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

George Adamopoulos

Research Intern

Ivan Anokhin

PhD - Université de Montréal

Co-supervisor :

Samira Ebrahimi Kahou

Rifat Arefin

PhD - Université de Montréal

Arjun Ashok

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

PhD - Concordia University

Principal supervisor :

Mohammad Javad Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

Collaborating researcher - Université de Montréal

Wagner Drew

Master's Research - Concordia University

Principal supervisor :

Mirco Ravanelli

Mojtaba Faramarzi

PhD - Université de Montréal

Kshitij Gupta

Collaborating researcher

Co-supervisor :

Sarath Chandar

Parviz Haggi Mani

Independent visiting researcher - -

Nadhir Hassen

Collaborating researcher - Université de Montréal

Master's Research

Collaborating Alumni - Université de Montréal

Principal supervisor :

Ioannis Mitliagkas

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

PhD - Université de Montréal

PhD - Université de Montréal

Zafir Khalid

Master's Research - Concordia University

Principal supervisor :

Collaborating researcher - Université de Montréal

Neeraj Kumar

Collaborating Alumni - Université de Montréal

Gwen Legate

PhD - Concordia University

Principal supervisor :

David Lemay

Master's Research - Université de Montréal

Amin Mansouri

Collaborating Alumni - Université de Montréal

Collaborating researcher

Andrei Mircea

PhD - Université de Montréal

Collaborating researcher - Université de Montréal

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Principal supervisor :

Doina Precup

Timothy Nest

PhD - Université de Montréal

Co-supervisor :

Eilif B. Muller

Mohammad Pezeshki

Collaborating researcher

Co-supervisor :

Collaborating researcher - Polytechnique Montréal

Motahareh Pourrahimi

PhD - McGill University

Principal supervisor :

Pouya Bashivan

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

Matthew Riemer

PhD - Université de Montréal

Alexis Roger

PhD - McGill University

Principal supervisor :

Blake Richards

Munish Sathish Kumar

Collaborating researcher

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

Gopeshh Subbaraj

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Collaborating Alumni - Université de Montréal

Sihui Wei

Undergraduate - McGill University

Andrew Williams

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

He Zhu

PhD - McGill University

Publications

AI Agents Learn to Trust

Ardavan S. Nobandegani

Jean-christophe Gagnon-audet

T. Shultz

2022-12-31

Annual Meeting of the Cognitive Science Society (published)

dblp.uni-trier.de

GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models

Germán Abrevaya

Mahta Ramezanian-Panahi

Pablo Polosecki

Silvina Ponce Dawson

Guillermo Cecchi

Guillaume Dumas

Scientiﬁc Machine Learning (SciML) is a burgeoning ﬁeld that synergistically combines domain-aware and interpretable models with agnosti… (see more)c machine learning techniques. In this work, we introduce GOKU-UI, an evolution of the SciML generative model GOKU-nets. The GOKU-UI broadens the original model’s spectrum to incorporate other classes of differential equations, such as Stochastic Differential Equations (SDEs), and integrates a distributed, i.e. ubiquitous, inference through attention mechanisms and a novel multiple shooting training strategy in the latent space. These enhancements have led to a signiﬁcant increase in its performance in both reconstruction and forecast tasks, as demonstrated by our evaluation of simulated and empirical data. Speciﬁcally, GOKU-UI outperformed all baseline models on synthetic datasets even with a training set 32-fold smaller, underscoring its remarkable data efﬁciency. Furthermore, when applied to empirical human brain data, while incorporating stochastic Stuart-Landau

2022-12-31

arXiv.org (preprint)

Gradient Masked Averaging for Federated Learning

Irene Tenison

Sai Aravind Sreeramadas

Vaikkunth Mugunthan

Edouard Oyallon

Federated learning (FL) is an emerging paradigm that permits a large number of clients with heterogeneous data to coordinate learning of a u… (see more)nified global model without the need to share data amongst each other. A major challenge in federated learning is the heterogeneity of data across client, which can degrade the performance of standard FL algorithms. Standard FL algorithms involve averaging of model parameters or gradient updates to approximate the global model at the server. However, we argue that in heterogeneous settings, averaging can result in information loss and lead to poor generalization due to the bias induced by dominant client gradients. We hypothesize that to generalize better across non-i.i.d datasets, the algorithms should focus on learning the invariant mechanism that is constant while ignoring spurious mechanisms that differ across clients. Inspired from recent works in Out-of-Distribution generalization, we propose a gradient masked averaging approach for FL as an alternative to the standard averaging of client updates. This aggregation technique for client updates can be adapted as a drop-in replacement in most existing federated algorithms. We perform extensive experiments on multiple FL algorithms with in-distribution, real-world, feature-skewed out-of-distribution, and quantity imbalanced datasets and show that it provides consistent improvements, particularly in the case of heterogeneous clients.

2022-12-31

Trans. Mach. Learn. Res. (published)

openreview.net

Towards Continual Reinforcement Learning: A Review and Perspectives

Khimya Khetarpal

Matthew D Riemer

Doina Precup

2022-12-21

Journal of Artificial Intelligence Research (published)

arxiv.org

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

Proceedings of The 1st 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 (published)

openreview.net

Aligning MAGMA by Few-Shot Learning and Finetuning

Jean-Charles Layoun

Alexis Roger

2022-10-17

ArXiv (preprint)

Jean-christophe Gagnon-audet

arxiv.org

Generative Models of Brain Dynamics

Mahta Ramezanian-Panahi

Germán Abrevaya

Vikram Voleti

Guillaume Dumas

2022-07-14

Frontiers in Artificial Intelligence (published)

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

2022-06-27

Proceedings of the 39th International Conference on Machine Learning (published)

proceedings.mlr.press

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))