Chris Pal

hugo.baudchon@mila.quebec

Biography

Christopher Pal is a Canada CIFAR AI Chair, full professor at Polytechnique Montréal and adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Distinguished Scientist at ServiceNow Research.

Pal has been involved in AI and machine learning research for over twenty-five years and has published extensively on large-scale language modelling methods and generative modelling techniques. He has a PhD in computer science from the University of Waterloo.

Current Students

Paul Barde

Collaborating researcher - McGill University

Principal supervisor :

Derek Nowrouzezahrai

paul.b.barde@gmail.com

Master's Research - Université de Montréal

Can (Sam) Chen

Collaborating Alumni - McGill University

Principal supervisor :

PhD - Université de Montréal

Principal supervisor :

Glen Berseth

lea.demeule@mila.quebec

olivier-lessard@videotron.ca

Amine El-hattami

PhD - Polytechnique Montréal

elhattaa@mila.quebec

Chris Emezue

PhD - Université de Montréal

Co-supervisor :

Derek Nowrouzezahrai

chris.emezue@mila.quebec

PhD - Polytechnique Montréal

girgisro@mila.quebec

Simon Guiroy

PhD - Université de Montréal

Co-supervisor :

Yousef Kotp

Master's Research - Concordia University

Co-supervisor :

Mahdi Hosseini

yousef.kotp@mila.quebec

PhD - Polytechnique Montréal

Co-supervisor :

Jana Pavlasek

Michelle Lin

Master's Research - Université de Montréal

michelle.lin@mila.quebec

aristides.milios@mila.quebec

Olga Luo

PhD - Université de Montréal

xugeya@mila.quebec

Aristides Milios

PhD - Université de Montréal

juan.rodriguez@mila.quebec

Joel Moniz

PhD - Polytechnique Montréal

joel.moniz@mila.quebec

Juan Rodriguez

PhD - École de technologie suprérieure

Luke Rowe

PhD - Université de Montréal

Principal supervisor :

Liam Paull

luke.rowe@mila.quebec

aditya.sharma@mila.quebec

Gaurav Sahu

Postdoctorate - HEC Montréal

Principal supervisor :

Laurent Charlin

gaurav.sahu@mila.quebec

PhD - Polytechnique Montréal

Principal supervisor :

Amal Zouaq

Collaborating researcher - McGill University

Principal supervisor :

Derek Nowrouzezahrai

mattie.tesfaldet@mila.quebec

Postdoctorate - Polytechnique Montréal

Co-supervisor :

PhD - Université de Montréal

matthew.wiens@mila.quebec

joanna.wolski@mila.quebec

Joanna Wolski

Collaborating researcher

Direct Behavior Specification via Constrained Reinforcement Learning

Blog Posts

August 31, 2022

Julien Roy

Roger Girgis

Joshua Romoff

Pierre-Luc Bacon

Chris Pal

Read the article

Publications

Lacuna: A Research Map for Machine Learning

Miles Q. Li

Alejandro H. Artiles

Yacine Mkhinini

Hugo Larochelle

Nasim Rahaman

Lacuna is a research map for machine learning that uses LLMs to turn papers and scholarly metadata into markdown summaries, concept elements… (see more), research directions, and research proposals. Each item keeps links to the primary source records and papers that support it. We release the map with web, markdown, and MCP interfaces. Across LitSearch, Multi-XScience-CS/ML, and ScholarQA-CS-ML, Lacuna outperforms OpenScholar with the strongest gains on LitSearch retrieval (Recall@10 0.538 vs. 0.424 for OpenScholar v3). We also evaluate Lacuna Deep Research, a multi-stage report agent over the map, on 25 ReportBench-ML survey tasks: Lacuna Deep Research reaches 0.052 citation F1, 0.339 citation precision, 99 expert-reference hits, and 7.82/10 RACE report quality, while GPT-Researcher reaches 0.039 F1, 0.290 precision, 72 hits, and 5.24/10 RACE.

