.
2022-07-27
Bioinformatics (publié)
doi.org
A Parsimonious Description of Global Functional Brain Organization in Three Spatiotemporal Patterns
Taylor Bolt
Jason S. Nomi
Danilo Bzdok
Jorge A. Salas
Catie Chang
B.T. Thomas Yeo
Lucina Q. Uddin
Shella D. Keilholz
Resting-state functional MRI has yielded seemingly disparate insights into large-scale organization of the human brain. The brain’s large-… (voir plus)scale organization can be divided into two broad categories - zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. Here we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern, and the functional connectome network structure are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses, and unifies phenomena in resting-state functional MRI previously thought distinct.
2022-07-27
Nature Neuroscience (publié)
doi.org
Explanatory latent representation of heterogeneous spatial maps of task-fMRI in large-scale datasets
Mariam Zabihi
Seyed Mostafa Kia
Thomas Wolfers
Stijn de Boer
Charlotte Fraza
Sourena Soheili-Nezhad
Richard Dinga
Alberto Llera Arenas
Danilo Bzdok
Christian F. Beckmann
Andre Marquand
Finding an interpretable and compact representation of complex neuroimage data can be extremely useful for understanding brain behavioral ma… (voir plus)pping and hence for explaining the biological underpinnings of mental disorders. Hand-crafted representations, as well as linear transformations, may not accurately reflect the significant variability across individuals. Here, we applied a data-driven approach to learn interpretable and generalizable latent representations that link cognition with underlying brain systems; we applied a three-dimensional autoencoder to two large-scale datasets to find an interpretable latent representation of high dimensional task fMRI image data. This representation also accounts for demographic characteristics, achieved by solving a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics (‘latent indices’) to find a multivariate mapping to non-imaging measures. We trained our model with multi-task fMRI data derived from the Human Connectome Project (HCP) that provides whole-brain coverage across a range of cognitive tasks. Next, in a transfer learning setting, we tested the generalization of our latent space on UK Biobank data as an independent dataset. Our model showed high performance in terms of age and predictions and was capable of capturing complex behavioral characteristics and preserving the individualized variabilities using a highly interpretable latent representation.
2022-07-26
bioRxiv (prépublication)
doi.org
H4rm0ny: A Competitive Zero-Sum Two-Player Markov Game for Multi-Agent Learning on Evasive Malware Generation and Detection
Christopher Molloy
Steven H. H. Ding
Benjamin C. M. Fung
Philippe Charland
To combat the increasingly versatile and mutable modern malware, Machine Learning (ML) is now a popular and effective complement to the exis… (voir plus)ting signature-based techniques for malware triage and identification. However, ML is also a readily available tool for adversaries. Recent studies have shown that malware can be modified by deep Reinforcement Learning (RL) techniques to bypass AI-based and signature-based anti-virus systems without altering their original malicious functionalities. These studies only focus on generating evasive samples and assume a static detection system as the enemy.Malware detection and evasion essentially form a two-party cat-and-mouse game. Simulating the real-life scenarios, in this paper we present the first two-player competitive game for evasive malware detection and generation, following the zero-sum Multi-Agent Reinforcement Learning (MARL) paradigm. Our experiments on recent malware show that the produced malware detection agent is more robust against adversarial attacks. Also, the produced malware modification agent is able to generate more evasive samples fooling both AI-based and other anti-malware techniques.
2022-07-26
Computer Science Symposium in Russia (publié)
doi.org
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
Stephen Bonner
Ufuk Kirik
Ola Engkvist
Jian Tang
Ian P Barrett
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KG) that utilize… (voir plus) the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modeling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely-connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modeling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
2022-07-25
Briefings in Bioinformatics (inconnu)
doi.org
arxiv.org
On the Expressivity of Markov Reward (Extended Abstract)
David Abel
Will Dabney
Anna Harutyunyan
Mark K. Ho
Michael L. Littman
Doina Precup
Satinder Singh
2022-07-22
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (publié)
doi.org
Flaky Performances when Pre-Training on Relational Databases with a Plan for Future Characterization Efforts
Shengchao Liu
David Vázquez
Jian Tang
Pierre-Andre Noel
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extra… (voir plus)cted from relational databases (RDBs). Intu-itively, this joint use of SSL and GNNs allows us to leverage more of the available data, which could translate to better results. However, while we observe positive transfer in some cases, others showed systematic performance degradation, including some spectacular ones. We hypothesize a mechanism that could explain this behaviour and draft the plan for future work testing it by characterizing how much relevant information different strategies can (theoretically and/or empirically) extract from (synthetic and/or real) RDBs.
2022-07-21
ICML.cc/2022/Workshop/Pre-Training (inconnu)
openreview.net
openreview.net
Revisiting Hotels-50K and Hotel-ID
Aarash Feizi
Arantxa Casanova
Adriana Romero
Reihaneh Rabbany
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions prov… (voir plus)ide evaluation setups with different levels of difficulty to better align with the intended real-world application, i.e. countering human trafficking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.
2022-07-19
ArXiv (prépublication)
doi.org
arxiv.org
On the Generalization and Adaption Performance of Causal Models
Nino Scherrer
Anirudh Goyal
Stefan Bauer
Yoshua Bengio
Nan Rosemary Ke
2022-07-19
ICML.cc/2022/Workshop/SCIS (poster)
doi.org
openreview.net
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights
Maxime Gasse
Quentin Cappart
Jonas Charfreitag
Laurent Charlin
Didier Chételat
Antonia Chmiela
Justin Dumouchelle
Ambros Gleixner
Aleksandr M. Kazachkov
Elias Khalil
Pawel Lichocki
Andrea Lodi
Miles Lubin
Chris J. Maddison
Christopher Morris
Dimitri J. Papageorgiou
Augustin Parjadis
Sebastian Pokutta
Antoine Prouvost
Lara Scavuzzo … (voir 21 de plus)
Giulia Zarpellon
Linxin Yang
Sha Lai
Akang Wang
Xiaodong Luo
Xiang Zhou
Haohan Huang
Shengcheng Shao
Yuanming Zhu
Dong Zhang
Tao Quan
Zixuan Cao
Yang Xu
Zhewei Huang
Shuchang Zhou
Chen Binbin
He Minggui
Hao Hao
Zhang Zhiyu
An Zhiwu
Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused … (voir plus)on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.
2022-07-19
Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track (publié)
doi.org
proceedings.mlr.press
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Brandon Trabucco
Mariano Phielipp
Glen Berseth
The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new mor… (voir plus)phology. Recent work aims to eliminate the re-training of policies by investigating whether a morphology-agnostic policy, trained on a diverse set of agents with similar task objectives, can be transferred to new agents with unseen morphologies without re-training. This is a challenging problem that required previous approaches to use hand-designed descriptions of the new agent's morphology. Instead of hand-designing this description, we propose a data-driven method that learns a representation of morphology directly from the reinforcement learning objective. Ours is the first reinforcement learning algorithm that can train a policy to generalize to new agent morphologies without requiring a description of the agent's morphology in advance. We evaluate our approach on the standard benchmark for agent-agnostic control, and improve over the current state of the art in zero-shot generalization to new agents. Importantly, our method attains good performance without an explicit description of morphology.
2022-07-18
International Conference on Machine Learning (Accept for Short Presentation)
doi.org
proceedings.mlr.press