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

Deep Generative Sampling in the Dual Divergence Space: A Data-efficient&Interpretative Approach for Generative AI

Sahil Garg

Anderson Schneider

Anant Raj

Kashif Rasul

Yuriy Nevmyvaka

S. Gopal

Amit Dhurandhar

Guillermo A. Cecchi

Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly amb… (see more)itious, which involves generating samples of entire multivariate time series that resemble images. However, the statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects. This issue is especially problematic for deep generative models that follow the conventional approach of generating samples from a canonical distribution and then decoding or denoising them to match the true data distribution. In contrast, our method is grounded in information theory and aims to implicitly characterize the distribution of images, particularly the (global and local) dependency structure between pixels. We achieve this by empirically estimating its KL-divergence in the dual form with respect to the respective marginal distribution. This enables us to perform generative sampling directly in the optimized 1-D dual divergence space. Specifically, in the dual space, training samples representing the data distribution are embedded in the form of various clusters between two end points. In theory, any sample embedded between those two end points is in-distribution w.r.t. the data distribution. Our key idea for generating novel samples of images is to interpolate between the clusters via a walk as per gradients of the dual function w.r.t. the data dimensions. In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution. We provide strong theoretical guarantees along with an extensive empirical evaluation using many real-world datasets from diverse domains, establishing the superiority of our approach w.r.t. state-of-the-art deep learning methods.

2024-04-09

ArXiv (preprint)

arxiv.org

Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok

Tikeng Notsawo Pascal Junior

Pascal Notsawo

2024-03-03

ICLR.cc/2024/Workshop/ME-FoMo (poster)

Jean-christophe Gagnon-audet

Effective Latent Differential Equation Models via Attention and Multiple Shooting

Germán Abrevaya

Mahta Ramezanian-Panahi

Pablo Polosecki

Silvina Ponce Dawson

Guillermo Cecchi

Guillaume Dumas

2024-02-25

Transactions on Machine Learning Research (accepted)

Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning

Mohammad-Javad Darvishi-Bayazi

Mohammad Sajjad Ghaemi

Timothee LESORT

Md Rifat Arefin

Jocelyn Faubert

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With … (see more)the advancement of artificial intelligence methods and machine learning techniques, the potential for accurate data-driven diagnoses and effective treatments has grown significantly. However, applying machine learning algorithms to real-world datasets presents diverse challenges at multiple levels. The scarcity of labelled data, especially in low regime scenarios with limited availability of real patient cohorts due to high costs of recruitment, underscores the vital deployment of scaling and transfer learning techniques. In this study, we explore a real-world pathology classification task to highlight the effectiveness of data and model scaling and cross-dataset knowledge transfer. As such, we observe varying performance improvements through data scaling, indicating the need for careful evaluation and labelling. Additionally, we identify the challenges of possible negative transfer and emphasize the significance of some key components to overcome distribution shifts and potential spurious correlations and achieve positive transfer. We see improvement in the performance of the target model on the target (NMT) datasets by using the knowledge from the source dataset (TUAB) when a low amount of labelled data was available. Our findings indicate a small and generic model (e.g. ShallowNet) performs well on a single dataset, however, a larger model (e.g. TCN) performs better on transfer and learning from a larger and diverse dataset.

2024-01-31

Computers in Biology and Medicine (published)

arxiv.org

Dance of the Neurons: Unraveling Sex from Brain Signals (short paper).

Mohammad-Javad Darvishi Bayazi

Mohammad Sajjad Ghaemi

Jocelyn Faubert

2023-12-31

ML4CMH@AAAI (published)

dblp.uni-trier.de

Towards Machines that Trust: AI Agents Learn to Trust in the Trust Game

Ardavan S. Nobandegani

Thomas Shultz

Widely considered a cornerstone of human morality, trust shapes many aspects of human social interactions. In this work, we present a theore… (see more)tical analysis of the

2023-12-19

ArXiv (preprint)

arxiv.org

Maximum State Entropy Exploration using Predecessor and Successor Representations

Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misp… (see more)laced items. These exploration skills necessarily track where they have been so that they can plan for finding items with relative efficiency. Contemporary exploration algorithms often learn a less efficient exploration strategy because they either condition only on the current state or simply rely on making random open-loop exploratory moves. In this work, we propose

2023-12-11

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

Challenging Common Assumptions About Catastrophic Forgetting and Knowledge Accumulation

Timothee LESORT

Pau Rodríguez

Md Rifat Arefin

Building learning agents that can progressively learn and accumulate knowledge is the core goal of the continual learning (CL) research fiel… (see more)d. Unfortunately, training a model on new data usually compromises the performance on past data. In the CL literature, this effect is referred to as catastrophic forgetting (CF). CF has been largely studied, and a plethora of methods have been proposed to address it on short sequences of non-overlapping tasks. In such setups, CF always leads to a quick and significant drop in performance in past tasks. Nevertheless, despite CF, recent work showed that SGD training on linear models accumulates knowledge in a CL regression setup. This phenomenon becomes especially visible when tasks reoccur. We might then wonder if DNNs trained with SGD or any standard gradient-based optimization accumulate knowledge in such a way. Such phenomena would have interesting consequences for applying DNNs to real continual scenarios. Indeed, standard gradient-based optimization methods are significantly less computationally expensive than existing CL algorithms. In this paper, we study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence. We propose a new framework, SCoLe (Scaling Continual Learning), to investigate KA and discover that catastrophic forgetting has a limited effect on DNNs trained with SGD. When trained on long sequences with data sparsely re-occurring, the overall accuracy improves, which might be counter-intuitive given the CF phenomenon. We empirically investigate KA in DNNs under various data occurrence frequencies and propose simple and scalable strategies to increase knowledge accumulation in DNNs.

