Irina Rish

rishika.bhagwatkar@mila.quebec

Biographie

Irina Rish est professeure titulaire à l'Université de Montréal (UdeM), où elle dirige le Laboratoire d'IA autonome. Membre du corps professoral de Mila – Institut québécois d’intelligence artificielle, elle est titulaire d'une chaire d'excellence en recherche du Canada (CERC) et d'une chaire en IA Canada-CIFAR. Irina dirige le projet INCITE du ministère américain de l'Environnement au sujet des modèles de fondation évolutifs sur les superordinateurs Summit et Frontier à l'Oak Ridge Leadership Computing Facility (OLCF). Elle est cofondatrice et directrice scientifique de Nolano.ai.

Ses recherches actuelles portent sur les lois de mise à l'échelle neuronale et les comportements émergents (capacités et alignement) dans les modèles de fondation, ainsi que sur l'apprentissage continu, la généralisation hors distribution et la robustesse. Avant de se joindre à l'UdeM en 2019, Irina était chercheuse au Centre de recherche IBM Thomas J. Watson, où elle a travaillé sur divers projets à l'intersection des neurosciences et de l'IA, et dirigé le défi NeuroAI. Elle a reçu plusieurs prix IBM : ceux de l’excellence et de l’innovation exceptionnelle (2018), celui de la réalisation technique exceptionnelle (2017), et celui de l’accomplissement en recherche (2009). Elle détient 64 brevets et a écrit plus de 120 articles de recherche, plusieurs chapitres de livres, trois livres publiés et une monographie sur la modélisation éparse.

Étudiants actuels

Ivan Anokhin

Doctorat - UdeM

Co-superviseur⋅e :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

Doctorat - UdeM

Arjun Ashok

Doctorat - UdeM

Co-superviseur⋅e :

Alexandre Drouin

arjun.ashok@mila.quebec

Maîtrise recherche - UdeM

Doctorat - UdeM

ethan.caballero@mila.quebec

Pietro Cagnasso

Doctorat - Concordia

Superviseur⋅e principal⋅e :

pietro.cagnasso@mila.quebec

Mohammad Javad Darvishi Bayazi

Amin Darabi

Doctorat - UdeM

amin.darabi@mila.quebec

Collaborateur·rice de recherche - UdeM

mj.darvishi92@gmail.com

nadhir.hassen@mila.quebec

Wagner Drew

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Mirco Ravanelli

drew.wagner@mila.quebec

Doctorat - UdeM

Visiteur de recherche indépendant - -

parviz.haggi@gmail.com

Nadhir Hassen

Collaborateur·rice de recherche - UdeM

Niklas Herbster

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Gauthier Gidel

niklas.herbster@gmail.com

prateek.humane@mila.quebec

Mai-Thi Ho

Maîtrise recherche - UdeM

mai-thi.ho@mila.quebec

Prateek Humane

Doctorat

arnav-kumar.jain@mila.quebec

Nizar Islah

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Eilif B. Muller

nizar.islah@mila.quebec

Doctorat - UdeM

Doctorat - UdeM

ayush.kaushal@mila.quebec

Arian Khorasani

Collaborateur·rice de recherche - UdeM

arian.khorasani@mila.quebec

Neeraj Kumar

Collaborateur·rice alumni - UdeM

neeraj.kumar@mila.quebec

Gwen Legate

Doctorat - Concordia

Superviseur⋅e principal⋅e :

gwendolyne.legate@mila.quebec

David Lemay

Maîtrise recherche - UdeM

david.lemay@mila.quebec

amin.mansouri@mila.quebec

Amin Mansouri

Collaborateur·rice alumni - UdeM

Collaborateur·rice de recherche

Doctorat - UdeM

Collaborateur·rice de recherche - UdeM

diganta.misra@mila.quebec

motahareh.pourrahimi@mila.quebec

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Doina Precup

moisescg@mila.quebec

Timothy Nest

Doctorat - UdeM

Co-superviseur⋅e :

