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 - McGill University

Principal supervisor :

Blake Richards

Mohammad Javad Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Karim Jerbi

Wagner Drew

Master's Research - Concordia University

Principal supervisor :

Mirco Ravanelli

Mojtaba Faramarzi

PhD - Université de Montréal

Nadhir Hassen

Collaborating Alumni - Université de Montréal

Master's Research

Nizar Islah

PhD - Université de Montréal

Principal supervisor :

Eilif Benjamin Muller

PhD - Université de Montréal

Collaborating researcher

Zafir Khalid

Master's Research - Concordia University

Principal supervisor :

Master's Research - Université de Montréal

Neeraj Kumar

Professional Master's - Université de Montréal

Gwen Legate

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

David Lemay

Master's Research - Université de Montréal

amin.mansouri@mila.quebec

Jonathan Lim

Collaborating researcher

Postdoctorate - Université de Montréal

Principal supervisor :

Master's Research - Université de Montréal

Collaborating Alumni

PhD - Université de Montréal

Master's Research - Université de Montréal

Diganta Misra

Master's Research - Université de Montréal

Timothy Nest

PhD - Université de Montréal

Co-supervisor :

Eilif Benjamin Muller

Mohammad Pezeshki

Collaborating researcher

Co-supervisor :

PhD - McGill University

Principal supervisor :

Pouya Bashivan

Mahta Ramezanian

Master's Research - Université de Montréal

Co-supervisor :

Guillaume Dumas

Roland Riachi

Collaborating researcher - Université de Montréal

Matthew Riemer

PhD - Université de Montréal

Alexis Roger

PhD - McGill University

Principal supervisor :

Blake Richards

Zahra Sheikhbahaee

Postdoctorate - Université de Montréal

Principal supervisor :

Guillaume Dumas

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

Gopeshh Subbaraj

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

PhD - Université de Montréal

Daria Yasafova

Master's Research - Université de Montréal

Publications

Towards Out-of-Distribution Adversarial Robustness

Adam Ibrahim

Charles Guille-Escuret

Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fail… (see more)s to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different

2023-06-20

ICML.cc/2023/Workshop/AdvML-Frontiers (published)

openreview.net

Dialogue System with Missing Observation

Djallel Bouneffouf

Mayank Agarwal

Within the domain of dialogue, the ability to orchestrate multiple independently trained dialogue agents to create a unified system is of pa… (see more)rticular importance. Where we define 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. In this work, we study the task of online dialogue orchestration where the user feedback associated with the dialogue agent may not always be observed. In order to address the missing feedback setting, we propose to combine the attentive contextual bandit approach with an unsupervised learning mechanism such as clustering. By leveraging clustering to estimate missing reward, we are able to learn from each incoming event, even those with missing rewards. Promising empirical results are obtained on proprietary conversational datasets.

2023-06-04

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (published)

Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data.

Guillaume Lam

P. Dixon

2023-06-01

Journal of Biomechanics (published)

Towards ethical multimodal systems

Alexis Roger

Esma Aimeur

2023-04-26

ArXiv (preprint)

A Survey on Compositional Generalization in Applications

Baihan Lin

Djallel Bouneffouf

2023-02-02

ArXiv (preprint)

Broken Neural Scaling Laws

Ethan Caballero

Kshitij Gupta

David Scott Krueger

We present a smoothly broken power law functional form (that we refer to as a Broken Neural Scaling Law (BNSL)) that accurately models&extra… (see more)polates the scaling behaviors of deep neural networks (i.e. how the evaluation metric of interest varies as amount of compute used for training (or inference), number of model parameters, training dataset size, model input size, number of training steps, or upstream performance varies) for various architectures&for each of various tasks within a large&diverse set of upstream&downstream tasks, in zero-shot, prompted,&finetuned settings. This set includes large-scale vision, language, audio, video, diffusion, generative modeling, multimodal learning, contrastive learning, AI alignment, AI capabilities, robotics, out-of-distribution (OOD) generalization, continual learning, transfer learning, uncertainty estimation / calibration, OOD detection, adversarial robustness, distillation, sparsity, retrieval, quantization, pruning, fairness, molecules, computer programming/coding, math word problems,"emergent phase transitions", arithmetic, supervised learning, unsupervised/self-supervised learning,&reinforcement learning (single agent&multi-agent). When compared to other functional forms for neural scaling, this functional form yields extrapolations of scaling behavior that are considerably more accurate on this set. Moreover, this functional form accurately models&extrapolates scaling behavior that other functional forms are incapable of expressing such as the nonmonotonic transitions present in the scaling behavior of phenomena such as double descent&the delayed, sharp inflection points present in the scaling behavior of tasks such as arithmetic. Lastly, we use this functional form to glean insights about the limit of the predictability of scaling behavior. Code is available at https://github.com/ethancaballero/broken_neural_scaling_laws

2023-02-01

ICLR.cc/2023/Conference (poster)

openreview.net

AI Agents Learn to Trust

Ardavan S. Nobandegani

T. Shultz

2023-01-01

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

Jean-Christophe Gagnon-Audet

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

2023-01-01

arXiv.org (preprint)

Gradient Masked Averaging for Federated Learning

Irene Tenison

Sai Aravind Sreeramadas

Vaikkunth Mugunthan

Edouard Oyallon

Eugene Belilovsky

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.

2023-01-01

Trans. Mach. Learn. Res. (published)

openreview.net

Lag-Llama: Towards Foundation Models for Time Series Forecasting

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Biloš

Hena Ghonia

N. Hassen

Anderson Schneider

Sahil Garg

Alexandre Drouin

Nicolas Chapados

Yuriy Nevmyvaka

Aiming to build foundation models for time-series forecasting and study their scaling behavior, we present here our work-in-progress on Lag-… (see more)Llama , a general-purpose univariate probabilistic time-series forecasting model trained on a large collection of time-series data. The model shows good zero-shot prediction capabilities on unseen “out-of-distribution” time-series datasets, outperforming supervised baselines. We use smoothly broken power-laws [7] to fit and predict model scaling behavior. The open source code is made available at https://github

2023-01-01

arXiv.org (preprint)

Towards Continual Reinforcement Learning: A Review and Perspectives

Khimya Khetarpal

Matthew D Riemer

Doina Precup

2022-12-22

Journal of Artificial Intelligence Research (published)

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

Oleksiy Ostapenko

Timothee LESORT

Pau Rodriguez

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. We will publish the code.

2022-11-28

Proceedings of The 1st Conference on Lifelong Learning Agents (published)

proceedings.mlr.press