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

Rifat Arefin

PhD - Université de Montréal

Arjun Ashok

PhD - Université de Montréal

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

Arian Khorasani

Master's Research - Université de Montréal

Neeraj Kumar

Collaborating Alumni - Université de Montréal

Gwen Legate

PhD - Concordia University

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

Mahta Ramezanian

Master's Research - Université de Montréal

Matthew Riemer

PhD - Université de Montréal

Alexis Roger

PhD - McGill University

Vaibhav Singh

PhD - Concordia University

Gopeshh Subbaraj

PhD - Université de Montréal

PhD - Université de Montréal

Collaborating Alumni - Université de Montréal

PhD - Université de Montréal

Daria Yasafova

Master's Research - Université de Montréal

He Zhu

PhD - McGill University

Publications

Handling Delay in Real-Time Reinforcement Learning

Ivan Anokhin

Rishav

Matthew D Riemer

Stephen Chung

Samira Ebrahimi Kahou

Real-time reinforcement learning (RL) introduces several challenges. First, policies are constrained to a fixed number of actions per second… (see more) due to hardware limitations. Second, the environment may change while the network is still computing an action, leading to observational delay. The first issue can partly be addressed with pipelining, leading to higher throughput and potentially better policies. However, the second issue remains: if each neuron operates in parallel with an execution time of

2025-01-22

ICLR.cc/2025/Conference (poster)

Handling Delay in Real-Time Reinforcement Learning

Ivan Anokhin

Rishav

Matthew D Riemer

Stephen Chung

Samira Ebrahimi Kahou

2025-01-22

ICLR.cc/2025/Conference (poster)

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

Sanjiban Choudhury

2025-01-22

ICLR.cc/2025/Conference (poster)

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning

Md Rifat Arefin

Gopeshh Subbaraj

Nicolas Gontier

Yann LeCun

Ravid Shwartz-Ziv

Chris Pal

2025-01-22

ICLR.cc/2025/Conference (poster)

Artificial Neural Networks for Magnetoencephalography: A review of an emerging field

Arthur Dehgan

Hamza Abdelhedi

Vanessa Hadid

Karim Jerbi

Magnetoencephalography (MEG) is a cutting-edge neuroimaging technique that measures the intricate brain dynamics underlying cognitive proces… (see more)ses with an unparalleled combination of high temporal and spatial precision. MEG data analytics has always relied on advanced signal processing and mathematical and statistical tools for various tasks ranging from data cleaning to probing the signals' rich dynamics and estimating the neural sources underlying the surface-level recordings. Like in most domains, the surge in Artificial Intelligence (AI) has led to the increased use of Machine Learning (ML) methods for MEG data classification. More recently, an emerging trend in this field is using Artificial Neural Networks (ANNs) to address many MEG-related tasks. This review provides a comprehensive overview of how ANNs are being used with MEG data from three vantage points: First, we review work that employs ANNs for MEG signal classification, i.e., for brain decoding. Second, we report on work that has used ANNs as putative models of information processing in the human brain. Finally, we examine studies that use ANNs as techniques to tackle methodological questions in MEG, including artifact correction and source estimation. Furthermore, we assess the current strengths and limitations of using ANNs with MEG and discuss future challenges and opportunities in this field. Finally, by establishing a detailed portrait of the field and providing practical recommendations for the future, this review seeks to provide a helpful reference for both seasoned MEG researchers and newcomers to the field who are interested in using ANNs to enhance the exploration of the complex dynamics of the human brain with MEG.

2025-01-20

ArXiv (preprint)

CHIRP: A Fine-Grained Benchmark for Open-Ended Response Evaluation in Vision-Language Models

Alexis Roger

Prateek Humane

Daniel Z Kaplan

Kshitij Gupta

Qirui Sun

George Adamopoulos

Jonathan Siu Chi Lim

Quentin Gregory Anthony

Edwin Fennell

The proliferation of Vision-Language Models (VLMs) in the past several years calls for rigorous and comprehensive evaluation methods and ben… (see more)chmarks. This work analyzes existing VLM evaluation techniques, including automated metrics, AI-based assessments, and human evaluations across diverse tasks. We first introduce Robin - a novel suite of VLMs that we built by combining Large Language Models (LLMs) and Vision Encoders (VEs) at multiple scales, and use Robin to identify shortcomings of current evaluation approaches across scales. Next, to overcome the identified limitations, we introduce CHIRP - a new long form response benchmark we developed for more robust and complete VLM evaluation. We provide open access to the Robin training code, model suite, and CHIRP benchmark to promote reproducibility and advance VLM research.

