Chris’s goal is to understand and improve the algorithms that agents can use to learn from data and reason about their experience. This goal is typically framed through the language of statistics, and solved using algorithms for probabilistic inference or optimization. Chris has worked on gradient estimation, variational inference, optimization, and Monte Carlo methods. He is currently interested in learning useful and robust representations, with a particular interest in techniques that learn representations from pretext tasks. Chris is an Open Philanthropy AI Fellow. Chris received a NIPS Best Paper Award in 2014, and was one of the founding members of the AlphaGo project.
Assistant Professor, Department of Computer Science, University of Toronto
Assistant Professor, Department of Statistical Sciences, University of Toronto
Canada CIFAR Artificial Intelligence Chair
Research Interests
- Representation Learning
- Discrete Search
- Optimization & Inference
Highlights
- Open Philanthropy AI Fellow
- NIPS Best Paper Award in 2014
- Founding members of the AlphaGo project
Publications
Conditional neural processes
2018
On empirical comparisons of optimizers for deep learning
2019
Tighter variational bounds are not necessarily better
2017
Doubly reparameterized gradient estimators for Monte Carlo objectives
2018
Gradient Estimation with Stochastic Softmax Tricks
202
Hamiltonian descent for composite objectives
2019
Hamiltonian descent methods
2018
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
2018
Lossy Compression for Lossless Prediction
2021
Optimal Representations for Covariate Shift
2022
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
2021
Selecting actions to be performed by a reinforcement learning agent using tree search
2021
Twisted variational sequential monte carlo
2018
Dual space preconditioning for gradient descent
2021
Learning to cut by looking ahead: Cutting plane selection via imitation learning
2022
Learning Generalized Gumbel-max Causal Mechanisms
2021
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem
2022
Learning Branching Heuristics for Propositional Model Counting
2021
Direct policy gradients: Direct optimization of policies in discrete action spaces
2020
Neural network systems implementing conditional neural processes for efficient learning
2021
The machine learning for combinatorial optimization competition (ml4co): Results and insights
2022
Unbiased gradient estimation with balanced assignments for mixtures of experts
2021
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
2023
Bayesian nonparametrics for offline skill discovery
2022
Stochastic Reweighted Gradient Descent
2022
Learning to Extend Program Graphs to Work-in-Progress Code
2021
Training a policy neural network and a value neural network
2018
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
2021