Eilif B. Muller

timothy.nest@mila.quebec

Jonathan Pilault

Collaborateur·rice de recherche - Polytechnique

Doctorat - McGill

Superviseur⋅e principal⋅e :

Pouya Bashivan

Mahta Ramezanian

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Guillaume Dumas

mahta.ramezanian@mila.quebec

Matthew Riemer

Doctorat - UdeM

matthew.riemer@mila.quebec

Alexis Roger

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

alexis.roger@mila.quebec

munish.sathish-kumar@mila.quebec

Munish Sathish Kumar

Collaborateur·rice de recherche

Zibo Shang

Doctorat - UdeM

zibo.shang@mila.quebec

Vaibhav Singh

Doctorat - Concordia

Superviseur⋅e principal⋅e :

vaibhav.singh@mila.quebec

Gopeshh Subbaraj

Doctorat - UdeM

gopeshh.subbaraj@mila.quebec

Doctorat - UdeM

Co-superviseur⋅e :

benjamin.therien@mila.quebec

Collaborateur·rice de recherche - UdeM

tejas.vaidhya@mila.quebec

andrew.williams@mila.quebec

Sihui Wei

Baccalauréat - McGill

sihui.wei@mila.quebec

Andrew Williams

Doctorat - UdeM

Co-superviseur⋅e :

Nicolas Chapados

daria.yasafova@mila.quebec

Daria Yasafova

Maîtrise recherche - UdeM

He Zhu

Doctorat - McGill

he.zhu@mila.quebec

Publications

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization

Soroosh Shahtalebi

Jean-christophe Gagnon-audet

A major bottleneck in the real-world applications of machine learning models is their failure in generalizing to unseen domains whose data d… (voir plus)istribution is not i.i.d to the training domains. This failure often stems from learning non-generalizable features in the training domains that are spuriously correlated with the label of data. To address this shortcoming, there has been a growing surge of interest in learning good explanations that are hard to vary, which is studied under the notion of Out-of-Distribution (OOD) Generalization. The search for good explanations that are \textit{invariant} across different domains can be seen as finding local (global) minimas in the loss landscape that hold true across all of the training domains. In this paper, we propose a masking strategy, which determines a continuous weight based on the agreement of gradients that flow in each edge of network, in order to control the amount of update received by the edge in each step of optimization. Particularly, our proposed technique referred to as"Smoothed-AND (SAND)-masking", not only validates the agreement in the direction of gradients but also promotes the agreement among their magnitudes to further ensure the discovery of invariances across training domains. SAND-mask is validated over the Domainbed benchmark for domain generalization and significantly improves the state-of-the-art accuracy on the Colored MNIST dataset while providing competitive results on other domain generalization datasets.

2021-06-03

ArXiv (prépublication)

Continual Learning in Deep Networks: an Analysis of the Last Layer

Timothee LESORT

Thomas George

We study how different output layers in a deep neural network learn and forget in continual learning settings. The following three factors… (voir plus) can affect catastrophic forgetting in the output layer: (1) weights modifications, (2) interference, and (3) projection drift. In this paper, our goal is to provide more insights into how changing the output layers may address (1) and (2). Some potential solutions to those issues are proposed and evaluated here in several continual learning scenarios. We show that the best-performing type of the output layer depends on the data distribution drifts and/or the amount of data available. In particular, in some cases where a standard linear layer would fail, it turns out that changing parameterization is sufficient in order to achieve a significantly better performance, whithout introducing a continual-learning algorithm and instead using the standard SGD to train a model. Our analysis and results shed light on the dynamics of the output layer in continual learning scenarios, and suggest a way of selecting the best type of output layer for a given scenario.

2021-06-02

ArXiv (prépublication)

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Nasim Rahaman

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilanuik

David Buckeridge

Gaétan Marceau Caron

Pierre-Luc Carrier

Joumana Ghosn

Satya Ortiz-Gagné

Chris Pal

Bernhard Schölkopf … (voir 3 de plus)

Abhinav Sharma

Jian Tang

Andrew Williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (voir plus)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2021-05-02

International Conference on Learning Representations (Spotlight)

Gradient Masked Federated Optimization

Irene Tenison

Sreya Francis

2021-04-20

ArXiv (prépublication)

Towards Causal Federated Learning For Enhanced Robustness and Privacy

Sreya Francis

Irene Tenison

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distribu… (voir plus)ted training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.

