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

Mohammad Javad Darvishi Bayazi

Amin Darabi

PhD - Université de Montréal

PhD - Université de Montréal

Wagner Drew

Master's Research - Concordia University

Mojtaba Faramarzi

PhD - Université de Montréal

Parviz Haggi Mani

Independent visiting researcher - -

Nadhir Hassen

Collaborating Alumni - Université de Montréal

Master's Research

Collaborating Alumni - Université de Montréal

Nizar Islah

PhD - Université de Montréal

PhD - Université de Montréal

PhD - Université de Montréal

Zafir Khalid

Master's Research - Concordia University

Principal supervisor :

Master's Research - Université de Montréal

Neeraj Kumar

Collaborating Alumni - Université de Montréal

Gwen Legate

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

David Lemay

Master's Research - Université de Montréal

Jonathan Lim

Collaborating researcher

Master's Research - Université de Montréal

Collaborating researcher

Andrei Mircea

PhD - Université de Montréal

Collaborating researcher - Université de Montréal

Gabriela Moisescu-Pareja

Collaborating researcher - McGill University

Timothy Nest

PhD - Université de Montréal

Mohammad Pezeshki

Collaborating researcher

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

Vaibhav Singh

PhD - Concordia University

Principal supervisor :

Eugene Belilovsky

Gopeshh Subbaraj

PhD - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

Co-supervisor :

Master's Research - Université de Montréal

He Zhu

PhD - McGill University

Publications

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Andrew Robert Williams

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Roland Riachi

Alexandre Lacoste

Forecasting is a critical task in decision making across various domains. While numerical data provides a foundation, it often lacks crucial… (see more) context necessary for accurate predictions. Human forecasters frequently rely on additional information, such as background knowledge or constraints, which can be efficiently communicated through natural language. However, the ability of existing forecasting models to effectively integrate this textual information remains an open question. To address this, we introduce"Context is Key"(CiK), a time series forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context, requiring models to integrate both modalities. We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters, and propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark. Our experiments highlight the importance of incorporating contextual information, demonstrate surprising performance when using LLM-based forecasting models, and also reveal some of their critical shortcomings. By presenting this benchmark, we aim to advance multimodal forecasting, promoting models that are both accurate and accessible to decision-makers with varied technical expertise. The benchmark can be visualized at https://servicenow.github.io/context-is-key-forecasting/v0/ .

2024-10-24

ArXiv (preprint)

arxiv.org

Context is Key: A Benchmark for Forecasting with Essential Textual Information

Andrew Robert Williams

Arjun Ashok

Étienne Marcotte

Valentina Zantedeschi

Jithendaraa Subramanian

Roland Riachi

Alexandre Lacoste

2024-10-24

ArXiv (preprint)

arxiv.org

$\mu$LO: Compute-Efficient Meta-Generalization of Learned Optimizers

Benjamin Therien

Charles-Etienne Joseph

Boris Knyazev

Edouard Oyallon

Eugene Belilovsky

2024-10-10

NeurIPS.cc/2024/Workshop/OPT (published)

Mohammad Javad Darvishi Bayazi

Introducing Brain Foundation Models

Hena Ghonia

Roland Riachi

Bruno Aristimunha

Arian Khorasani

Md Rifat Arefin

Sylvain Chevallier

Amin Darabi

Guillaume Dumas

Brain function represents one of the most complex systems driving our world. Decoding its signals poses significant challenges, particularly… (see more) due to the limited availability of data and the high cost of recordings. The existence of large hospital datasets and laboratory collections partially mitigates this issue. However, the lack of standardized recording protocols, varying numbers of channels, diverse setups, scenarios, and recording devices further complicate the task. This work addresses these challenges by introducing the Brain Foundation Model (BFM), a suite of open-source models trained on brain signals. These models serve as foundational tools for various types of time-series neuroimaging tasks. This work presents the first model of the BFM series, which is trained on electroencephalogram signal data. Our results demonstrate that BFM-EEG can generate signals more accurately than other models. Upon acceptance, we will release the model weights and pipeline.

