Irina Rish

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

Germán Abrevaya

Visiteur de recherche indépendant - UdeM

Co-superviseur⋅e :

Guillaume Dumas

George Adamopoulos

Stagiaire de recherche

Ivan Anokhin

Doctorat - UdeM

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Doctorat - UdeM

Arjun Ashok

Doctorat - UdeM

Co-superviseur⋅e :

Reza Bayat

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Pouya Bashivan

Rishika Bhagwatkar

Maîtrise recherche - UdeM

Collaborateur·rice de recherche - UdeM

Ethan Caballero

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

Mohammad-javad Darvishi-bayazi Darvishi Bayazi

Amin Darabi

Doctorat - UdeM

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Doctorat - UdeM

Collaborateur·rice alumni - UdeM

Co-superviseur⋅e :

Sarath Chandar

Nadhir Hassen

Stagiaire de recherche - UdeM

Maîtrise recherche

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Ioannis Mitliagkas

Nizar Islah

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

Doctorat - UdeM

Collaborateur·rice de recherche

Zafir Khalid

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Neeraj Kumar

Maîtrise professionnelle - UdeM

Gwen Legate

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

David Lemay

Maîtrise recherche - UdeM

amin.mansouri@mila.quebec

Ekaterina Lobacheva

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Maîtrise recherche - UdeM

Collaborateur·rice alumni

Doctorat - UdeM

Maîtrise recherche - UdeM

Diganta Misra

Maîtrise recherche - UdeM

Remus Mocanu

Maîtrise recherche - UdeM

Timothy Nest

Doctorat - UdeM

Co-superviseur⋅e :

Eilif Benjamin Muller

Mohammad Pezeshki

Collaborateur·rice de recherche

Motahareh Pourrahimi

Doctorat - McGill

Superviseur⋅e principal⋅e :

Pouya Bashivan

Mahta Ramezanian

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Guillaume Dumas

Roland Riachi

Collaborateur·rice de recherche - UdeM

Matthew Riemer

Doctorat - UdeM

Alexis Roger

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

Zahra Sheikhbahaee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Guillaume Dumas

Vaibhav Singh

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Dave Whipps

Maîtrise recherche - UdeM

Doctorat - UdeM

Maîtrise recherche - UdeM

Omar Younis

Collaborateur·rice de recherche

Superviseur⋅e principal⋅e :

Yoshua Bengio

Publications

A Story of Two Streams: Reinforcement Learning Models from Human Behavior and Neuropsychiatry

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework fo… (voir plus)r reinforcement learning that extends standard Q-learning to a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems, as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities.

2020-01-01

AAMAS (publié)

arxiv.org

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

Baihan Lin

Guillermo Cecchi

Djallel Bouneffouf

Jenna Reinen

Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement … (voir plus)learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For the AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems. Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributions and real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems.

2019-10-02

NeurIPS.cc/2019/Workshop/Neuro_AI (poster)

openreview.net

Continual Learning with Self-Organizing Maps

Pouya Bashivan

Martin Schrimpf

Robert Ajemian

Matthew D Riemer

Yuhai Tu

Despite remarkable successes achieved by modern neural networks in a wide range of applications, these networks perform best in domain-speci… (voir plus)fic stationary environments where they are trained only once on large-scale controlled data repositories. When exposed to non-stationary learning environments, current neural networks tend to forget what they had previously learned, a phenomena known as catastrophic forgetting. Most previous approaches to this problem rely on memory replay buffers which store samples from previously learned tasks, and use them to regularize the learning on new ones. This approach suffers from the important disadvantage of not scaling well to real-life problems in which the memory requirements become enormous. We propose a memoryless method that combines standard supervised neural networks with self-organizing maps to solve the continual learning problem. The role of the self-organizing map is to adaptively cluster the inputs into appropriate task contexts - without explicit labels - and allocate network resources accordingly. Thus, it selectively routes the inputs in accord with previous experience, ensuring that past learning is maintained and does not interfere with current learning. Out method is intuitive, memoryless, and performs on par with current state-of-the-art approaches on standard benchmarks.

