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Showing papers from University of Montreal, Mila Show all papers

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NeurIPS 2026Mila / Université de MontréalMila - Quebec Artificial IntelliMcGillU Montreal, MilaMila, U. MontrealDeep RL

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Predictive representation learning combined with high-capacity value approximation drives scalable multitask RL, with the simple model-free MR.Q outperforming world-model methods across continuous control tasks.

Johan Obando Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon and 2 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 4/5
medium 8/10
strict 1/5
80%Must read
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NeurIPS 2026Nanyang TechnologicalMila - Quebec Artificial IntelliU OxfordU Montreal, MilaDeep RL

Beyond One-Size-Fits-All: Diagnosis-Driven Online Reinforcement Learning with Offline Priors

Diagnosis-driven tension management adapts online RL to deployment-specific prior validity shifts, rejecting universal benchmarks for flexible, evidence-guided optimization.

Guozheng Ma, Lu Li, Zilin Wang, Pierre-Luc Bacon and 1 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5
74%Highly rated
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NeurIPS 2026Montreal Institute for Learning U Montreal, MilaGoogle DeepMind, MilaMila, Université de Montréal &amSamsung - SAIT AI Lal MontrealAutoML & architecture search

Layerwise LQR for Geometry-Aware Optimization of Deep Networks

Layerwise LQR frames deep network preconditioners as LQR problems to learn scalable structured inverse preconditioners preserving cross-layer geometry, improving optimization dynamics with modest overhead.

Simon Dufort-Labbé, Pierre-Luc Bacon, Razvan Pascanu, Simon Lacoste-Julien and 1 more

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 2/5
medium 6/10
strict 1/5
72%Highly rated
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NeurIPS 2026Google DeepMind & MilaMeta Superintelligence Labs, McGMila - Quebec Artificial IntelliU Montreal, MilaOffline RL

From Static Policies to Adaptive Priors in Offline Reinforcement Learning

Offline RL should prioritize adaptive policy priors preserving improvement capacity during online updates rather than static conservative deployment.

Tianwei Ni, Vineet Jain, Akash Karthikeyan, Pierre-Luc Bacon

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 0/5