Irina Rish

Biography

Irina Rish is a full professor at the Université de Montréal (UdeM), where she leads the Autonomous AI Lab, and a core academic member of Mila – Quebec Artificial Intelligence Institute.

In addition to holding a Canada Excellence Research Chair (CERC) and a CIFAR Chair, she leads the U.S. Department of Energy’s INCITE project on Scalable Foundation Models on Summit & Frontier supercomputers at the Oak Ridge Leadership Computing Facility. She co-founded and serves as CSO of Nolano.ai.

Rish’s current research interests include neural scaling laws and emergent behaviors (capabilities and alignment) in foundation models, as well as continual learning, out-of-distribution generalization and robustness.

Before joining UdeM in 2019, she was a research scientist at the IBM T.J. Watson Research Center, where she worked on various projects at the intersection of neuroscience and AI, and led the Neuro-AI challenge. She was awarded the IBM Eminence & Excellence Award and IBM Outstanding Innovation Award (2018), IBM Outstanding Technical Achievement Award (2017) and IBM Research Accomplishment Award (2009).

She holds 64 patents and has published 120 research papers, several book chapters, three edited books and a monograph on sparse modeling.

Current Students

Ivan Anokhin

PhD - Université de Montréal

Co-supervisor :

Samira Ebrahimi Kahou

ivan.anokhin@mila.quebec

rishika.bhagwatkar@mila.quebec

Rifat Arefin

PhD - Université de Montréal

Arjun Ashok

PhD - Université de Montréal

Co-supervisor :

Alexandre Drouin

arjun.ashok@mila.quebec

Master's Research - Université de Montréal

PhD - Université de Montréal

ethan.caballero@mila.quebec

Pietro Cagnasso

PhD - Concordia University

Principal supervisor :

pietro.cagnasso@mila.quebec

Mohammad Javad Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

amin.darabi@mila.quebec

Collaborating researcher - Université de Montréal

mj.darvishi92@gmail.com

Wagner Drew

Master's Research - Concordia University

Principal supervisor :

Mirco Ravanelli

drew.wagner@mila.quebec

kshitij.gupta@mila.quebec

Mojtaba Faramarzi

PhD - Université de Montréal

faramarm@mila.quebec

Kshitij Gupta

Collaborating researcher

Co-supervisor :

Sarath Chandar

Parviz Haggi Mani

Independent visiting researcher - -

parviz.haggi@gmail.com

Nadhir Hassen

Collaborating researcher - Université de Montréal

nadhir.hassen@mila.quebec

Niklas Herbster

Research Intern - Université de Montréal

Co-supervisor :

Gauthier Gidel

niklas.herbster@gmail.com

prateek.humane@mila.quebec

Prateek Humane

PhD

arnav-kumar.jain@mila.quebec

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

Eilif B. Muller

nizar.islah@mila.quebec

PhD - Université de Montréal

PhD - Université de Montréal

ayush.kaushal@mila.quebec

Arian Khorasani

Collaborating researcher - Université de Montréal

arian.khorasani@mila.quebec

Neeraj Kumar

Collaborating Alumni - Université de Montréal

neeraj.kumar@mila.quebec

Gwen Legate

PhD - Concordia University

Principal supervisor :

gwendolyne.legate@mila.quebec

David Lemay

Master's Research - Université de Montréal

david.lemay@mila.quebec

amin.mansouri@mila.quebec

Amin Mansouri

Collaborating Alumni - Université de Montréal

Collaborating researcher

memariaa@mila.quebec

Andrei Mircea

PhD - Université de Montréal

Collaborating researcher - Université de Montréal

diganta.misra@mila.quebec

motahareh.pourrahimi@mila.quebec

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Principal supervisor :

Doina Precup

moisescg@mila.quebec

Timothy Nest

PhD - Université de Montréal

Co-supervisor :

Eilif B. Muller

timothy.nest@mila.quebec

Jonathan Pilault

Collaborating researcher - Polytechnique Montréal

PhD - McGill University

Principal supervisor :

Pouya Bashivan

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

mahta.ramezanian@mila.quebec

Matthew Riemer

PhD - Université de Montréal

matthew.riemer@mila.quebec

Alexis Roger

PhD - McGill University

Principal supervisor :

Blake Richards

alexis.roger@mila.quebec

munish.sathish-kumar@mila.quebec

Munish Sathish Kumar

Collaborating researcher

Zibo Shang

PhD - Université de Montréal

zibo.shang@mila.quebec

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

vaibhav.singh@mila.quebec

Gopeshh Subbaraj

PhD - Université de Montréal

gopeshh.subbaraj@mila.quebec

PhD - Université de Montréal

Co-supervisor :

benjamin.therien@mila.quebec

Collaborating researcher - Université de Montréal

tejas.vaidhya@mila.quebec

andrew.williams@mila.quebec

Sihui Wei

Undergraduate - McGill University

sihui.wei@mila.quebec

Andrew Williams

PhD - Université de Montréal

Co-supervisor :

