Publications

Mean-field approximation for large-population beauty-contest games
Raihan Seraj
Jerome Le Ny
Aditya Mahajan
We study a class of Keynesian beauty contest games where a large number of heterogeneous players attempt to estimate a common parameter base… (see more)d on their own observations. The players are rewarded for producing an estimate close to a certain multiplicative factor of the average decision, this factor being specific to each player. This model is motivated by scenarios arising in commodity or financial markets, where investment decisions are sometimes partly based on following a trend. We provide a method to compute Nash equilibria within the class of affine strategies. We then develop a mean-field approximation, in the limit of an infinite number of players, which has the advantage that computing the best-response strategies only requires the knowledge of the parameter distribution of the players, rather than their actual parameters. We show that the mean-field strategies lead to an ε-Nash equilibrium for a system with a finite number of players. We conclude by analyzing the impact on individual behavior of changes in aggregate population behavior.
2021-12-14
IEEE Conference on Decision and Control (published)
doi.org
Thompson sampling for linear quadratic mean-field teams
Mukul Gagrani
Sagar Sudhakara
Aditya Mahajan
Ashutosh Nayyar
Yi Ouyang
We consider optimal control of an unknown multi-agent linear quadratic (LQ) system where the dynamics and the cost are coupled across the ag… (see more)ents through the mean-field (i.e., empirical mean) of the states and controls. Directly using single-agent LQ learning algorithms in such models results in regret which increases polynomially with the number of agents. We propose a new Thompson sampling based learning algorithm which exploits the structure of the system model and show that the expected Bayesian regret of our proposed algorithm for a system with agents of |M| different types at time horizon T is
2021-12-14
2021 60th IEEE Conference on Decision and Control (CDC) (published)
doi.org
arxiv.org
Early Transcriptional Changes in Rabies Virus-Infected Neurons and Their Impact on Neuronal Functions
Seonhee Kim
Florence Larrous
Hugo Varet
Rachel Legendre
Lena Feige
Guillaume Dumas
Rebecca Matsas
Georgia Kouroupi
Regis Grailhe
Hervé Bourhy
2021-12-13
Frontiers in Microbiology (published)
doi.org
Implications of Topological Imbalance for Representation Learning on Biomedical Knowledge Graphs
Stephen Bonner
Ufuk Kirik
Ola Engkvist
Jian Tang
I. Barrett
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utiliz… (see more)e 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 modelling 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 modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.
2021-12-13
ArXiv (preprint)
doi.org
arxiv.org
A taxonomy of weight learning methods for statistical relational learning
Sriram Srinivasan
Charles Dickens
Eriq Augustine
Golnoosh Farnadi
Lise Getoor
2021-12-13
Machine-mediated learning (published)
doi.org
A taxonomy of weight learning methods for statistical relational learning
Sriram Srinivasan
Charles Dickens
Eriq Augustine
Golnoosh Farnadi
Lise Getoor
2021-12-13
Machine-mediated learning (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
Andrew Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
2021-12-10
Surgical Innovation (published)
doi.org
Artificial Intelligence in Surgical Education: Considerations for Interdisciplinary Collaborations
Elif Bilgic
A. Gorgy
Meredith Young
Samira Abbasgholizadeh-Rahimi
Jason M. Harley
Arti fi cial intelligence (AI) based devices are currently being used in the delivery of surgical care in a variety of settings. 1,2 Howeve… (see more)r, AI-enabled systems can trigger a variety of opinions and emotions, which reveals the different lenses that shape views on AI. Nonethless, work within surgical education may necessitate a more balanced view; with an acknowledgment of the participation of AI-enhanced devices in the delivery of surgical care and education
2021-12-10
Surgical Innovation (published)
doi.org
Few Shot Image Generation via Implicit Autoencoding of Support Sets
Andy Huang
Kuan-Chieh Wang
Guillaume Rabusseau
Alireza Makhzani
Recent generative models such as generative adversarial networks have achieved remarkable success in generating realistic images, but they r… (see more)equire large training datasets and computational resources. The goal of few-shot image generation is to learn the distribution of a new dataset from only a handful of examples by transferring knowledge learned from structurally similar datasets. Towards achieving this goal, we propose the “Implicit Support Set Autoencoder” (ISSA) that adversarially learns the relationship across datasets using an unsupervised dataset representation, while the distribution of each individual dataset is learned using implicit distributions. Given a few examples from a new dataset, ISSA can generate new samples by inferring the representation of the underlying distribution using a single forward pass. We showcase significant gains from our method on generating high quality and diverse images for unseen classes in the Omniglot and CelebA datasets in few-shot image generation settings.
