Maddison, Chris J.

45 publications

TMLR 2026 Bayesian Sensitivity of Causal Inference Estimators Under Evidence-Based Priors Nikita Dhawan, Daniel Shen, Leonardo Cotta, Chris J. Maddison

NeurIPS 2025 A Geometric Analysis of PCA Ayoub El Hanchi, Murat A Erdogdu, Chris J. Maddison

NeurIPS 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, Omar Ibrahim, Hani Goodarzi, Chris J. Maddison, Bo Wang

ICLRW 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

NeurIPS 2025 Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab Haonan Duan, Stephen Zhewen Lu, Caitlin Fiona Harrigan, Nishkrit Desai, Jiarui Lu, Michał Koziarski, Leonardo Cotta, Chris J. Maddison

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

ICML 2025 MixMin: Finding Data Mixtures via Convex Minimization Anvith Thudi, Evianne Rovers, Yangjun Ruan, Tristan Thrush, Chris J. Maddison

TMLR 2025 Test-Time Fairness and Robustness in Large Language Models Leonardo Cotta, Chris J. Maddison

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

ICMLW 2024 End-to-End Causal Effect Estimation from Unstructured Natural Language Data Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul Krishnan, Chris J. Maddison

ICLRW 2024 Experts Don't Cheat: Learning What You Don't Know by Predicting Pairs Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

ICML 2024 Experts Don’t Cheat: Learning What You Don’t Know by Predicting Pairs Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison

ICLR 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

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

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

ICMLW 2024 Out-of-Context Prompting Boosts Fairness and Robustness in Large Language Model Predictions Leonardo Cotta, Chris J. Maddison

ICMLW 2024 PAIR: Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations Haonan Duan, Marta Skreta, Leonardo Cotta, Ella Miray Rajaonson, Nikita Dhawan, Alan Aspuru-Guzik, Chris J. Maddison

ICLR 2023 Contrastive Learning Can Find an Optimal Basis for Approximately View-Invariant Functions Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

NeurIPSW 2023 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

NeurIPS 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

NeurIPS 2023 Probabilistic Invariant Learning with Randomized Linear Classifiers Leonardo Cotta, Gal Yehuda, Assaf Schuster, Chris J Maddison

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

ICMLW 2022 Contrastive Learning Can Find an Optimal Basis for Approximately Invariant Functions Daniel D. Johnson, Ayoub El Hanchi, Chris J. Maddison

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

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

AAAI 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

NeurIPS 2021 Learning Generalized Gumbel-Max Causal Mechanisms Guy Lorberbom, Daniel D. Johnson, Chris J Maddison, Daniel Tarlow, Tamir Hazan

NeurIPS 2021 Lossy Compression for Lossless Prediction Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J Maddison

ICLRW 2021 Lossy Compression for Lossless Prediction Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, Chris J. Maddison

NeurIPSW 2021 Optimal Representations for Covariate Shifts Yann Dubois, Yangjun Ruan, Chris J. Maddison

ICLR 2021 Rao-Blackwellizing the Straight-Through Gumbel-SoftMax Gradient Estimator Max B Paulus, Chris J. Maddison, Andreas Krause

NeurIPSW 2021 Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts Wouter Kool, Chris J. Maddison, Andriy Mnih

NeurIPS 2020 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 Paulus, Dami Choi, Daniel Tarlow, Andreas Krause, Chris J Maddison

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

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

NeurIPS 2019 Hamiltonian Descent for Composite Objectives Brendan O'Donoghue, Chris J. Maddison

NeurIPS 2017 Filtering Variational Objectives Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Teh

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

NeurIPS 2017 REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, Jascha Sohl-Dickstein

ICLR 2017 REBAR: Low-Variance, Unbiased Gradient Estimates for Discrete Latent Variable Models George Tucker, Andriy Mnih, Chris J. Maddison, Jascha Sohl-Dickstein

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

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

NeurIPS 2014 A* Sampling Chris J Maddison, Daniel Tarlow, Tom Minka

NeurIPS 2013 Annealing Between Distributions by Averaging Moments Roger B Grosse, Chris J Maddison, Ruslan Salakhutdinov