Portrait of David Rolnick

David Rolnick

Core Academic Member
Canada CIFAR AI Chair
Assistant Professor, McGill University, School of Computer Science
Adjunct Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Machine Learning Theory

Biography

David Rolnick is an assistant professor at McGill University’s School of Computer Science, a core academic member of Mila – Quebec Artificial Intelligence Institute and holds a Canada CIFAR AI Chair. Rolnick’s work focuses on applications of machine learning to help address climate change. He is the co-founder and chair of Climate Change AI, and scientific co-director of Sustainability in the Digital Age. After completing his PhD in applied mathematics at the Massachusetts Institute of Technology (MIT), he was a NSF Mathematical Sciences Postdoctoral Research Fellow, an NSF Graduate Research Fellow and a Fulbright Scholar. He was named to MIT Technology Review’s “35 Innovators Under 35” in 2021.

Current Students

Master's Research - McGill University
Collaborating Alumni - Université de Montréal
Collaborating researcher - The University of Dresden, Helmholtz Centre for Environmental Research Leipzig
Collaborating researcher
Collaborating researcher - National Observatory of Athens
Collaborating researcher
Collaborating researcher - McGill University
Collaborating researcher - KU Leuven
Master's Research - McGill University
Research Intern
Collaborating Alumni
Collaborating researcher - Université Paris-Saclay
Co-supervisor :
Collaborating researcher
Collaborating researcher
Postdoctorate - Université de Montréal
Principal supervisor :
Collaborating researcher - Université de Montréal
Research Intern - Johannes Kepler University
Master's Research - McGill University
PhD - McGill University
PhD - McGill University
Collaborating researcher - University of Waterloo
Collaborating researcher
Collaborating researcher
Research Intern - Université de Montréal
Postdoctorate - McGill University
Co-supervisor :
PhD - University of Waterloo
Co-supervisor :
PhD - Université de Montréal
Collaborating researcher
Master's Research - Osnabrueck university
Collaborating researcher
Co-supervisor :
PhD - McGill University
Collaborating researcher
Co-supervisor :
Collaborating researcher - Karlsruhe Institute of Technology
PhD - McGill University
Postdoctorate - Université de Montréal
Principal supervisor :
Collaborating researcher
Collaborating researcher - RWTH Aachen University (Rheinisch-Westfälische Technische Hochschule Aachen)
Co-supervisor :
PhD - McGill University
Postdoctorate - McGill University
Collaborating researcher
Collaborating researcher