2021-12-10
NeurIPS.cc/2021/Workshop/MetaLearn (poster)
openreview.net
openreview.net
Maternal chemosignals enhance infant-adult brain-to-brain synchrony
Yaara Endevelt-Shapira
Amir Djalovski
Guillaume Dumas
Ruth Feldman
2021-12-10
Science Advances (published)
doi.org
Endocytic proteins with prion-like domains form viscoelastic condensates that enable membrane remodeling
Louis-Philippe Bergeron-Sandoval
Sandeep Kumar
Hossein Khadivi Heris
Catherine L. A. Chang
Caitlin E. Cornell
Sarah L. Keller
Paul François
Adam G. Hendricks
Allen J. Ehrlicher
Rohit V. Pappu
Stephen W. Michnick
Significance The uptake of molecules into cells, known as endocytosis, requires membrane invagination and the formation of vesicles. A versi… (see more)on of endocytosis that is independent of actin polymerization is aided by the assembly of membraneless biomolecular condensates at the site of membrane invagination. Here, we show that endocytic condensates are viscoelastic bodies that concentrate key proteins with prion-like domains to enable membrane remodeling. A distinct molecular grammar, namely the preference for glutamine versus asparagine residues, underlies the cohesive interactions that give rise to endocytic condensates. We incorporate material properties inferred using active rheology into a mechanical model to explain how cohesive interactions within condensates and interfacial tensions among condensates, membranes, and the cytosol can drive membrane invagination to initiate endocyosis. Membrane invagination and vesicle formation are key steps in endocytosis and cellular trafficking. Here, we show that endocytic coat proteins with prion-like domains (PLDs) form hemispherical puncta in the budding yeast, Saccharomyces cerevisiae. These puncta have the hallmarks of biomolecular condensates and organize proteins at the membrane for actin-dependent endocytosis. They also enable membrane remodeling to drive actin-independent endocytosis. The puncta, which we refer to as endocytic condensates, form and dissolve reversibly in response to changes in temperature and solution conditions. We find that endocytic condensates are organized around dynamic protein–protein interaction networks, which involve interactions among PLDs with high glutamine contents. The endocytic coat protein Sla1 is at the hub of the protein–protein interaction network. Using active rheology, we inferred the material properties of endocytic condensates. These experiments show that endocytic condensates are akin to viscoelastic materials. We use these characterizations to estimate the interfacial tension between endocytic condensates and their surroundings. We then adapt the physics of contact mechanics, specifically modifications of Hertz theory, to develop a quantitative framework for describing how interfacial tensions among condensates, the membrane, and the cytosol can deform the plasma membrane to enable actin-independent endocytosis.
2021-12-09
Proceedings of the National Academy of Sciences of the United States of America (published)
doi.org
Mortality trends and lengths of stay among hospitalized COVID-19 patients in Ontario and Quebec (Canada): a population-based cohort study of the first three epidemic waves
Yiqing Xia
Huiting Ma
David Buckeridge
Marc Brisson
Beate Sander
Adrienne K. Chan
Aman D Verma
Iris Ganser
Nadine Kronfli
Sharmistha Mishra
Mathieu Maheu‐giroux
Background: Epidemic waves of COVID-19 strained hospital resources. We describe temporal trends in mortality risk and length of stay in inte… (see more)nsive cares units (ICUs) among COVID-19 patients hospitalized through the first three epidemic waves in Canada. Methods: We used population-based provincial hospitalization data from Ontario and Qu&eacutebec to examine mortality risk and lengths of ICU stay. For each province, adjusted estimates were obtained using marginal standardization of logistic regression models, adjusting for patient-level characteristics and hospital-level determinants. Results: Using all hospitalizations from Ontario (N=26,541) and Qu&eacutebec (N=23,857), we found that unadjusted in-hospital mortality risks peaked at 31% in the first wave and was lowest at the end of the third wave at 6-7%. This general trend remained after controlling for confounders. The odds of in-hospital mortality in the highest hospital occupancy quintile was 1.2 (95%CI: 1.0-1.4; Ontario) and 1.6 (95%CI: 1.3-1.9; Qu&eacutebec) times that of the lowest quintile. Variants of concerns were associated with an increased in-hospital mortality. Length of ICU stay decreased over time from a mean of 16 days (SD=18) to 15 days (SD=15) in the third wave but were consistently higher in Ontario than Qu&eacutebec by 3-6 days. Conclusion: In-hospital mortality risks and lengths of ICU stay declined over time in both provinces, despite changing patient demographics, suggesting that new therapeutics and treatment, as well as improved clinical protocols, could have contributed to this reduction. Continuous population-based monitoring of patient outcomes in an evolving epidemic is necessary for health system preparedness and response.
2021-12-08
medRxiv (preprint)
doi.org