Chris J. Maddison

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 66

Top Topics

Gradient Estimators

Top Venues

Publications

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 (2024)
Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison
Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. CoRR (2024)
Leonardo Cotta, Chris J. Maddison
Out-Of-Context Prompting Boosts Fairness and Robustness in Large Language Model Predictions. CoRR (2024)
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)
Anvith Thudi, Chris J. Maddison
Finding Optimally Robust Data Mixtures via Concave Maximization. CoRR (2024)
Ayoub El Hanchi, Chris J. Maddison, Murat A. Erdogdu
Minimax Linear Regression under the Quantile Risk. COLT (2024)
Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto
Observational Scaling Laws and the Predictability of Language Model Performance. CoRR (2024)
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison
End-To-End Causal Effect Estimation from Unstructured Natural Language Data. CoRR (2024)
Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison
Probabilistic Invariant Learning with Randomized Linear Classifiers. CoRR (2023)
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)
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 (2023)
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)
Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. ICLR (2023)
Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J. Maddison
Probabilistic Invariant Learning with Randomized Linear Classifiers. NeurIPS (2023)
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 (2023)
Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem
Bayesian Nonparametrics for Offline Skill Discovery. CoRR (2022)
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 (2022)
Yangjun Ruan, Yann Dubois, Chris J. Maddison
Optimal Representations for Covariate Shift. ICLR (2022)
Yangjun Ruan, Yann Dubois, Chris J. Maddison
Optimal Representations for Covariate Shift. CoRR (2022)
Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem
Bayesian Nonparametrics for Offline Skill Discovery. ICML (2022)
Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison
Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem. CoRR (2022)
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 Yangm, 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, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun
The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights. CoRR (2022)
Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. CoRR (2022)
Haonan Duan, Pashootan Vaezipoor, Max B. Paulus, Yangjun Ruan, Chris J. Maddison
Augment with Care: Contrastive Learning for Combinatorial Problems. ICML (2022)
Ayoub El Hanchi, David A. Stephens, Chris J. Maddison
Stochastic Reweighted Gradient Descent. ICML (2022)
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)
Xuechen Li, Chris J. Maddison, Daniel Tarlow
Learning to Extend Program Graphs to Work-in-Progress Code. CoRR (2021)
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR (2021)
Wouter Kool, Chris J. Maddison, Andriy Mnih
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts. CoRR (2021)
Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet
Dual Space Preconditioning for Gradient Descent. SIAM J. Optim. 31 (1) (2021)
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan
Learning Generalized Gumbel-max Causal Mechanisms. CoRR (2021)
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)
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML (2021)
Max B. Paulus, Chris J. Maddison, Andreas Krause
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. ICLR (2021)
Maxime Gasse, Simon Bowly, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros M. Gleixner, Aleksandr M. Kazachkov, Elias 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)
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)
Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison
Lossy Compression for Lossless Prediction. CoRR (2021)
Guy Lorberbom, Daniel D. Johnson, Chris J. Maddison, Daniel Tarlow, Tamir Hazan
Learning Generalized Gumbel-max Causal Mechanisms. NeurIPS (2021)
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 (2021)
Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison
Lossy Compression for Lossless Prediction. NeurIPS (2021)
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
Gradient Estimation with Stochastic Softmax Tricks. CoRR (2020)
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 (2020)
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. NeurIPS (2020)
Max B. Paulus, Chris J. Maddison, Andreas Krause
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. CoRR (2020)
Max B. Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J. Maddison
Gradient Estimation with Stochastic Softmax Tricks. NeurIPS (2020)
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Hierarchical Representations with Poincaré Variational Auto-Encoders. CoRR (2019)
Brendan O'Donoghue, Chris J. Maddison
Hamiltonian descent for composite objectives. NeurIPS (2019)
Guy Lorberbom, Chris J. Maddison, Nicolas Heess, Tamir Hazan, Daniel Tarlow
Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. CoRR (2019)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) (2019)
Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh
Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. NeurIPS (2019)
Dami Choi, Christopher J. Shallue, Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl
On Empirical Comparisons of Optimizers for Deep Learning. CoRR (2019)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR (2018)
Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet
Hamiltonian Descent Methods. CoRR (2018)
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 (2018)
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 (2018)
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)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
Filtering Variational Objectives. CoRR (2017)
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)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Particle Value Functions. CoRR (2017)
George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. ICLR (Workshop) (2017)
Chris J. Maddison, Andriy Mnih, Yee Whye Teh
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. ICLR (Poster) (2017)
George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. CoRR (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Particle Value Functions. ICLR (Workshop) (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
Filtering Variational Objectives. NIPS (2017)
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) (2016)
Chris J. Maddison, Andriy Mnih, Yee Whye Teh
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. CoRR (2016)
Chris J. Maddison, Aja Huang, Ilya Sutskever, David Silver
Move Evaluation in Go Using Deep Convolutional Neural Networks. ICLR (Poster) (2015)
Chris J. Maddison, Daniel Tarlow, Tom Minka
A* Sampling. NIPS (2014)
Chris J. Maddison, Daniel Tarlow
Structured Generative Models of Natural Source Code. ICML (2014)
Chris J. Maddison, Daniel Tarlow
Structured Generative Models of Natural Source Code. CoRR (2014)
Roger B. Grosse, Chris J. Maddison, Ruslan Salakhutdinov
Annealing between distributions by averaging moments. NIPS (2013)