Nicolas Chapados

daria.yasafova@mila.quebec

Daria Yasafova

Master's Research - Université de Montréal

He Zhu

PhD - McGill University

he.zhu@mila.quebec

Publications

Optimizing deep learning for Magnetoencephalography (MEG): From sensory perception to sex prediction and brain fingerprinting

Arthur Dehgan

Jean-christophe Gagnon-audet

Karim Jerbi

2021-12-31

2022 Conference on Cognitive Computational Neuroscience (published)

PRACTICAL GUIDE

Paolo Bellavista

2021-12-31

(published)

www.semanticscholar.org

Generative Models of Brain Dynamics -- A review

Mahta Ramezanian Panahi

Germán Abrevaya

Vikram Voleti

Guillaume Dumas

The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-tw… (see more)entieth century. Recent developments in artificial intelligence (AI) have accelerated this progress. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.

2021-12-21

ArXiv (preprint)

arxiv.org

Adversarial Feature Desensitization

Neural networks are known to be vulnerable to adversarial attacks -- slight but carefully constructed perturbations of the inputs which can … (see more)drastically impair the network's performance. Many defense methods have been proposed for improving robustness of deep networks by training them on adversarially perturbed inputs. However, these models often remain vulnerable to new types of attacks not seen during training, and even to slightly stronger versions of previously seen attacks. In this work, we propose a novel approach to adversarial robustness, which builds upon the insights from the domain adaptation field. Our method, called Adversarial Feature Desensitization (AFD), aims at learning features that are invariant towards adversarial perturbations of the inputs. This is achieved through a game where we learn features that are both predictive and robust (insensitive to adversarial attacks), i.e. cannot be used to discriminate between natural and adversarial data. Empirical results on several benchmarks demonstrate the effectiveness of the proposed approach against a wide range of attack types and attack strengths. Our code is available at https://github.com/BashivanLab/afd.

2021-12-06

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

Jean-christophe Gagnon-audet

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

Kartik Ahuja

Ethan Caballero

Dinghuai Zhang

Yoshua Bengio

Ioannis Mitliagkas

The invariance principle from causality is at the heart of notable approaches such as invariant risk minimization (IRM) that seek to address… (see more) out-of-distribution (OOD) generalization failures. Despite the promising theory, invariance principle-based approaches fail in common classification tasks, where invariant (causal) features capture all the information about the label. Are these failures due to the methods failing to capture the invariance? Or is the invariance principle itself insufficient? To answer these questions, we revisit the fundamental assumptions in linear regression tasks, where invariance-based approaches were shown to provably generalize OOD. In contrast to the linear regression tasks, we show that for linear classification tasks we need much stronger restrictions on the distribution shifts, or otherwise OOD generalization is impossible. Furthermore, even with appropriate restrictions on distribution shifts in place, we show that the invariance principle alone is insufficient. We prove that a form of the information bottleneck constraint along with invariance helps address key failures when invariant features capture all the information about the label and also retains the existing success when they do not. We propose an approach that incorporates both of these principles and demonstrate its effectiveness in several experiments.

2021-12-06

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

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly… (see more) in the light of recent breakthroughs achieved by large-scale pre-trained models such as GPT-3, CLIP and DALL-e. Accurately predicting the neural network performance with increasing resources such as data, compute and model size provides a more comprehensive evaluation of different approaches across multiple scales, as opposed to traditional point-wise comparisons of fixed-size models on fixed-size benchmarks, and, most importantly, allows for focus on the best-scaling, and thus most promising in the future, approaches. In this work, we consider a challenging problem of few-shot learning in image classification, especially when the target data distribution in the few-shot phase is different from the source, training, data distribution, in a sense that it includes new image classes not encountered during training. Our current main goal is to investigate how the amount of pre-training data affects the few-shot generalization performance of standard image classifiers. Our key observations are that (1) such performance improvements are well-approximated by power laws (linear log-log plots) as the training set size increases, (2) this applies to both cases of target data coming from either the same or from a different domain (i.e., new classes) as the training data, and (3) few-shot performance on new classes converges at a faster rate than the standard classification performance on previously seen classes. Our findings shed new light on the relationship between scale and generalization.