2026-06-23

arXiv (preprint)

Training Diffusion Language Models for Black-Box Optimization

Jiayao Gu

Xue Liu

We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem … (see more)common in robotics, DNA, and materials science with limited labeled samples. While recent work applies autoregressive LLMs to BBO by formatting tasks as natural-language prompts, their left-to-right design generation struggles to capture the strong bidirectional dependencies inherent in design problems. To address this, we propose adapting diffusion LLMs to offline BBO to leverage their bidirectional modeling capabilities. However, a domain gap exists between the natural text pre-training of diffusion LLMs and the heterogeneous signals in BBO (prompts, designs, and labels). To bridge this gap, we construct a unified prompt–response corpus and introduce delimiter tokens to explicitly mark field boundaries for *domain adaptation*. We further propose a two-stage *post-training* framework to align the diffusion LLM generation with high-label designs. The first stage performs supervised fine-tuning on the unified dataset via masked-response prediction, and the second stage adopts reinforcement learning with rewards defined by label improvements. Our method achieves state-of-the-art results on Design-Bench small-data settings. Code for our work is available here: https://anonymous.4open.science/r/Anonymous-dllm4bbo-D78A/README.md.

2025-12-31

International Conference on Machine Learning (Accept (spotlight))

The NaijaVoices Dataset: Cultivating Large-Scale, High-Quality, Culturally-Rich Speech Data for African Languages

Chris Emezue

The NaijaVoices Community

Busayo Awobade

Abraham Toluwase Owodunni

Handel Emezue

Gloria Monica Tobechukwu Emezue

Nefertiti Nneoma Emezue

Sewade Ogun

Bunmi Akinremi

David Ifeoluwa Adelani

2025-05-25

arXiv (preprint)

CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling

Matthew Fortier

Mats L. Richter

Oliver Sonnentag

Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO…

2025-01-21

ICLR.cc/2025/Conference (poster)

A General-Purpose Neural Architecture for Geospatial Systems

Nasim Rahaman

Frederik Träuble

Francesco Locatello

Alexandre Lacoste

Yoshua Bengio

Li Erran Li

Bernhard Schölkopf

2022-11-01

OpenReview (unknown)

Neural Attentive Circuits

Nasim Rahaman

Francesco Locatello

Yoshua Bengio

Bernhard Schölkopf

Li Erran Li

Nicolas Ballas

Recent work has seen the development of general purpose neural architectures that can be trained to perform tasks across diverse data modali… (see more)ties. General purpose models typically make few assumptions about the underlying data-structure and are known to perform well in the large-data regime. At the same time, there has been growing interest in modular neural architectures that represent the data using sparsely interacting modules. These models can be more robust out-of-distribution, computationally efficient, and capable of sample-efficient adaptation to new data. However, they tend to make domain-specific assumptions about the data, and present challenges in how module behavior (i.e., parameterization) and connectivity (i.e., their layout) can be jointly learned. In this work, we introduce a general purpose, yet modular neural architecture called Neural Attentive Circuits (NACs) that jointly learns the parameterization and a sparse connectivity of neural modules without using domain knowledge. NACs are best understood as the combination of two systems that are jointly trained end-to-end: one that determines the module configuration and the other that executes it on an input. We demonstrate qualitatively that NACs learn diverse and meaningful module configurations on the NLVR2 dataset without additional supervision. Quantitatively, we show that by incorporating modularity in this way, NACs improve upon a strong non-modular baseline in terms of low-shot adaptation on CIFAR and CUBs dataset by about 10%, and OOD robustness on Tiny ImageNet-R by about 2.5%. Further, we find that NACs can achieve an 8x speedup at inference time while losing less than 3% performance. Finally, we find NACs to yield competitive results on diverse data modalities spanning point-cloud classification, symbolic processing and text-classification from ASCII bytes, thereby confirming its general purpose nature.

2021-12-31

Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (published)

Learned Image Compression for Machine Perception

Felipe Codevilla

Jean Gabriel Simard

Ross Goroshin

Recent work has shown that learned image compression strategies can outperform standard hand-crafted compression algorithms that have been d… (see more)eveloped over decades of intensive research on the rate-distortion trade-off. With growing applications of computer vision, high quality image reconstruction from a compressible representation is often a secondary objective. Compression that ensures high accuracy on computer vision tasks such as image segmentation, classification, and detection therefore has the potential for significant impact across a wide variety of settings. In this work, we develop a framework that produces a compression format suitable for both human perception and machine perception. We show that representations can be learned that simultaneously optimize for compression and performance on core vision tasks. Our approach allows models to be trained directly from compressed representations, and this approach yields increased performance on new tasks and in low-shot learning settings. We present results that improve upon segmentation and detection performance compared to standard high quality JPGs, but with representations that are four to ten times smaller in terms of bits per pixel. Further, unlike naive compression methods, at a level ten times smaller than standard JEPGs, segmentation and detection models trained from our format suffer only minor degradation in performance.