2021-04-13

ArXiv (prépublication)

Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

2021-04-07

Human Brain and Artificial Intelligence (publié)

Understanding Continual Learning Settings with Data Distribution Drift Analysis

Timothee LESORT

Massimo Caccia

Classical machine learning algorithms often assume that the data are drawn i.i.d. from a stationary probability distribution. Recently, cont… (voir plus)inual learning emerged as a rapidly growing area of machine learning where this assumption is relaxed, i.e. where the data distribution is non-stationary and changes over time. This paper represents the state of data distribution by a context variable

2021-04-03

ArXiv (prépublication)

Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning

Massimo Caccia

Pau Rodríguez

Lucas Caccia

Alexandre Lacoste

Continual learning studies agents that learn from streams of tasks without forgetting previous ones while adapting to new ones. Two recent c… (voir plus)ontinual-learning scenarios have opened new avenues of research. In meta-continual learning, the model is pre-trained to minimize catastrophic forgetting of previous tasks. In continual-meta learning, the aim is to train agents for faster remembering of previous tasks through adaptation. In their original formulations, both methods have limitations. We stand on their shoulders to propose a more general scenario, OSAKA, where an agent must quickly solve new (out-of-distribution) tasks, while also requiring fast remembering. We show that current continual learning, meta-learning, meta-continual learning, and continual-meta learning techniques fail in this new scenario. We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario. We empirically show that Continual-MAML is better suited to the new scenario than the aforementioned methodologies, as well as standard continual learning and meta-learning approaches.

2020-12-06

Conference on Neural Information Processing Systems (Accept (poster))

Probabilistic Classifiers

2020-11-29

Data-Driven Computational Neuroscience (publié)

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

Prateek Gupta

Tegan Maharaj

Martin Weiss

Nasim Rahaman

Hannah Alsdurf

Abhinav Sharma

Nanor Minoyan

Soren Harnois Leblanc

Victor Schmidt

Pierre-Luc St. Charles

Akshay Patel

David Buckeridge … (voir 9 de plus)

Joumana Ghosn

Yang Zhang

Bernhard Schölkopf

Jian Tang

Christopher Pal

Joanna Merckx

Eilif B. Muller

Yoshua Bengio

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and vari… (voir plus)ous digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

2020-10-01

OpenReview.net/Anonymous_Preprint (inconnu)

Survey on Applications of Multi-Armed and Contextual Bandits

Djallel Bouneffouf

Charu Aggarwal

In recent years, the multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems a… (voir plus)nd information retrieval to healthcare and finance. This success is due to its stellar performance combined with attractive properties, such as learning from less feedback. The multiarmed bandit field is currently experiencing a renaissance, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize the state-of-the-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this burgeoning field.

2020-07-18

2020 IEEE Congress on Evolutionary Computation (CEC) (publié)

Chaotic Continual Learning

Touraj Laleh

Mojtaba Faramarzi

A. Chandar

Training a deep neural network requires the model to go over training data for several epochs and update network parameters. In continual le… (voir plus)arning, this process results in catastrophic forgetting which is one of the core issues of this domain. Most proposed approaches for this issue try to compensate for the effects of parameter updates in the batch incremental setup in which the training model visits a lot of samples for several epochs. However, it is not realistic to expect training data will always be fed to model in a batch incremental setup. This paper proposes a chaotic stream learner that mimics the chaotic behavior of biological neurons and does not updates network parameters. In addition, it can work with fewer samples compared to deep learning models on stream learning setup. Our experiments on MNIST, CIFAR10, and Omniglot show that the chaotic stream learner has less catastrophic forgetting by its nature in comparison to a CNN model in continual learning.

2020-07-12

ICML.cc/2020/Workshop/LifelongML (accepté)