Annealing between distributions by averaging moments. - Roger B. Grosse; Chris J. Maddison; Ruslan Salakhutdinov (NIPS, 2013)

Structured Generative Models of Natural Source Code. - Chris J. Maddison; Daniel Tarlow (ICML, 2014)

A* Sampling. - Chris J. Maddison; Daniel Tarlow; Tom Minka (NIPS, 2014)

Move Evaluation in Go Using Deep Convolutional Neural Networks. - Chris J. Maddison; Aja Huang; Ilya Sutskever; David Silver (ICLR (Poster), 2015)

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 002; Julian Schrittwieser; Ioannis Antonoglou; Vedavyas Panneershelvam; Marc Lanctot; Sander Dieleman; Dominik Grewe; et al. (Nat., 2016)

Particle Value Functions. - Chris J. Maddison; Dieterich Lawson; George Tucker; Nicolas Heess; Arnaud Doucet; Andriy Mnih; Yee Whye Teh (ICLR (Workshop), 2017)

The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. - Chris J. Maddison; Andriy Mnih; Yee Whye Teh (ICLR (Poster), 2017)

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. - George Tucker; Andriy Mnih; Chris J. Maddison; Jascha Sohl-Dickstein (ICLR (Workshop), 2017)

Filtering Variational Objectives. - Chris J. Maddison; Dieterich Lawson; George Tucker; Nicolas Heess; Mohammad Norouzi 002; Andriy Mnih; Arnaud Doucet; Yee Whye Teh (NIPS, 2017)

REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. - George Tucker; Andriy Mnih; Chris J. Maddison; Dieterich Lawson; Jascha Sohl-Dickstein (NIPS, 2017)

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 (ICML, 2018)

Tighter Variational Bounds are Not Necessarily Better. - Tom Rainforth; Adam R. Kosiorek; Tuan Anh Le 001; Chris J. Maddison; Maximilian Igl; Frank Wood; Yee Whye Teh (ICML, 2018)

Hamiltonian Descent Methods. - Chris J. Maddison; Daniel Paulin; Yee Whye Teh; Brendan O'Donoghue; Arnaud Doucet (CoRR, 2018)

Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. - George Tucker; Dieterich Lawson; Shixiang Gu; Chris J. Maddison (ICLR (Poster), 2019)

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. - Emile Mathieu; Charline Le Lan; Chris J. Maddison; Ryota Tomioka; Yee Whye Teh (NeurIPS, 2019)

Hamiltonian descent for composite objectives. - Brendan O'Donoghue; Chris J. Maddison (NeurIPS, 2019)

Hierarchical Representations with Poincaré Variational Auto-Encoders. - Emile Mathieu; Charline Le Lan; Chris J. Maddison; Ryota Tomioka; Yee Whye Teh (CoRR, 2019)

On Empirical Comparisons of Optimizers for Deep Learning. - Dami Choi; Christopher J. Shallue; Zachary Nado; Jaehoon Lee 001; Chris J. Maddison; George E. Dahl (CoRR, 2019)

Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces. - Guy Lorberbom; Chris J. Maddison; Nicolas Heess; Tamir Hazan; Daniel Tarlow (NeurIPS, 2020)

Gradient Estimation with Stochastic Softmax Tricks. - Max B. Paulus; Dami Choi; Daniel Tarlow; Andreas Krause 001; Chris J. Maddison (NeurIPS, 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)

Between integrals and optima: new methods for scalable machine learning. - Chris J. Maddison (2020)

Learning Branching Heuristics for Propositional Model Counting. - Pashootan Vaezipoor; Gil Lederman; Yuhuai Wu; Chris J. Maddison; Roger B. Grosse; Sanjit A. Seshia; Fahiem Bacchus (AAAI, 2021)

Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator. - Max B. Paulus; Chris J. Maddison; Andreas Krause 001 (ICLR, 2021)

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. - Will Grathwohl; Kevin Swersky; Milad Hashemi; David Duvenaud; Chris J. Maddison (ICML, 2021)

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. - Yangjun Ruan; Karen Ullrich; Daniel Severo 001; James Townsend; Ashish Khisti; Arnaud Doucet; Alireza Makhzani; Chris J. Maddison (ICML, 2021)

Lossy Compression for Lossless Prediction. - Yann Dubois; Benjamin Bloem-Reddy; Karen Ullrich; Chris J. Maddison (NeurIPS, 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; et al. (NeurIPS (Competition and Demos), 2021)

Learning Generalized Gumbel-max Causal Mechanisms. - Guy Lorberbom; Daniel D. Johnson 001; Chris J. Maddison; Daniel Tarlow; Tamir Hazan (NeurIPS, 2021)

Learning to Extend Program Graphs to Work-in-Progress Code. - Xuechen Li; Chris J. Maddison; Daniel Tarlow (CoRR, 2021)

Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts. - Wouter Kool 001; Chris J. Maddison; Andriy Mnih (CoRR, 2021)

Dual Space Preconditioning for Gradient Descent. - Chris J. Maddison; Daniel Paulin; Yee Whye Teh; Arnaud Doucet (SIAM J. Optim., 2021)