2023-11-19

Conference on Lifelong Learning Agents (published)

Mohammad Javad Darvishi Bayazi

proceedings.mlr.press

Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Hena Ghonia

Rishika Bhagwatkar

Arian Khorasani

George Adamopoulos

Roland Riachi

Nadhir Hassen

Marin Biloš

Sahil Garg

Anderson Schneider

Nicolas Chapados

Alexandre Drouin

Valentina Zantedeschi

Yuriy Nevmyvaka

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (see more)t and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.

2023-10-31

R0-FoMo @ Neural Information Processing Systems (poster)

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.

2023-10-23

Transactions on Machine Learning Research (accepted)

Comparison of Radiologists and Deep Learning for US Grading of Hepatic Steatosis

Pedro Vianna

Sara‐Ivana Calce

Pamela Boustros

Cassandra Larocque-Rigney

Laurent Patry-Beaudoin

Yi Hui Luo

Emre Aslan

John Marinos

Talal Alamri

Kim‐Nhien Vu

Jessica Murphy-Lavallée

Jean-Sébastien Billiard

Emmanuel Montagnon

Hongliang Li

Samuel Kadoury

Bich Nguyen

Shanel Gauthier

Benjamin Therien

Eugene Belilovsky … (see 4 more)

Guy Wolf

Michaël Chassé

Guy Cloutier

An Tang

Background Screening for nonalcoholic fatty liver disease (NAFLD) is suboptimal due to the subjective interpretation of US images. Purpose T… (see more)o evaluate the agreement and diagnostic performance of radiologists and a deep learning model in grading hepatic steatosis in NAFLD at US, with biopsy as the reference standard. Materials and Methods This retrospective study included patients with NAFLD and control patients without hepatic steatosis who underwent abdominal US and contemporaneous liver biopsy from September 2010 to October 2019. Six readers visually graded steatosis on US images twice, 2 weeks apart. Reader agreement was assessed with use of κ statistics. Three deep learning techniques applied to B-mode US images were used to classify dichotomized steatosis grades. Classification performance of human radiologists and the deep learning model for dichotomized steatosis grades (S0, S1, S2, and S3) was assessed with area under the receiver operating characteristic curve (AUC) on a separate test set. Results The study included 199 patients (mean age, 53 years ± 13 [SD]; 101 men). On the test set (n = 52), radiologists had fair interreader agreement (0.34 [95% CI: 0.31, 0.37]) for classifying steatosis grades S0 versus S1 or higher, while AUCs were between 0.49 and 0.84 for radiologists and 0.85 (95% CI: 0.83, 0.87) for the deep learning model. For S0 or S1 versus S2 or S3, radiologists had fair interreader agreement (0.30 [95% CI: 0.27, 0.33]), while AUCs were between 0.57 and 0.76 for radiologists and 0.73 (95% CI: 0.71, 0.75) for the deep learning model. For S2 or lower versus S3, radiologists had fair interreader agreement (0.37 [95% CI: 0.33, 0.40]), while AUCs were between 0.52 and 0.81 for radiologists and 0.67 (95% CI: 0.64, 0.69) for the deep learning model. Conclusion Deep learning approaches applied to B-mode US images provided comparable performance with human readers for detection and grading of hepatic steatosis. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Tuthill in this issue.

2023-09-30

Radiology (unknown)

Beyond performance: the role of task demand, effort, and individual differences in ab initio pilots

Mohammad-Javad Darvishi-Bayazi

Andrew Law

Sergio Mejia Romero

Sion Jennings

Jocelyn Faubert

Aviation safety depends on the skill and expertise of pilots to meet the task demands of flying an aircraft in an effective and efficient ma… (see more)nner. During flight training, students may respond differently to imposed task demands based on individual differences in capacity, physiological arousal, and effort. To ensure that pilots achieve a common desired level of expertise, training programs should account for individual differences to optimize pilot performance. This study investigates the relationship between task performance and physiological correlates of effort in ab initio pilots. Twenty-four participants conducted a flight simulator task with three difficulty levels and were asked to rate their perceived demand and effort using the NASA TLX. We recorded heart rate, EEG brain activity, and pupil size to assess changes in the participants’ mental and physiological states across different task demands. We found that, despite group-level correlations between performance error and physiological responses, individual differences in physiological responses to task demands reflected different levels of participant effort and task efficiency. These findings suggest that physiological monitoring of student pilots might provide beneficial insights to flight instructors to optimize pilot training at the individual level.

2023-08-27

Scientific Reports (published)