2024-10-10

NeurIPS.cc/2024/Workshop/TSALM (published)

Language model scaling laws and zero-sum learning

Andrei Mircea

Ekaterina Lobacheva

Supriyo Chakraborty

Nima Chitsazan

This work aims to understand how, in terms of training dynamics, scaling up language model size yields predictable loss improvements. We fin… (see more)d that these improvements can be tied back to loss deceleration, an abrupt transition in the rate of loss improvement, characterized by piece-wise linear behavior in log-log space. Notably, improvements from increased model size appear to be a result of (1) improving the loss at which this transition occurs; and (2) improving the rate of loss improvement after this transition. As an explanation for the mechanism underlying this transition (and the effect of model size on loss it mediates), we propose the zero-sum learning (ZSL) hypothesis. In ZSL, per-token gradients become systematically opposed, leading to degenerate training dynamics where the model can't improve loss on one token without harming it on another; bottlenecking the overall rate at which loss can improve. We find compelling evidence of ZSL, as well as unexpected results which shed light on other factors contributing to ZSL.

2024-10-10

NeurIPS.cc/2024/Workshop/SciForDL (poster)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

This study investigates the application of human psychometric assessments to large language models (LLMs) to examine their consistency and m… (see more)alleability in exhibiting personality traits. We administered the Big Five Inventory (BFI) and the Eysenck Personality Questionnaire-Revised (EPQ-R) to various LLMs across different model sizes and persona prompts. Our results reveal substantial variability in responses due to question order shuffling, challenging the notion of a stable LLM "personality." Larger models demonstrated more consistent responses, while persona prompts significantly influenced trait scores. Notably, the assistant persona led to more predictable scaling, with larger models exhibiting more socially desirable and less variable traits. In contrast, non-conventional personas displayed unpredictable behaviors, sometimes extending personality trait scores beyond the typical human range. These findings have important implications for understanding LLM behavior under different conditions and reflect on the consequences of scaling.

2024-10-09

NeurIPS.cc/2024/Workshop/Behavioral_ML (oral)

LLMs and Personalities: Inconsistencies Across Scales

Tosato Tommaso

Mahmood Hegazy

2024-10-09

NeurIPS.cc/2024/Workshop/Behavioral_ML (oral)

RedPajama: an Open Dataset for Training Large Language Models

Maurice Weber

Daniel Y Fu

Quentin Gregory Anthony

Yonatan Oren

Shane Adams

Anton Alexandrov

Xiaozhong Lyu

Huu Nguyen

Xiaozhe Yao

Virginia Adams

Ben Athiwaratkun

Rahul Chalamala

Kezhen Chen

Max Ryabinin

Tri Dao

Percy Liang

Christopher Re

Ce Zhang

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)

Using Unity to Help Solve Reinforcement Learning

Connor Brennan

Andrew Robert Williams

Omar G. Younis

Vedant Vyas

Daria Yasafova

Leveraging the depth and flexibility of XLand as well as the rapid prototyping features of the Unity engine, we present the United Unity Uni… (see more)verse — an open-source toolkit designed to accelerate the creation of innovative reinforcement learning environments. This toolkit includes a robust implementation of XLand 2.0 complemented by a user-friendly interface which allows users to modify the details of procedurally generated terrains and task rules with ease. Additionally, we provide a curated selection of terrains and rule sets, accompanied by implementations of reinforcement learning baselines to facilitate quick experimentation with novel architectural designs for adaptive agents. Furthermore, we illustrate how the United Unity Universe serves as a high-level language that enables researchers to develop diverse and endlessly variable 3D environments within a unified framework. This functionality establishes the United Unity Universe (U3) as an essential tool for advancing the field of reinforcement learning, especially in the development of adaptive and generalizable learning systems.

2024-09-26

NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (poster)