David Rolnick

Biographie

David Rolnick est professeur adjoint et titulaire d’une chaire en IA Canada-CIFAR à l'École d'informatique de l'Université McGill et membre académique principal de Mila – Institut québécois d’intelligence artificielle. Ses travaux portent sur les applications de l'apprentissage automatique dans la lutte contre le changement climatique. Il est cofondateur et président de Climate Change AI et codirecteur scientifique de Sustainability in the Digital Age. David Rolnick a obtenu un doctorat en mathématiques appliquées du Massachusetts Institute of Technology (MIT). Il a été chercheur postdoctoral en sciences mathématiques à la National Science Foundation (NSF), chercheur diplômé à la NSF et boursier Fulbright. Il a figuré sur la liste des « 35 innovateurs de moins de 35 ans » de la MIT Technology Review en 2021.

Étudiants actuels

Benjamin Akera Binen

Collaborateur·rice alumni - McGill

Collaborateur·rice alumni - UdeM

Collaborateur·rice de recherche - Cambridge University

Co-superviseur⋅e :

Postdoctorat - McGill

Michael Bunsen

Collaborateur·rice de recherche - McGill

Juan Sebastián Cañas

Collaborateur·rice de recherche

Collaborateur·rice de recherche - N/A

Co-superviseur⋅e :

Yoshua Bengio

Yuyan Chen

Maîtrise recherche - McGill

Eya Cherif

Collaborateur·rice de recherche - Leipzig University

Amna El-Mustafa

Collaborateur·rice de recherche

Mohamed Elabbas

Collaborateur·rice de recherche

Paula Harder

Visiteur de recherche indépendant

Collaborateur·rice de recherche - UdeM

Christina Humer

Collaborateur·rice de recherche - Johannes Kepler University

Christina Isaicu Isaicu

Collaborateur·rice de recherche - University of Amsterdam

Gaurav Iyer

Maîtrise recherche - McGill

Doctorat - McGill

Devin Kwok

Doctorat - McGill

Collaborateur·rice de recherche

Visiteur de recherche indépendant - Université de Montréal

David Mickisch

Collaborateur·rice de recherche

Felix Andreas Nahrstedt

Stagiaire de recherche - UdeM

Juan Nathaniel Nathaniel

Collaborateur·rice de recherche - Columbia university

Postdoctorat - McGill

Co-superviseur⋅e :

Lena Podina

Doctorat - University of Waterloo

Co-superviseur⋅e :

Collaborateur·rice alumni - UdeM

Marlena Reil

Maîtrise recherche - McGill

Carla Roesch

Collaborateur·rice de recherche - Columbia university

luca.schmidt@uni-tuebingen.de

Luca Marie Schmidt

Collaborateur·rice de recherche - University of Tübingen

Collaborateur·rice de recherche

seth.pratinav@gmail.com

Collaborateur·rice de recherche - Karlsruhe Institute of Technology

Doctorat - McGill

Postdoctorat - UdeM

Superviseur⋅e principal⋅e :

Collaborateur·rice de recherche

anna.viklund@mila.quebec

Doctorat - McGill

Collaborateur·rice alumni - McGill

Publications

Tackling Climate Change with Machine Learning

Priya L. Donti

Lynn H. Kaack

Kelly Kochanski

Alexandre Lacoste

Kris Sankaran

Andrew Slavin Ross

Nikola Milojevic-Dupont

Natasha Jaques

Anna Waldman-Brown

Alexandra Luccioni

Tegan Maharaj

Evan David Sherwin

S. Karthik Mukkavilli

Konrad Paul Kording

Carla P. Gomes

Andrew Y. Ng

Demis Hassabis

John C. Platt

Felix Creutzig … (voir 2 de plus)

Jennifer T Chayes

Yoshua Bengio

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here… (voir plus) we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

2022-02-07

ACM Computing Surveys (publié)

doi.org

arxiv.org

TIML: Task-Informed Meta-Learning for Agriculture

Gabriel Tseng

Hannah Kerner

Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach … (voir plus)is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data are rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of data-points or the class of task being learned. We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of task-specific metadata. We apply TIML to crop type classification and yield estimation, and find that TIML significantly improves performance compared to a range of benchmarks in both contexts, across a diversity of model architectures. While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling.

2022-02-04

ArXiv (prépublication)

arxiv.org

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning

Setareh Cohan

Nam Hee Gordon Kim

Michiel van de Panne

Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in … (voir plus)many other respects. ReLU-based policies result in a partitioning of the input space into piecewise linear regions. We seek to understand how observed region counts and their densities evolve during deep reinforcement learning using empirical results that span a range of continuous control tasks and policy network dimensions. Intuitively, we may expect that during training, the region density increases in the areas that are frequently visited by the policy, thereby affording fine-grained control. We use recent theoretical and empirical results for the linear regions induced by neural networks in supervised learning settings for grounding and comparison of our results. Empirically, we find that the region density increases only moderately throughout training, as measured along fixed trajectories coming from the final policy. However, the trajectories themselves also increase in length during training, and thus the region densities decrease as seen from the perspective of the current trajectory. Our findings suggest that the complexity of deep reinforcement learning policies does not principally emerge from a significant growth in the complexity of functions observed on-and-around trajectories of the policy.

openreview.net

Hidden Hypergraphs, Error-Correcting Codes, and Critical Learning in Hopfield Networks

Christopher Hillar

Tenzin Chan

Rachel Taubman