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

Continual Learning In Environments With Polynomial Mixing Times

Matthew Riemer

Sharath Chandra Raparthy

Ignacio Cases

Gopeshh Subbaraj

Maximilian Puelma Touzel

The mixing time of the Markov chain induced by a policy limits performance in real-world continual learning scenarios. Yet, the effect of mi… (see more)xing times on learning in continual reinforcement learning (RL) remains underexplored. In this paper, we characterize problems that are of long-term interest to the development of continual RL, which we call scalable MDPs, through the lens of mixing times. In particular, we theoretically establish that scalable MDPs have mixing times that scale polynomially with the size of the problem. We go on to demonstrate that polynomial mixing times present significant difficulties for existing approaches, which suffer from myopic bias and stale bootstrapped estimates. To validate our theory, we study the empirical scaling behavior of mixing times with respect to the number of tasks and task duration for high performing policies deployed across multiple Atari games. Our analysis demonstrates both that polynomial mixing times do emerge in practice and how their existence may lead to unstable learning behavior like catastrophic forgetting in continual learning settings.

2022-11-28

Neural Information Processing Systems (Accept)

Continual Learning with Foundation Models: An Empirical Study of Latent Replay

Oleksiy Ostapenko

Timothee LESORT

Pau Rodríguez

Md Rifat Arefin

Arthur Douillard

Laurent Charlin

Rapid development of large-scale pre-training has resulted in foundation models that can act as effective feature extractors on a variety of… (see more) downstream tasks and domains. Motivated by this, we study the efficacy of pre-trained vision models as a foundation for downstream continual learning (CL) scenarios. Our goal is twofold. First, we want to understand the compute-accuracy trade-off between CL in the raw-data space and in the latent space of pre-trained encoders. Second, we investigate how the characteristics of the encoder, the pre-training algorithm and data, as well as of the resulting latent space affect CL performance. For this, we compare the efficacy of various pre-trained models in large-scale benchmarking scenarios with a vanilla replay setting applied in the latent and in the raw-data space. Notably, this study shows how transfer, forgetting, task similarity and learning are dependent on the input data characteristics and not necessarily on the CL algorithms. First, we show that under some circumstances reasonable CL performance can readily be achieved with a non-parametric classifier at negligible compute. We then show how models pre-trained on broader data result in better performance for various replay sizes. We explain this with representational similarity and transfer properties of these representations. Finally, we show the effectiveness of self-supervised pre-training for downstream domains that are out-of-distribution as compared to the pre-training domain. We point out and validate several research directions that can further increase the efficacy of latent CL including representation ensembling. The diverse set of datasets used in this study can serve as a compute-efficient playground for further CL research. The codebase is available under https://github.com/oleksost/latent_CL.

2022-11-27

Conference on Lifelong Learning Agents (published)

proceedings.mlr.press

APP: Anytime Progressive Pruning

Diganta Misra

Bharat Runwal

Tianlong Chen

Zhangyang Wang

With the latest advances in deep learning, several methods have been investigated for optimal learning settings in scenarios where the data … (see more)stream is continuous over time. However, training sparse networks in such settings has often been overlooked. In this paper, we explore the problem of training a neural network with a target sparsity in a particular case of online learning: the anytime learning at macroscale paradigm (ALMA). We propose a novel way of progressive pruning, referred to as \textit{Anytime Progressive Pruning} (APP); the proposed approach significantly outperforms the baseline dense and Anytime OSP models across multiple architectures and datasets under short, moderate, and long-sequence training. Our method, for example, shows an improvement in accuracy of

2022-11-16

ACML.org/2022/Workshop/CLL (accepted)

Aligning MAGMA by Few-Shot Learning and Finetuning

Jean-Charles Layoun

Alexis Roger

2022-10-17

ArXiv (preprint)

arxiv.org

Towards Scaling Difference Target Propagation by Learning Backprop Targets

The development of biologically-plausible learning algorithms is important for understanding learning in the brain, but most of them fail to… (see more) scale-up to real-world tasks, limiting their potential as explanations for learning by real brains. As such, it is important to explore learning algorithms that come with strong theoretical guarantees and can match the performance of backpropagation (BP) on complex tasks. One such algorithm is Difference Target Propagation (DTP), a biologically-plausible learning algorithm whose close relation with Gauss-Newton (GN) optimization has been recently established. However, the conditions under which this connection rigorously holds preclude layer-wise training of the feedback pathway synaptic weights (which is more biologically plausible). Moreover, good alignment between DTP weight updates and loss gradients is only loosely guaranteed and under very specific conditions for the architecture being trained. In this paper, we propose a novel feedback weight training scheme that ensures both that DTP approximates BP and that layer-wise feedback weight training can be restored without sacrificing any theoretical guarantees. Our theory is corroborated by experimental results and we report the best performance ever achieved by DTP on CIFAR-10 and ImageNet 32