2021-11-02

ArXiv (preprint)

Predicting Infectiousness for Proactive Contact Tracing

Prateek Gupta

Nasim Rahaman

Pierre-Luc St-Charles

Hannah Alsdurf

Olexa Bilanuik

David Buckeridge

Gaétan Marceau Caron

Pierre-Luc Carrier

Joumana Ghosn

Satya Ortiz-Gagné

Irina Rish

Bernhard Schölkopf … (see 3 more)

Abhinav Sharma

Jian Tang

Andrew Williams

The COVID-19 pandemic has spread rapidly worldwide, overwhelming manual contact tracing in many countries and resulting in widespread lockdo… (see more)wns for emergency containment. Large-scale digital contact tracing (DCT) has emerged as a potential solution to resume economic and social activity while minimizing spread of the virus. Various DCT methods have been proposed, each making trade-offs between privacy, mobility restrictions, and public health. The most common approach, binary contact tracing (BCT), models infection as a binary event, informed only by an individual's test results, with corresponding binary recommendations that either all or none of the individual's contacts quarantine. BCT ignores the inherent uncertainty in contacts and the infection process, which could be used to tailor messaging to high-risk individuals, and prompt proactive testing or earlier warnings. It also does not make use of observations such as symptoms or pre-existing medical conditions, which could be used to make more accurate infectiousness predictions. In this paper, we use a recently-proposed COVID-19 epidemiological simulator to develop and test methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness (risk of infecting others) based on their contact history and other information, while respecting strong privacy constraints. Predictions are used to provide personalized recommendations to the individual via an app, as well as to send anonymized messages to the individual's contacts, who use this information to better predict their own infectiousness, an approach we call proactive contact tracing (PCT). We find a deep-learning based PCT method which improves over BCT for equivalent average mobility, suggesting PCT could help in safe re-opening and second-wave prevention.

2021-05-02

International Conference on Learning Representations (Spotlight)

Accounting for Variance in Machine Learning Benchmarks

Mirko Bronzi

Naz Sepah

Edward Raff

Kanika Madan

Vikram Voleti

Samira Ebrahimi Kahou

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (see more)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.

2020-12-31

MLSys (published)

Navigation Agents for the Visually Impaired: A Sidewalk Simulator and Experiments

Simon Chamorro

Roger Girgis

Margaux Luck

Samira E. Kahou

Joseph P. Cohen

Millions of blind and visually-impaired (BVI) people navigate urban environments every day, using smartphones for high-level path-planning a… (see more)nd white canes or guide dogs for local information. However, many BVI people still struggle to travel to new places. In our endeavor to create a navigation assistant for the BVI, we found that existing Reinforcement Learning (RL) environments were unsuitable for the task. This work introduces SEVN, a sidewalk simulation environment and a neural network-based approach to creating a navigation agent. SEVN contains panoramic images with labels for house numbers, doors, and street name signs, and formulations for several navigation tasks. We study the performance of an RL algorithm (PPO) in this setting. Our policy model fuses multi-modal observations in the form of variable resolution images, visible text, and simulated GPS data to navigate to a goal door. We hope that this dataset, simulator, and experimental results will provide a foundation for further research into the creation of agents that can assist members of the BVI community with outdoor navigation.

2020-05-11

Proceedings of the Conference on Robot Learning (published)

proceedings.mlr.press

Medical Imaging with Deep Learning: MIDL 2020 -- Short Paper Track

Tal Arbel

Ismail Ben Ayed

Marleen de Bruijne

Maxime Descoteaux

Hervé Lombaert

This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (M… (see more)IDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Research (PMLR).

2019-12-31

arXiv (preprint)

Towards Standardization of Data Licenses: The Montreal Data License

Misha Benjamin

Paul Gagnon

Negar Rostamzadeh

Yoshua Bengio

Alex Shee

This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The paper's goal is … (see more)to build towards a common framework for data licensing akin to the licensing of open source software. Increased transparency and resolving conceptual ambiguities in existing licensing language are two noted benefits of the approach proposed in the paper. In parallel, such benefits may help foster fairer and more efficient markets for data through bringing about clearer tools and concepts that better define how data can be used in the fields of AI and ML. The paper's approach is summarized in a new family of data license language - \textit{the Montreal Data License (MDL)}. Alongside this new license, the authors and their collaborators have developed a web-based tool to generate license language espousing the taxonomies articulated in this paper.

2019-03-20

ArXiv (preprint)