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ORCIDChris J. Maddison
Christopher Maddison
Person information
- affiliation: University of Toronto, Department of Computer Science, ON, Canada
- affiliation: University of Toronto, Department of Statistical Sciences, ON, Canada
- affiliation (PhD 2020): University of Oxford, Department of Statistics, Oxford, UK
- affiliation: DeepMind, London, UK
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Books and Theses
- 2020
[b1]Chris J. Maddison:
Between integrals and optima: new methods for scalable machine learning. University of Oxford, UK, 2020
Journal Articles
- 2021
- [j2]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet:
Dual Space Preconditioning for Gradient Descent. SIAM J. Optim. 31(1): 991-1016 (2021) - 2016
- [j1]David Silver, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Vedavyas Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy P. Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, Demis Hassabis:
Mastering the game of Go with deep neural networks and tree search. Nat. 529(7587): 484-489 (2016)
Conference and Workshop Papers
- 2024
- [c38]Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu:
Minimax Linear Regression under the Quantile Risk. COLT 2024: 1516-1572 - [c37]Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto:
Identifying the Risks of LM Agents with an LM-Emulated Sandbox. ICLR 2024 - [c36]Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison:
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. ICML 2024 - [c35]Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison:
End-To-End Causal Effect Estimation from Unstructured Natural Language Data. NeurIPS 2024 - [c34]Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu:
On the Efficiency of ERM in Feature Learning. NeurIPS 2024 - [c33]Yangjun Ruan, Chris J. Maddison, Tatsunori B. Hashimoto:
Observational Scaling Laws and the Predictability of Langauge Model Performance. NeurIPS 2024 - 2023
- [c32]Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison:
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. ICLR 2023 - [c31]Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison:
Probabilistic Invariant Learning with Randomized Linear Classifiers. NeurIPS 2023 - [c30]Honghua Dong, Jiawei Xu, Yu Yang, Rui Zhao, Shiwen Wu, Chun Yuan, Xiu Li, Chris J. Maddison, Lei Han:
MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy. NeurIPS 2023 - [c29]Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Dan Roy:
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. NeurIPS 2023 - 2022
- [c28]Yangjun Ruan, Yann Dubois, Chris J. Maddison:
Optimal Representations for Covariate Shift. ICLR 2022 - [c27]Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison:
Augment with Care: Contrastive Learning for Combinatorial Problems. ICML 2022: 5627-5642 - [c26]Ayoub El Hanchi, David A. Stephens, Chris J. Maddison:
Stochastic Reweighted Gradient Descent. ICML 2022: 8359-8374 - [c25]Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison:
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning. ICML 2022: 17584-17600 - [c24]Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem:
Bayesian Nonparametrics for Offline Skill Discovery. ICML 2022: 22284-22299 - 2021
- [c23]Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus:
Learning Branching Heuristics for Propositional Model Counting. AAAI 2021: 12427-12435 - [c22]Max B. Paulus, Chris J. Maddison, Andreas Krause:
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. ICLR 2021 - [c21]Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison:
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML 2021: 3831-3841 - [c20]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. ICML 2021: 9136-9147 - [c19]Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao:
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. NeurIPS (Competition and Demos) 2021: 220-231 - [c18]Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison:
Lossy Compression for Lossless Prediction. NeurIPS 2021: 14014-14028 - [c17]Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan:
Learning Generalized Gumbel-max Causal Mechanisms. NeurIPS 2021: 26792-26803 - 2020
- [c16]Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow:
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. NeurIPS 2020 - [c15]Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison:
Gradient Estimation with Stochastic Softmax Tricks. NeurIPS 2020 - 2019
- [c14]George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison:
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) 2019 - [c13]Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS 2019: 12544-12555 - [c12]Brendan O'Donoghue, Chris J. Maddison:
Hamiltonian descent for composite objectives. NeurIPS 2019: 14443-14453 - 2018
- [c11]Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami:
Conditional Neural Processes. ICML 2018: 1690-1699 - [c10]Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh:
Tighter Variational Bounds are Not Necessarily Better. ICML 2018: 4274-4282 - 2017
- [c9]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. ICLR (Workshop) 2017 - [c8]Chris J. Maddison, Andriy Mnih, Yee Whye Teh:
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR (Poster) 2017 - [c7]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) 2017 - [c6]George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS 2017: 2627-2636 - [c5]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. NIPS 2017: 6573-6583 - 2015
- [c4]Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver:
Move Evaluation in Go Using Deep Convolutional Neural Networks. ICLR (Poster) 2015 - 2014
- [c3]Chris J. Maddison, Daniel Tarlow:
Structured Generative Models of Natural Source Code. ICML 2014: 649-657 - [c2]Chris J. Maddison, Daniel Tarlow, Tom Minka:
A* Sampling. NIPS 2014: 3086-3094 - 2013