Publications

Digitalization and the Anthropocene
Felix Creutzig
Daron Acemoglu
Xuemei Bai
Paul N. Edwards
Marie Josefine Hintz
Lynn H. Kaack
Siir Kilkis
Stefanie Kunkel
Amy Luers
Nikola Milojevic-Dupont
Dave Rejeski
Jürgen Renn
Christoph Rosol
Daniela Russ
Thomas Turnbull
Elena Verdolini
Felix Wagner
Charlie Wilson
Aicha Zekar … (see 1 more)
Marius Zumwald
Great claims have been made about the benefits of dematerialization in a digital service economy. However, digitalization has historically i… (see more)ncreased environmental impacts at local and planetary scales, affecting labor markets, resource use, governance, and power relationships. Here we study the past, present, and future of digitalization through the lens of three interdependent elements of the Anthropocene: ( a) planetary boundaries and stability, ( b) equity within and between countries, and ( c) human agency and governance, mediated via ( i) increasing resource efficiency, ( ii) accelerating consumption and scale effects, ( iii) expanding political and economic control, and ( iv) deteriorating social cohesion. While direct environmental impacts matter, the indirect and systemic effects of digitalization are more profoundly reshaping the relationship between humans, technosphere and planet. We develop three scenarios: planetary instability, green but inhumane, and deliberate for the good. We conclude with identifying leverage points that shift human–digital–Earth interactions toward sustainability. Expected final online publication date for the Annual Review of Environment and Resources, Volume 47 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
A portrait of the different configurations between digitally-enabled innovations and climate governance
Pierre J. C. Chuard
Jennifer Garard
Karsten A. Schulz
Nilushi Kumarasinghe
Damon Matthews
Neural Networks as Paths through the Space of Representations
Richard D Lange
Devin Kwok
Jordan Kyle Matelsky
Xinyue Wang
Konrad Paul Kording
Clustering units in neural networks: upstream vs downstream information
Richard D Lange
Konrad Paul Kording
It has been hypothesized that some form of"modular"structure in artificial neural networks should be useful for learning, compositionality, … (see more)and generalization. However, defining and quantifying modularity remains an open problem. We cast the problem of detecting functional modules into the problem of detecting clusters of similar-functioning units. This begs the question of what makes two units functionally similar. For this, we consider two broad families of methods: those that define similarity based on how units respond to structured variations in inputs ("upstream"), and those based on how variations in hidden unit activations affect outputs ("downstream"). We conduct an empirical study quantifying modularity of hidden layer representations of simple feedforward, fully connected networks, across a range of hyperparameters. For each model, we quantify pairwise associations between hidden units in each layer using a variety of both upstream and downstream measures, then cluster them by maximizing their"modularity score"using established tools from network science. We find two surprising results: first, dropout dramatically increased modularity, while other forms of weight regularization had more modest effects. Second, although we observe that there is usually good agreement about clusters within both upstream methods and downstream methods, there is little agreement about the cluster assignments across these two families of methods. This has important implications for representation-learning, as it suggests that finding modular representations that reflect structure in inputs (e.g. disentanglement) may be a distinct goal from learning modular representations that reflect structure in outputs (e.g. compositionality).
On Neural Architecture Inductive Biases for Relational Tasks
Current deep learning approaches have shown good in-distribution generalization performance, but struggle with out-of-distribution generaliz… (see more)ation. This is especially true in the case of tasks involving abstract relations like recognizing rules in sequences, as we find in many intelligence tests. Recent work has explored how forcing relational representations to remain distinct from sensory representations, as it seems to be the case in the brain, can help artificial systems. Building on this work, we further explore and formalize the advantages afforded by 'partitioned' representations of relations and sensory details, and how this inductive bias can help recompose learned relational structure in newly encountered settings. We introduce a simple architecture based on similarity scores which we name Compositional Relational Network (CoRelNet). Using this model, we investigate a series of inductive biases that ensure abstract relations are learned and represented distinctly from sensory data, and explore their effects on out-of-distribution generalization for a series of relational psychophysics tasks. We find that simple architectural choices can outperform existing models in out-of-distribution generalization. Together, these results show that partitioning relational representations from other information streams may be a simple way to augment existing network architectures' robustness when performing out-of-distribution relational computations.
Aligning artificial intelligence with climate change mitigation
Lynn H. Kaack
Priya L. Donti
Emma Strubell
George Yoshito Kamiya
Felix Creutzig
Inductive Biases for Relational Tasks
Current deep learning approaches have shown good in-distribution performance but struggle in out-of-distribution settings. This is especiall… (see more)y true in the case of tasks involving abstract relations like recognizing rules in sequences, as required in many intelligence tests. In contrast, our brains are remarkably flexible at such tasks, an attribute that is likely linked to anatomical constraints on computations. Inspired by this, recent work has explored how enforcing that relational representations remain distinct from sensory representations can help artificial systems. Building on this work, we further explore and formalize the advantages afforded by ``partitioned'' representations of relations and sensory details. We investigate inductive biases that ensure abstract relations are learned and represented distinctly from sensory data across several neural network architectures and show that they outperform existing architectures on out-of-distribution generalization for various relational tasks. These results show that partitioning relational representations from other information streams may be a simple way to augment existing network architectures' robustness when performing relational computations.
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
Evan David Sherwin
S. Karthik Mukkavilli
Konrad Paul Kording
Carla P. Gomes
Andrew Y. Ng
Demis Hassabis
John C. Platt
Felix Creutzig … (see 2 more)
Jennifer T Chayes
Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here… (see more) 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.
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 … (see more)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.
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 … (see more)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.