2019-04-19

ArXiv (prépublication)

arxiv.org

A Survey on Practical Applications of Multi-Armed and Contextual Bandits

Djallel Bouneffouf

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and i… (voir plus)nformation retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, 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 state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.

2019-04-02

ArXiv (prépublication)

arxiv.org

Predicting conversion to psychosis in clinical high risk patients using resting-state functional MRI features

Jolie Mcdonnell

W. Hord

Jenna Reinen

Pablo Polosecki

Guillermo Cecchi

Recent progress in artificial intelligence provides researchers with a powerful set of machine learning tools for analyzing brain imaging da… (voir plus)ta. In this work, we explore a variety of classification algorithms and functional network features derived from resting-state fMRI data collected from clinical high-risk (prodromal schizophrenia) patients and controls, trying to identify features predictive of conversion to psychosis among a subset of CHR patients. While there are many existing studies suggesting that functional network features can be highly discriminative of schizophrenia when analyzing fMRI of patients suffering from the disease vs controls, few studies attempt to explore a similar approach to actual prediction of future psychosis development ahead of time, in the prodromal stage. Our preliminary results demonstrate the potential of fMRI functional network features to predict the conversion to psychosis in CHR patients. However, given the high variance of our results across different classifiers and subsets of data, a more extensive empirical investigation is required to reach more robust conclusions.

2019-02-16

Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging (publié)

doi.org

Learning Brain Dynamics from Calcium Imaging with Coupled van der Pol and LSTM

Germán Abrevaya

Aleksandr Y. Aravkin

Guillermo Cecchi

James Kozloski

Pablo Polosecki

Peng Zheng

Silvina Ponce Dawson

Juliana Y. Rhee

David Daniel Cox

Many real-world data sets, especially in biology, are produced by complex nonlinear dynamical systems. In this paper, we focus on brain calc… (voir plus)ium imaging (CaI) of different organisms (zebrafish and rat), aiming to build a model of joint activation dynamics in large neuronal populations, including the whole brain of zebrafish. We propose a new approach for capturing dynamics of temporal SVD components that uses the coupled (multivariate) van der Pol (VDP) oscillator, a nonlinear ordinary differential equation (ODE) model describing neural activity, with a new parameter estimation technique that combines variable projection optimization and stochastic search. We show that the approach successfully handles nonlinearities and hidden state variables in the coupled VDP. The approach is accurate, achieving 0.82 to 0.94 correlation between the actual and model-generated components, and interpretable, as VDP’s coupling matrix reveals anatomically meaningful positive (excitatory) and negative (inhibitory) interactions across different brain subsystems corresponding to spatial SVD components. Moreover, VDP is comparable to (or sometimes better than) recurrent neural networks (LSTM) for (short-term) prediction of future brain activity; VDP needs less parameters to train, which was a plus on our small training data. Finally, the overall best predictive method, greatly outperforming both VDP and LSTM in shortand long-term predicitve settings on both datasets, was the new hybrid VDP-LSTM approach that used VDP to simulate large domain-specific dataset for LSTM pretraining; note that simple LSTM data-augmentation via noisy versions of training data was much less effective.

2019-01-01

(publié)

www.semanticscholar.org

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

Matthew D Riemer

Ignacio Cases

Robert Ajemian

Miao Liu

Yuhai Tu

Gerald Tesauro

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neura… (voir plus)l network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.

2019-01-01

ICLR.cc/2019/Conference (poster)

openreview.net

Machine Learning and Interpretation in Neuroimaging

Georg Langs

Leila Wehbe

Guillermo Cecchi

Kai-min Kevin Chang

Brian G Murphy

2014-01-01

Lecture Notes in Computer Science (publié)

doi.org

Cognitive Models as Simulators: Using Cognitive Models to Tap into Implicit Human Feedback

Ardavan S Nobandegani

Thomas R. Shultz