- [c1]Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov:
Annealing between distributions by averaging moments. NIPS 2013: 2769-2777
Informal and Other Publications
- 2024
- [i38]Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison:
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. CoRR abs/2402.08733 (2024) - [i37]Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto:
Observational Scaling Laws and the Predictability of Language Model Performance. CoRR abs/2405.10938 (2024) - [i36]Anvith Thudi, Chris J. Maddison:
Finding Optimally Robust Data Mixtures via Concave Maximization. CoRR abs/2406.01477 (2024) - [i35]Leonardo Cotta, Chris J. Maddison:
Out-Of-Context Prompting Boosts Fairness and Robustness in Large Language Model Predictions. CoRR abs/2406.07685 (2024) - [i34]Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si:
APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts. CoRR abs/2406.13161 (2024) - [i33]Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison:
End-To-End Causal Effect Estimation from Unstructured Natural Language Data. CoRR abs/2407.07018 (2024) - [i32]Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu:
On the Efficiency of ERM in Feature Learning. CoRR abs/2411.12029 (2024) - 2023
- [i31]George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson:
Benchmarking Neural Network Training Algorithms. CoRR abs/2306.07179 (2023) - [i30]Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Daniel M. Roy:
The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. CoRR abs/2306.17759 (2023) - [i29]Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison:
Probabilistic Invariant Learning with Randomized Linear Classifiers. CoRR abs/2308.04412 (2023) - [i28]Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto:
Identifying the Risks of LM Agents with an LM-Emulated Sandbox. CoRR abs/2309.15817 (2023) - 2022
- [i27]Yangjun Ruan, Yann Dubois, Chris J. Maddison:
Optimal Representations for Covariate Shift. CoRR abs/2201.00057 (2022) - [i26]Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem:
Bayesian Nonparametrics for Offline Skill Discovery. CoRR abs/2202.04675 (2022) - [i25]Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison:
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem. CoRR abs/2202.08396 (2022) - [i24]Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias B. Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Sheng Cheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Binbin Chen, Minggui He, Hao Hao, Zhiyu Zhang, Zhiwu An, Kun Mao:
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. CoRR abs/2203.02433 (2022) - [i23]Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison:
Learning To Cut By Looking Ahead: Cutting Plane Selection via Imitation Learning. CoRR abs/2206.13414 (2022) - [i22]Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison:
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. CoRR abs/2210.01883 (2022) - 2021
- [i21]Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison:
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR abs/2102.04509 (2021) - [i20]Yangjun Ruan, Karen Ullrich, Daniel Severo
, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. CoRR abs/2102.11086 (2021) - [i19]Xuechen Li, Chris J. Maddison, Daniel Tarlow:
Learning to Extend Program Graphs to Work-in-Progress Code. CoRR abs/2105.14038 (2021) - [i18]Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison:
Lossy Compression for Lossless Prediction. CoRR abs/2106.10800 (2021) - [i17]Wouter Kool, Chris J. Maddison, Andriy Mnih:
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts. CoRR abs/2109.11817 (2021) - [i16]Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan:
Learning Generalized Gumbel-max Causal Mechanisms. CoRR abs/2111.06888 (2021) - 2020
- [i15]Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison:
Gradient Estimation with Stochastic Softmax Tricks. CoRR abs/2006.08063 (2020) - [i14]Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Edward A. Lee, Sanjit A. Seshia, Fahiem Bacchus:
Learning Branching Heuristics for Propositional Model Counting. CoRR abs/2007.03204 (2020) - [i13]Max B. Paulus, Chris J. Maddison, Andreas Krause:
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. CoRR abs/2010.04838 (2020) - 2019
- [i12]Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh:
Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR abs/1901.06033 (2019) - [i11]Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow:
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. CoRR abs/1906.06062 (2019) - [i10]Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl:
On Empirical Comparisons of Optimizers for Deep Learning. CoRR abs/1910.05446 (2019) - 2018
- [i9]Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh:
Tighter Variational Bounds are Not Necessarily Better. CoRR abs/1802.04537 (2018) - [i8]Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami:
Conditional Neural Processes. CoRR abs/1807.01613 (2018) - [i7]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet:
Hamiltonian Descent Methods. CoRR abs/1809.05042 (2018) - [i6]George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison:
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR abs/1810.04152 (2018) - 2017
- [i5]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. CoRR abs/1703.05820 (2017) - [i4]George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein:
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR abs/1703.07370 (2017) - [i3]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. CoRR abs/1705.09279 (2017) - 2016
- [i2]Chris J. Maddison, Andriy Mnih, Yee Whye Teh:
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. CoRR abs/1611.00712 (2016) - 2014
- [i1]Chris J. Maddison, Daniel Tarlow:
Structured Generative Models of Natural Source Code. CoRR abs/1401.0514 (2014)
Coauthor Index
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