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

Co-superviseur⋅e :

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

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

Revisiting Successor Features for Inverse Reinforcement Learning

Sanjiban Choudhury

2024-06-16

ICML.cc/2024/Workshop/MFHAIA (poster)

Gradient Dissent in Language Model Training and Saturation

Andrei Mircea

Ekaterina Lobacheva

We seek to shed light on language model (LM) saturation from the perspective of learning dynamics. To this end, we define a decomposition o… (voir plus)f the cross-entropy gradient, which forms a shared low-dimensional basis for analyzing the training dynamics of models across scales. Intuitively, this decomposition consists of attractive and repulsive components that increase the logit of the correct class and decrease the logits of incorrect classes, respectively. Our analysis in this subspace reveals a phenomenon we term \textit{gradient dissent}, characterized by gradient components becoming systematically opposed such that loss cannot be improved along one component without being degraded along the other. Notably, we find that complete opposition, which we term \textit{total dissent}, reliably occurs in tandem with the saturation of smaller LMs. Based on these results, we hypothesize that gradient dissent can provide a useful foundation for better understanding and mitigating saturation.

2024-06-15

ICML.cc/2024/Workshop/HiLD (poster)

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… (voir plus)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 (prépublication)

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

TMLR (accepté)

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 … (voir plus)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 (publié)

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 (publié)

dblp.uni-trier.de

Improving Adversarial Robustness in Vision-Language Models with Architecture and Prompt Design.

2023-12-31

EMNLP (Findings) (publié)

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… (voir plus)tical analysis of the

2023-12-19

ArXiv (prépublication)

arxiv.org

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… (voir plus)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

Proceedings of The 2nd Conference on Lifelong Learning Agents (publié)

proceedings.mlr.press

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

Kashif Rasul

Arjun Ashok

Andrew Robert Williams

Arian Khorasani

George Adamopoulos

Rishika Bhagwatkar

Marin Biloš

Hena Ghonia

Nadhir Hassen

Anderson Schneider

Sahil Garg

Alexandre Drouin

Nicolas Chapados

Yuriy Nevmyvaka

Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-sho… (voir plus)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

NeurIPS.cc/2023/Workshop/R0-FoMo (poster)

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 … (voir 4 de plus)

Guy Wolf

Michael 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… (voir plus)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 (inconnu)