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Inspiring the development of artificial intelligence for the benefit of all 

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Located in the heart of Quebec’s AI ecosystem, Mila is a community of more than 1,400 researchers specializing in machine learning and dedicated to scientific excellence and innovation.

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News

05 Aug 2026
Building on Year One: Indigenous AI Gathering Returns More Impactful Than Ever
Group picture during Indigenous AI Gathering event at Mila
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03 Aug 2026
Machine Learning for Real-World Change: Celebrating AI4Good Lab’s 10th Cohort
Group picture of the AI4Good lab cohort
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28 Jul 2026
Summer School in Responsible AI and Human Rights: A Fourth Edition That Shines Internationally
Group picture of the Summer School in Responsible AI and Human Rights in Mexico in front of the CINVESTAV building
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Faculty 

Founded in 1993 by Professor Yoshua Bengio, Mila today brings together over 140 professors affiliated with Université de Montréal, McGill University, Polytechnique Montréal and HEC Montréal. Mila also welcomes professors from Université Laval, Université de Sherbrooke, École de technologie supérieure (ÉTS) and Concordia University. 

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Latest Publications

ECUAS: A family of metrics for principled evaluation of uncertainty-augmented systems
Lautaro Estienne
Erik Ernst
Matías Vera
Pablo Piantanida
Luciana Ferrer
In high-stakes automated decision-making, access to predictive uncertainty is essential for enabling users -- human or downstream systems --… (see more) to accept or reject predictions based on application-specific cost trade-offs. Such uncertainty-augmented (UA) systems -- i.e., systems that output both predictions and uncertainty scores -- are currently being assessed in the literature in a variety of ways, using separate metrics to evaluate the predictions and the uncertainty scores, setting a cost function with a fixed rejection cost or integrating over a coverage-risk curve. We argue that these evaluation approaches are inadequate for assessing overall performance of the UA system for decision making under uncertainty and propose a novel family of metrics, ECUAS
2026-12-30
OpenReview (published)
openreview.net
openreview.net
QueStER: Query Specification for Generative Keyword-Based Retrieval
Arthur SATOUF
Yuxuan Zong
Habiboulaye Amadou Boubacar
Pablo Piantanida
Benjamin Piwowarski
Generative retrieval (GR) differs from the traditional index–then–retrieve pipeline by storing relevance in model parameters and generat… (see more)ing retrieval cues directly from the query, but it can be brittle out of domain and expensive to scale. We introduce QueStER (QUEry SpecificaTion for gEnerative Keyword-Based Retrieval), which bridges GR and query reformulation by learning to generate explicit keyword-based search specifications. Given a user query, a lightweight LLM produces a keyword query that is executed by a standard retriever (BM25), combining the generalization benefits of generative query rewriting with the efficiency and scalability of lexical indexing. We train the rewriting policy with reinforcement learning techniques. Across in- and out-of-domain evaluations, QueStER consistently improves over BM25 and is competitive with neural IR baselines, while maintaining strong efficiency.
2026-12-30
OpenReview (published)
openreview.net
openreview.net
Improved mapping of Arctic fractional land cover and land cover change from multi-resolution optical remote sensing
Xiaoran Zhu
Jonathan A. Wang
Oliver Sonnentag
Isla H. Myers-Smith
Daryl Yang
Kathleen M. Orndahl
Leon Nill
Antoine Caron-Guay
Étienne Laliberté
Mark A. Friedl
Changes in Arctic tundra vegetation, driven by climate change, may be inducing major shifts in ecosystem services and the Arctic carbon budg… (see more)et, and altering high latitude feedbacks to the climate system. Field-based studies have documented warming-induced shrub expansion, and remote sensing has revealed heterogeneous, but primarily positive, trends in peak summer greenness across the Arctic. However, efforts to move beyond remotely sensed measures of spectral greening to quantify the spatial extent and rate of shrub expansion have been constrained by spectral similarities among tundra vegetation types, limited ground truth data, low revisit frequency of satellite observations, and sub-pixel heterogeneity of land cover at medium spatial resolution (30 m). To address these challenges, we developed a methodology that integrates high spatial resolution (2 m) commercial satellite imagery with Harmonized Landsat and Sentinel-2 observations in a machine learning framework, and used it to produce annual maps for 2016 to 2023 of sub-pixel land cover fractions at 30-m spatial resolution across three Arctic tundra ecoregions spanning 3.35 × 105 km2 between the Seward and Tuktoyaktuk Peninsulas. Uncertainty was quantified at each pixel via Monte Carlo resampling. Independent accuracy assessments yielded good accuracies (mean squared errors of 15.98% and 11.89% for low-stature vegetation and erect shrub cover, respectively), that were comparable to or exceeded previous mapping efforts. Further, repeat commercial satellite image pairs enabled the first assessment of mapped fractional cover change in Arctic tundra (R2 of 0.46 and 0.55, change direction accuracies of 77% and 78% for low-stature vegetation and erect shrub cover, respectively). This novel, scalable, multi-sensor approach to fractional land cover mapping produced the first annual maps of land cover fractions in the Arctic tundra, which support more accurate representation of vegetation dynamics and their linkages to climate change and disturbance processes.
2026-09-30
Remote Sensing of Environment (published)
doi.org
SHINIER: An open-source Python package for controlling low-level image properties
Mathias Salvas-Hébert
Nicolas Dupuis-Roy
Catherine Landry
Ian Charest
Frédéric Gosselin
2026-08-31
SoftwareX (published)
doi.org
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Mila Ventures

Mila Ventures

Our venture arm cultivates the next generation of companies backed by Mila's world-class AI research ecosystem. We invest in visionary founders building at the frontier of deep tech, AI, STEM, and beyond.

We believe the future will be shaped by Venture Scientists.

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