2022-07-18

International Conference on Machine Learning (Accept for Short Presentation)

Jean-christophe Gagnon-audet

proceedings.mlr.press

Generative Models of Brain Dynamics

Mahta Ramezanian-Panahi

Germán Abrevaya

Vikram Voleti

Guillaume Dumas

2022-07-14

Frontiers in Artificial Intelligence (published)

Parametric Scattering Networks

Shanel Gauthier

Benjamin Therien

Laurent Alsène-Racicot

Michael Eickenberg

The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yi… (see more)eld more discriminative representations compared to other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We show that our learned versions of the scattering transform yield significant performance gains in small-sample classification settings over the standard scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract effective representations.

2022-05-31

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (published)

Jean-christophe Gagnon-audet

arxiv.org

A Remedy For Distributional Shifts Through Expected Domain Translation.

Soroosh Shahtalebi

Frank Rudzicz

Machine learning models often fail to generalize to unseen domains due to the distributional shifts. A family of such shifts, “correlation… (see more) shifts,” is caused by spurious correlations in the data. It is studied under the overarching topic of “domain generalization.” In this work, we employ multi-modal translation networks to tackle the correlation shifts that appear when data is sampled out-of-distribution. Learning a generative model from training domains enables us to translate each training sample under the special characteristics of other possible domains. We show that by training a predictor solely on the generated samples, the spurious correlations in training domains average out, and the invariant features corresponding to true correlations emerge. Our proposed technique, Expected Domain Translation (EDT), is benchmarked on the Colored MNIST dataset and drastically improves the state-of-the-art classification accuracy by 38% with train-domain validation model selection.

2022-05-22

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

Compositional Attention: Disentangling Search and Retrieval

Sarthak Mittal

Sharath Chandra Raparthy

Yoshua Bengio

Guillaume Lajoie

Multi-head, key-value attention is the backbone of the widely successful Transformer model and its variants. This attention mechanism uses m… (see more)ultiple parallel key-value attention blocks (called heads), each performing two fundamental computations: (1) search - selection of a relevant entity from a set via query-key interactions, and (2) retrieval - extraction of relevant features from the selected entity via a value matrix. Importantly, standard attention heads learn a rigid mapping between search and retrieval. In this work, we first highlight how this static nature of the pairing can potentially: (a) lead to learning of redundant parameters in certain tasks, and (b) hinder generalization. To alleviate this problem, we propose a novel attention mechanism, called Compositional Attention, that replaces the standard head structure. The proposed mechanism disentangles search and retrieval and composes them in a dynamic, flexible and context-dependent manner through an additional soft competition stage between the query-key combination and value pairing. Through a series of numerical experiments, we show that it outperforms standard multi-head attention on a variety of tasks, including some out-of-distribution settings. Through our qualitative analysis, we demonstrate that Compositional Attention leads to dynamic specialization based on the type of retrieval needed. Our proposed mechanism generalizes multi-head attention, allows independent scaling of search and retrieval, and can easily be implemented in lieu of standard attention heads in any network architecture.

2022-04-24

International Conference on Learning Representations (Accept (Spotlight))

Summarizing Societies: Agent Abstraction in Multi-Agent Reinforcement Learning

Maximilian Puelma Touzel

Matthew D Riemer

Rupali Bhati

Agents cannot make sense of many-agent societies through direct consideration of small-scale, low-level agent identities, but instead must r… (see more)ecognize emergent collective identities. Here, we take a first step towards a framework for recognizing this structure in large groups of low-level agents so that they can be modeled as a much smaller number of high-level agents—a process that we call agent abstraction. We illustrate this process by extending bisimulation metrics for state abstraction in reinforcement learning to the setting of multi-agent reinforcement learning and analyze a straightforward, if crude, abstraction based on experienced joint actions. It addresses non-stationarity due to other learning agents by improving minimax regret by a intuitive factor. To test if this compression factor provides signal for higher-level agency, we applied it to a large dataset of human play of the popular social dilemma game Diplomacy. We find that it correlates strongly with the degree of ground-truth abstraction of low-level units into the human players.

2022-04-20

ICLR.cc/2022/Workshop/Cells2Societies (poster)