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Let's Collaborate
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Assistant Professor, Department of Computer Science, University of Toronto
Assistant Professor, Department of Statistical Sciences, University of Toronto
Canada CIFAR Artificial Intelligence Chair
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.
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
Marta Garnelo and Dan Rosenbaum and Chris J Maddison and Tiago Ramalho and David Saxton and Murray Shanahan and Yee Whye Teh and Danilo J Rezende and SM Eslami
2018
On empirical comparisons of optimizers for deep learning
Dami Choi and Christopher J Shallue and Zachary Nado and Jaehoon Lee and Chris J Maddison and George E Dahl
2019
Tighter variational bounds are not necessarily better
Tom Rainforth and Adam R Kosiorek and Tuan Anh Le and Chris J Maddison and Maximilian Igl and Frank Wood and Yee Whye Teh
2017
Doubly reparameterized gradient estimators for Monte Carlo objectives
George Tucker and Dieterich Lawson and Shixiang Gu and Chris J Maddison
2018
Gradient Estimation with Stochastic Softmax Tricks
Max B Paulus and Dami Choi and Daniel Tarlow and Andreas Krause and Chris J Maddison
202
Hamiltonian descent for composite objectives
Brendan O'Donoghue and Chris J Maddison
2019
Hamiltonian descent methods
Chris J Maddison and Daniel Paulin and Yee Whye Teh and Brendan O'Donoghue and Arnaud Doucet
2018
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl and Kevin Swersky and Milad Hashemi and David Duvenaud and Chris J Maddison
2018
Lossy Compression for Lossless Prediction
Yann Dubois and Benjamin Bloem-Reddy and Karen Ullrich and Chris J Maddison
2021
Optimal Representations for Covariate Shift
Yangjun Ruan and Yann Dubois and Chris J Maddison
2022
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding
Yangjun Ruan and Karen Ullrich and Daniel Severo and James Townsend and Ashish Khisti and Arnaud Doucet and Alireza Makhzani and Chris J Maddison
2021
Selecting actions to be performed by a reinforcement learning agent using tree search
Thore Kurt Hartwig Graepel and Shih-Chieh Huang and David Silver and Arthur Clement Guez and Laurent Sifre and Ilya Sutskever and Christopher Maddison
2021
Twisted variational sequential monte carlo
Dieterich Lawson and George Tucker and Christian A Naesseth and Chris Maddison and Ryan P Adams and Yee Whye Teh
2018
Dual space preconditioning for gradient descent
Chris J Maddison and Daniel Paulin and Yee Whye Teh and Arnaud Doucet
2021
Learning to cut by looking ahead: Cutting plane selection via imitation learning
Max B Paulus and Giulia Zarpellon and Andreas Krause and Laurent Charlin and Chris Maddison
2022
Learning Generalized Gumbel-max Causal Mechanisms
Guy Lorberbom and Daniel Johnson and Chris J Maddison and Daniel Tarlow and Tamir Hazan
2021
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem
Haonan Duan and Pashootan Vaezipoor and Max B Paulus and Yangjun Ruan and Chris J Maddison
2022
Learning Branching Heuristics for Propositional Model Counting
Pashootan Vaezipoor and Gil Lederman and Yuhuai Wu and Chris J Maddison and Roger Grosse and Edward Lee and Sanjit A Seshia and Fahiem Bacchus
2021
Direct policy gradients: Direct optimization of policies in discrete action spaces
Guy Lorberbom and Chris J Maddison and Nicolas Heess and Tamir Hazan and Daniel Tarlow
2020
Neural network systems implementing conditional neural processes for efficient learning
Tiago Miguel Sargento Pires Ramalho and Dan Rosenbaum and Marta Garnelo and Christopher Maddison and Seyed Mohammadali Eslami and Yee Whye Teh and Danilo Jimenez Rezende
2021
The machine learning for combinatorial optimization competition (ml4co): Results and insights
Maxime Gasse and Simon Bowly and Quentin Cappart and Jonas Charfreitag and Laurent Charlin and Didier Chételat and Antonia Chmiela and Justin Dumouchelle and Ambros Gleixner and Aleksandr M Kazachkov and Elias Khalil and Pawel Lichocki and Andrea Lodi and Miles Lubin and Chris J Maddison and Morris Christopher and Dimitri J Papageorgiou and Augustin Parjadis and Sebastian Pokutta and Antoine Prouvost and Lara Scavuzzo and Giulia Zarpellon and Linxin Yang and Sha Lai and Akang Wang and Xiaodong Luo and Xiang Zhou and Haohan Huang and Shengcheng Shao and Yuanming Zhu and Dong Zhang and Tao Quan and Zixuan Cao and Yang Xu and Zhewei Huang and Shuchang Zhou and Chen Binbin and He Minggui and Hao Hao and Zhang Zhiyu and An Zhiwu and Mao Kun
2022
Unbiased gradient estimation with balanced assignments for mixtures of experts
Wouter Kool and Chris J Maddison and Andriy Mnih
2021
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
Daniel D Johnson and Ayoub El Hanchi and Chris J Maddison
2023
Bayesian nonparametrics for offline skill discovery
Valentin Villecroze and Harry Braviner and Panteha Naderian and Chris Maddison and Gabriel Loaiza-Ganem
2022
Stochastic Reweighted Gradient Descent
Ayoub El Hanchi and David Stephens and Chris Maddison
2022
Learning to Extend Program Graphs to Work-in-Progress Code
Xuechen Li and Chris J Maddison and Daniel Tarlow
2021
Training a policy neural network and a value neural network
Thore Kurt Hartwig Graepel and Shih-Chieh Huang and David Silver and Arthur Clement Guez and Laurent Sifre and Ilya Sutskever and Christopher Maddison
2018
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator
Max B Paulus and Chris J Maddison and Andreas Krause
2021
Probabilistic invariant learning with randomized linear classifiers
Leonardo Cotta and Gal Yehuda and Assaf Schuster and Chris J Maddison
2023
Identifying the risks of lm agents with an lm-emulated sandbox
Yangjun Ruan and Honghua Dong and Andrew Wang and Silviu Pitis and Yongchao Zhou and Jimmy Ba and Yann Dubois and Chris J Maddison and Tatsunori Hashimoto
2024
Experts Don’t Cheat: Learning What You Don’t Know By Predicting Pairs
Daniel D Johnson and Daniel Tarlow and David Duvenaud and Chris J Maddison
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