Optimal Representations for Covariate Shift. - Yangjun Ruan; Yann Dubois; Chris J. Maddison (ICLR, 2022)

Augment with Care: Contrastive Learning for Combinatorial Problems. - Haonan Duan 002; Pashootan Vaezipoor; Max B. Paulus; Yangjun Ruan; Chris J. Maddison (ICML, 2022)

Stochastic Reweighted Gradient Descent. - Ayoub El Hanchi; David A. Stephens; Chris J. Maddison (ICML, 2022)

Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning. - Max B. Paulus; Giulia Zarpellon; Andreas Krause 001; Laurent Charlin; Chris J. Maddison (ICML, 2022)

Bayesian Nonparametrics for Offline Skill Discovery. - Valentin Villecroze; Harry J. Braviner; Panteha Naderian; Chris J. Maddison; Gabriel Loaiza-Ganem (ICML, 2022)

Augment with Care: Contrastive Learning for the Boolean Satisfiability Problem. - Haonan Duan 002; Pashootan Vaezipoor; Max B. Paulus; Yangjun Ruan; Chris J. Maddison (CoRR, 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 001; et al. (CoRR, 2022)

Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions. - Daniel D. Johnson 001; Ayoub El Hanchi; Chris J. Maddison (ICLR, 2023)

Probabilistic Invariant Learning with Randomized Linear Classifiers. - Leonardo Cotta; Gal Yehuda; Assaf Schuster; Chris J. Maddison (NeurIPS, 2023)

MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy. - Honghua Dong; Jiawei Xu; Yu Yang 015; Rui Zhao; Shiwen Wu; Chun Yuan; Xiu Li 001; Chris J. Maddison; Lei Han 001 (NeurIPS, 2023)

The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit. - Lorenzo Noci; Chuning Li; Mufan Bill Li; Bobby He; Thomas Hofmann 001; Chris J. Maddison; Dan Roy (NeurIPS, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (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 001; Chris J. Maddison; Daniel M. Roy 001 (CoRR, 2023)

Minimax Linear Regression under the Quantile Risk. - Ayoub El Hanchi; Chris J. Maddison; Murat A. Erdogdu (COLT, 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 (ICLR, 2024)

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. - Daniel D. Johnson 001; Daniel Tarlow; David Duvenaud; Chris J. Maddison (ICML, 2024)

End-To-End Causal Effect Estimation from Unstructured Natural Language Data. - Nikita Dhawan; Leonardo Cotta; Karen Ullrich; Rahul G. Krishnan; Chris J. Maddison (NeurIPS, 2024)

On the Efficiency of ERM in Feature Learning. - Ayoub El Hanchi; Chris J. Maddison; Murat A. Erdogdu (NeurIPS, 2024)

Observational Scaling Laws and the Predictability of Langauge Model Performance. - Yangjun Ruan; Chris J. Maddison; Tatsunori B. Hashimoto (NeurIPS, 2024)

Observational Scaling Laws and the Predictability of Language Model Performance. - Yangjun Ruan; Chris J. Maddison; Tatsunori Hashimoto (CoRR, 2024)

Finding Optimally Robust Data Mixtures via Concave Maximization. - Anvith Thudi; Chris J. Maddison (CoRR, 2024)

Out-Of-Context Prompting Boosts Fairness and Robustness in Large Language Model Predictions. - Leonardo Cotta; 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 (ACL (1), 2025)

MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures. - Anvith Thudi; Chris J. Maddison (ICLR, 2025)

Test-Time Fairness and Robustness in Large Language Models. - Leonardo Cotta; Chris J. Maddison (Trans. Mach. Learn. Res., 2025)

MixMin: Finding Data Mixtures via Convex Minimization. - Anvith Thudi; Evianne Rovers; Yangjun Ruan; Tristan Thrush; Chris J. Maddison (CoRR, 2025)

Reasoning to Learn from Latent Thoughts. - Yangjun Ruan; Neil Band; Chris J. Maddison; Tatsunori Hashimoto (CoRR, 2025)

Enumerate-Conjecture-Prove: Formally Solving Answer-Construction Problems in Math Competitions. - Jialiang Sun; Yuzhi Tang; Ao Li; Chris J. Maddison; Kuldeep S. Meel (CoRR, 2025)

BioReason: Incentivizing Multimodal Biological Reasoning within a DNA-LLM Model. - Adibvafa Fallahpour; Andrew Magnuson; Purav Gupta; Shihao Ma; Jack Naimer; Arnav Shah; Haonan Duan 002; Omar Ibrahim; Hani Goodarzi; Chris J. Maddison; Bo Wang 044 (CoRR, 2025)

Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab. - Haonan Duan 002; Stephen Zhewen Lu; Caitlin Fiona Harrigan; Nishkrit Desai; Jiarui Lu; Michal Koziarski; Leonardo Cotta; Chris J. Maddison (CoRR, 2025)

LM Agents May Fail to Act on Their Own Risk Knowledge. - Yuzhi Tang; Tianxiao Li; Elizabeth Li; Chris J. Maddison; Honghua Dong; Yangjun Ruan (CoRR, 2025)