2025-01-16

ArXiv (preprint)

Robin: a Suite of Multi-Scale Vision-Language Models and the CHIRP Evaluation Benchmark

Alexis Roger

Prateek Humane

Daniel Z Kaplan

Kshitij Gupta

Qirui Sun

George Adamopoulos

Jonathan Siu Chi Lim

Quentin Gregory Anthony

Edwin Fennell

2025-01-16

ArXiv (preprint)

Training Dynamics Underlying Language Model Scaling Laws: Loss Deceleration and Zero-Sum Learning

Andrei Mircea

Supriyo Chakraborty

Nima Chitsazan

Ekaterina Lobacheva

This work aims to understand how scaling improves language models, specifically in terms of training dynamics. We find that language models … (see more)undergo loss deceleration early in training; an abrupt slowdown in the rate of loss improvement, resulting in piecewise linear behaviour of the loss curve in log-log space. Scaling up the model mitigates this transition by (1) decreasing the loss at which deceleration occurs, and (2) improving the log-log rate of loss improvement after deceleration. We attribute loss deceleration to a type of degenerate training dynamics we term zero-sum learning (ZSL). In ZSL, per-example gradients become systematically opposed, leading to destructive interference in per-example changes in loss. As a result, improving loss on one subset of examples degrades it on another, bottlenecking overall progress. Loss deceleration and ZSL provide new insights into the training dynamics underlying language model scaling laws, and could potentially be targeted directly to improve language models independent of scale. We make our code and artefacts available at: https://github.com/mirandrom/zsl

2025-01-01

ACL (1) (published)

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Matthew Riemer

Gopeshh Raaj Subbaraj

Glen Berseth

Realtime environments change even as agents perform action inference and learning, thus requiring high interaction frequencies to effectivel… (see more)y minimize regret. However, recent advances in machine learning involve larger neural networks with longer inference times, raising questions about their applicability in realtime systems where reaction time is crucial. We present an analysis of lower bounds on regret in realtime reinforcement learning (RL) environments to show that minimizing long-term regret is generally impossible within the typical sequential interaction and learning paradigm, but often becomes possible when sufficient asynchronous compute is available. We propose novel algorithms for staggering asynchronous inference processes to ensure that actions are taken at consistent time intervals, and demonstrate that use of models with high action inference times is only constrained by the environment's effective stochasticity over the inference horizon, and not by action frequency. Our analysis shows that the number of inference processes needed scales linearly with increasing inference times while enabling use of models that are multiple orders of magnitude larger than existing approaches when learning from a realtime simulation of Game Boy games such as Pok\'emon and Tetris.

2024-12-18

ArXiv (preprint)

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Matthew Riemer

Gopeshh Raaj Subbaraj

Glen Berseth

2024-12-18

ArXiv (preprint)

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

Sanjiban Choudhury

In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Tradit… (see more)ionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner optimizes the reward through repeated RL procedures. This game-solving approach is both computationally expensive and difficult to stabilize. In this work, we propose a novel approach to IRL by direct policy optimization: exploiting a linear factorization of the return as the inner product of successor features and a reward vector, we design an IRL algorithm by policy gradient descent on the gap between the learner and expert features. Our non-adversarial method does not require learning a reward function and can be solved seamlessly with existing actor-critic RL algorithms. Remarkably, our approach works in state-only settings without expert action labels, a setting which behavior cloning (BC) cannot solve. Empirical results demonstrate that our method learns from as few as a single expert demonstration and achieves improved performance on various control tasks.

2024-11-11

ArXiv (preprint)

GitChameleon: Unmasking the Version-Switching Capabilities of Code Generation Models

Nizar Islah

Justine Gehring

Diganta Misra

Eilif Benjamin Muller

Terry Yue Zhuo

Massimo Caccia

2024-11-01

arXiv (published)