2021-10-12

arXiv (preprint)

Approximate Bayesian Optimisation for Neural Networks

Nadhir Hassen

2021-08-26

ArXiv (preprint)

arxiv.org

Toward Optimal Solution for the Context-Attentive Bandit Problem

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Yasaman Khazaeni

2021-08-18

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (published)

Sequoia: A Software Framework to Unify Continual Learning Research

Pau Rodríguez

J. Hurtado

Dominic Zhao

Ryan Lindeborg

Timothee LESORT

David Vázquez

Laurent Charlin

Massimo Caccia

The field of Continual Learning (CL) seeks to develop algorithms that accumulate knowledge and skills over time through interaction with non… (see more)-stationary environments. In practice, a plethora of evaluation procedures (settings) and algorithmic solutions (methods) exist, each with their own potentially disjoint set of assumptions. This variety makes measuring progress in CL difficult. We propose a taxonomy of settings, where each setting is described as a set of assumptions. A tree-shaped hierarchy emerges from this view, where more general settings become the parents of those with more restrictive assumptions. This makes it possible to use inheritance to share and reuse research, as developing a method for a given setting also makes it directly applicable onto any of its children. We instantiate this idea as a publicly available software framework called Sequoia, which features a wide variety of settings from both the Continual Supervised Learning (CSL) and Continual Reinforcement Learning (CRL) domains. Sequoia also includes a growing suite of methods which are easy to extend and customize, in addition to more specialized methods from external libraries. We hope that this new paradigm and its first implementation can help unify and accelerate research in CL. You can help us grow the tree by visiting www.github.com/lebrice/Sequoia.

2021-08-01

ArXiv (preprint)

Jean-christophe Gagnon-audet

Learning Brain Dynamics With Coupled Low-Dimensional Nonlinear Oscillators and Deep Recurrent Networks.

Germán Abrevaya

Guillaume Dumas

Aleksandr Y. Aravkin

Peng Zheng

James R. Kozloski

Pablo Polosecki

Guillaume Lajoie

David D. Cox

Silvina Ponce Dawson

Guillermo A. Cecchi

Many natural systems, especially biological ones, exhibit complex multivariate nonlinear dynamical behaviors that can be hard to capture by … (see more)linear autoregressive models. On the other hand, generic nonlinear models such as deep recurrent neural networks often require large amounts of training data, not always available in domains such as brain imaging; also, they often lack interpretability. Domain knowledge about the types of dynamics typically observed in such systems, such as a certain type of dynamical systems models, could complement purely data-driven techniques by providing a good prior. In this work, we consider a class of ordinary differential equation (ODE) models known as van der Pol (VDP) oscil lators and evaluate their ability to capture a low-dimensional representation of neural activity measured by different brain imaging modalities, such as calcium imaging (CaI) and fMRI, in different living organisms: larval zebrafish, rat, and human. We develop a novel and efficient approach to the nontrivial problem of parameters estimation for a network of coupled dynamical systems from multivariate data and demonstrate that the resulting VDP models are both accurate and interpretable, as VDP's coupling matrix reveals anatomically meaningful excitatory and inhibitory interactions across different brain subsystems. VDP outperforms linear autoregressive models (VAR) in terms of both the data fit accuracy and the quality of insight provided by the coupling matrices and often tends to generalize better to unseen data when predicting future brain activity, being comparable to and sometimes better than the recurrent neural networks (LSTMs). Finally, we demonstrate that our (generative) VDP model can also serve as a data-augmentation tool leading to marked improvements in predictive accuracy of recurrent neural networks. Thus, our work contributes to both basic and applied dimensions of neuroimaging: gaining scientific insights and improving brain-based predictive models, an area of potentially high practical importance in clinical diagnosis and neurotechnology.

2021-06-06

Neural Computation (unknown)

Double-Linear Thompson Sampling for Context-Attentive Bandits

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Yasaman Khazaeni

In this paper, we analyze and extend an online learning frame-work known as Context-Attentive Bandit, motivated by various practical applica… (see more)tions, from medical diagnosis to dialog systems, where due to observation costs only a small subset of a potentially large number of context variables can be observed at each iteration; however, the agent has a freedom to choose which variables to observe. We derive a novel algorithm, called Context-Attentive Thompson Sampling (CATS), which builds upon the Linear Thompson Sampling approach, adapting it to Context-Attentive Bandit setting. We provide a theoretical regret analysis and an extensive empirical evaluation demonstrating advantages of the proposed approach over several baseline methods on a variety of real-life datasets.

2021-06-05

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

arxiv.org

Toward Skills Dialog Orchestration with Online Learning

Djallel Bouneffouf

Raphael Feraud

Sohini Upadhyay

Mayank Agarwal

Yasaman Khazaeni

Building multi-domain AI agents is a challenging task and an open problem in the area of AI. Within the domain of dialog, the ability to orc… (see more)hestrate multiple independently trained dialog agents, or skills, to create a unified system is of particular significance. In this work, we study the task of online posterior dialog orchestration, where we define posterior orchestration as the task of selecting a subset of skills which most appropriately answer a user input using features extracted from both the user input and the individual skills. To account for the various costs associated with extracting skill features, we consider online posterior orchestration under a skill execution budget. We formalize this setting as Context Attentive Bandit with Observations (CABO), a variant of context attentive bandits, and evaluate it on proprietary conversational datasets.

2021-06-05

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)