Chris J. Maddison

According to our database, Chris J. Maddison authored at least 54 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

On the Efficiency of ERM in Feature Learning.

Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu

CoRR, 2024

End-To-End Causal Effect Estimation from Unstructured Natural Language Data.

Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison

CoRR, 2024

APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts.

Honghua Dong, Qidong Su, Yubo Gao, Zhaoyu Li, Yangjun Ruan, Gennady Pekhimenko, Chris J. Maddison, Xujie Si

CoRR, 2024

Out-Of-Context Prompting Boosts Fairness and Robustness in Large Language Model Predictions.

Leonardo Cotta, Chris J. Maddison

CoRR, 2024

Finding Optimally Robust Data Mixtures via Concave Maximization.

Anvith Thudi, Chris J. Maddison

CoRR, 2024

Observational Scaling Laws and the Predictability of Language Model Performance.

Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto

CoRR, 2024

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.

Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Identifying the Risks of LM Agents with an LM-Emulated Sandbox.

Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Minimax Linear Regression under the Quantile Risk.

Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit.

Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Daniel M. Roy

CoRR, 2023

Benchmarking Neural Network Training Algorithms.

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

CoRR, 2023

The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit.

Lorenzo Noci, Chuning Li, Mufan Bill Li, Bobby He, Thomas Hofmann, Chris J. Maddison, Dan Roy

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy.

Honghua Dong, Jiawei Xu, Yu Yang, Rui Zhao, Shiwen Wu, Chun Yuan, Xiu Li, Chris J. Maddison, Lei Han

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Probabilistic Invariant Learning with Randomized Linear Classifiers.

Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions.

Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.

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

CoRR, 2022

Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem.

Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison

CoRR, 2022

Bayesian Nonparametrics for Offline Skill Discovery.

Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem

Proceedings of the International Conference on Machine Learning, 2022

Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning.

Max B. Paulus, Giulia Zarpellon, Andreas Krause, Laurent Charlin, Chris J. Maddison

Proceedings of the International Conference on Machine Learning, 2022

Stochastic Reweighted Gradient Descent.

Ayoub El Hanchi, David A. Stephens, Chris J. Maddison

Proceedings of the International Conference on Machine Learning, 2022

Augment with Care: Contrastive Learning for Combinatorial Problems.

Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison

Proceedings of the International Conference on Machine Learning, 2022

Optimal Representations for Covariate Shift.

Yangjun Ruan, Yann Dubois, Chris J. Maddison

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Dual Space Preconditioning for Gradient Descent.

Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet

SIAM J. Optim., 2021

Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts.

Wouter Kool, Chris J. Maddison, Andriy Mnih

CoRR, 2021

Learning to Extend Program Graphs to Work-in-Progress Code.

Xuechen Li, Chris J. Maddison, Daniel Tarlow

CoRR, 2021

Learning Generalized Gumbel-max Causal Mechanisms.

Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights.

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

Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, 2021

Lossy Compression for Lossless Prediction.

Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding.

Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison

Proceedings of the 38th International Conference on Machine Learning, 2021

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.

Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

Proceedings of the 38th International Conference on Machine Learning, 2021

Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator.

Max B. Paulus, Chris J. Maddison, Andreas Krause

Proceedings of the 9th International Conference on Learning Representations, 2021

Learning Branching Heuristics for Propositional Model Counting.

Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Sanjit A. Seshia, Fahiem Bacchus

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Between integrals and optima: new methods for scalable machine learning.

Chris J. Maddison

PhD thesis, 2020

Learning Branching Heuristics for Propositional Model Counting.

Pashootan Vaezipoor, Gil Lederman, Yuhuai Wu, Chris J. Maddison, Roger B. Grosse, Edward A. Lee, Sanjit A. Seshia, Fahiem Bacchus

CoRR, 2020

Gradient Estimation with Stochastic Softmax Tricks.

Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces.

Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

On Empirical Comparisons of Optimizers for Deep Learning.

Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl

CoRR, 2019

Hierarchical Representations with Poincaré Variational Auto-Encoders.

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

CoRR, 2019

Hamiltonian descent for composite objectives.

Brendan O'Donoghue, Chris J. Maddison

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders.

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives.

George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Hamiltonian Descent Methods.

Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet

CoRR, 2018

Tighter Variational Bounds are Not Necessarily Better.

Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

Proceedings of the 35th International Conference on Machine Learning, 2018

Conditional Neural Processes.

Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Jimenez Rezende, S. M. Ali Eslami

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.

George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Filtering Variational Objectives.

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.

George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein

Proceedings of the 5th International Conference on Learning Representations, 2017

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables.

Chris J. Maddison, Andriy Mnih, Yee Whye Teh

Proceedings of the 5th International Conference on Learning Representations, 2017

Particle Value Functions.

Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Mastering the game of Go with deep neural networks and tree search.

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

Nat., 2016

2015

Move Evaluation in Go Using Deep Convolutional Neural Networks.

Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver

Proceedings of the 3rd International Conference on Learning Representations, 2015

2014

A* Sampling.

Chris J. Maddison, Daniel Tarlow, Tom Minka

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Structured Generative Models of Natural Source Code.

Chris J. Maddison, Daniel Tarlow

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Annealing between distributions by averaging moments.

Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013