Irina Rish

Biographie

Irina Rish est professeure titulaire à l'Université de Montréal (UdeM), où elle dirige le Laboratoire d'IA autonome. Membre du corps professoral de Mila – Institut québécois d’intelligence artificielle, elle est titulaire d'une chaire d'excellence en recherche du Canada (CERC) et d'une chaire en IA Canada-CIFAR. Irina dirige le projet INCITE du ministère américain de l'Environnement au sujet des modèles de fondation évolutifs sur les superordinateurs Summit et Frontier à l'Oak Ridge Leadership Computing Facility (OLCF). Elle est cofondatrice et directrice scientifique de Nolano.ai.

Ses recherches actuelles portent sur les lois de mise à l'échelle neuronale et les comportements émergents (capacités et alignement) dans les modèles de fondation, ainsi que sur l'apprentissage continu, la généralisation hors distribution et la robustesse. Avant de se joindre à l'UdeM en 2019, Irina était chercheuse au Centre de recherche IBM Thomas J. Watson, où elle a travaillé sur divers projets à l'intersection des neurosciences et de l'IA, et dirigé le défi NeuroAI. Elle a reçu plusieurs prix IBM : ceux de l’excellence et de l’innovation exceptionnelle (2018), celui de la réalisation technique exceptionnelle (2017), et celui de l’accomplissement en recherche (2009). Elle détient 64 brevets et a écrit plus de 120 articles de recherche, plusieurs chapitres de livres, trois livres publiés et une monographie sur la modélisation éparse.

Étudiants actuels

George Adamopoulos

Stagiaire de recherche

Ivan Anokhin

Doctorat - UdeM

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Doctorat - UdeM

Arjun Ashok

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

Doctorat - UdeM

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

Mohammad Javad Darvishi Bayazi

Amin Darabi

Doctorat - UdeM

Collaborateur·rice de recherche - UdeM

Wagner Drew

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Collaborateur·rice de recherche

Co-superviseur⋅e :

Sarath Chandar

Parviz Haggi Mani

Visiteur de recherche indépendant - -

Nadhir Hassen

Collaborateur·rice de recherche - UdeM

Niklas Herbster

Stagiaire de recherche - UdeM

Co-superviseur⋅e :

Maîtrise recherche

Collaborateur·rice alumni - UdeM

Superviseur⋅e principal⋅e :

Ioannis Mitliagkas

Nizar Islah

Doctorat - UdeM

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Maîtrise recherche - Concordia

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche - UdeM

Neeraj Kumar

Collaborateur·rice alumni - UdeM

Gwen Legate

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Eugene Belilovsky

David Lemay

Maîtrise recherche - UdeM

Amin Mansouri

Collaborateur·rice alumni - UdeM

Collaborateur·rice de recherche

Doctorat - UdeM

Collaborateur·rice de recherche - UdeM

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Superviseur⋅e principal⋅e :

Doina Precup

Timothy Nest

Doctorat - UdeM

Co-superviseur⋅e :

Eilif B. Muller

Mohammad Pezeshki

Collaborateur·rice de recherche

Co-superviseur⋅e :

Collaborateur·rice de recherche - Polytechnique

Motahareh Pourrahimi

Doctorat - McGill

Superviseur⋅e principal⋅e :

Pouya Bashivan

Mahta Ramezanian

Maîtrise recherche - UdeM

Co-superviseur⋅e :

Guillaume Dumas

Matthew Riemer

Doctorat - UdeM

Alexis Roger

Doctorat - McGill

Superviseur⋅e principal⋅e :

Blake Richards

Munish Sathish Kumar

Collaborateur·rice de recherche

Vaibhav Singh

Doctorat - Concordia

Superviseur⋅e principal⋅e :

Doctorat - UdeM

Doctorat - UdeM

Co-superviseur⋅e :

Collaborateur·rice alumni - UdeM

Sihui Wei

Baccalauréat - McGill

Andrew Williams

Doctorat - UdeM

Co-superviseur⋅e :

Maîtrise recherche - UdeM

He Zhu

Doctorat - McGill

Publications

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

Philippe C. Dixon

2023-04-29

Journal of Biomechanics (inconnu)

Towards ethical multimodal systems

Alexis Roger

Esma Aimeur

2023-04-25

ArXiv (prépublication)

A Survey on Compositional Generalization in Applications

Baihan Lin

Djallel Bouneffouf

2023-02-01

ArXiv (prépublication)

Broken Neural Scaling Laws

Ethan Caballero

Kshitij Gupta

David Krueger

We present a smoothly broken power law functional form (that we refer to as a Broken Neural Scaling Law (BNSL)) that accurately models &… (voir plus) extrapolates 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

2023-01-31

ICLR.cc/2023/Conference (poster)

openreview.net

AI Agents Learn to Trust

Ardavan S. Nobandegani

Jean-christophe Gagnon-audet

T. Shultz

2022-12-31

Annual Meeting of the Cognitive Science Society (publié)

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… (voir plus)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 (prépublication)

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… (voir plus)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. (publié)

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 (publié)

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… (voir plus) 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 (publié)

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 … (voir plus)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 (publié)

openreview.net

Aligning MAGMA by Few-Shot Learning and Finetuning

Jean-Charles Layoun

Alexis Roger

2022-10-17

ArXiv (prépublication)