Causal learning without DAGs. - David Duvenaud; Daniel Eaton; Kevin P. Murphy; Mark Schmidt 001 (NIPS Causality: Objectives and Assessment, 2010)

Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification. - David Duvenaud; Benjamin M. Marlin; Kevin P. Murphy (CRV, 2011)

Additive Gaussian Processes. - David Duvenaud; Hannes Nickisch; Carl Edward Rasmussen (NIPS, 2011)

Active Learning of Model Evidence Using Bayesian Quadrature. - Michael A. Osborne; David Duvenaud; Roman Garnett; Carl E. Rasmussen; Stephen J. Roberts; Zoubin Ghahramani (NIPS, 2012)

Optimally-Weighted Herding is Bayesian Quadrature. - Ferenc Huszar; David Duvenaud (UAI, 2012)

Structure Discovery in Nonparametric Regression through Compositional Kernel Search. - David Duvenaud; James Robert Lloyd; Roger B. Grosse; Joshua B. Tenenbaum; Zoubin Ghahramani (ICML (3), 2013)

Warped Mixtures for Nonparametric Cluster Shapes. - Tomoharu Iwata; David Duvenaud; Zoubin Ghahramani (UAI, 2013)

Automatic Construction and Natural-Language Description of Nonparametric Regression Models. - James Robert Lloyd; David Duvenaud; Roger B. Grosse; Joshua B. Tenenbaum; Zoubin Ghahramani (AAAI, 2014)

Avoiding pathologies in very deep networks. - David Duvenaud; Oren Rippel; Ryan P. Adams; Zoubin Ghahramani (AISTATS, 2014)

Active learning of intuitive control knobs for synthesizers using gaussian processes. - Cheng-Zhi Anna Huang; David Duvenaud; Kenneth C. Arnold; Brenton Partridge; Josiah W. Oberholtzer; Krzysztof Z. Gajos (IUI, 2014)

Probabilistic ODE Solvers with Runge-Kutta Means. - Michael Schober; David Duvenaud; Philipp Hennig (NIPS, 2014)

Testing MCMC code. - Roger B. Grosse; David Kristjanson Duvenaud (CoRR, 2014)

Gradient-based Hyperparameter Optimization through Reversible Learning. - Dougal Maclaurin; David Duvenaud; Ryan P. Adams (ICML, 2015)

Convolutional Networks on Graphs for Learning Molecular Fingerprints. - David Duvenaud; Dougal Maclaurin; Jorge Aguilera-Iparraguirre; Rafael Gómez-Bombarelli; Timothy Hirzel; Alán Aspuru-Guzik; Ryan P. Adams (NIPS, 2015)

Early Stopping is Nonparametric Variational Inference. - Dougal Maclaurin; David Duvenaud; Ryan P. Adams (CoRR, 2015)

Early Stopping as Nonparametric Variational Inference. - David Duvenaud; Dougal Maclaurin; Ryan P. Adams (AISTATS, 2016)

ChordRipple: Recommending Chords to Help Novice Composers Go Beyond the Ordinary. - Cheng-Zhi Anna Huang; David Duvenaud; Krzysztof Z. Gajos (IUI, 2016)

Composing graphical models with neural networks for structured representations and fast inference. - Matthew J. Johnson 002; David Duvenaud; Alexander B. Wiltschko; Ryan P. Adams; Sandeep R. Datta (NIPS, 2016)

Probing the Compositionality of Intuitive Functions. - Eric Schulz; Josh Tenenbaum 001; David Duvenaud; Maarten Speekenbrink; Samuel J. Gershman (NIPS, 2016)

Automatic chemical design using a data-driven continuous representation of molecules. - Rafael Gómez-Bombarelli; David Duvenaud; José Miguel Hernández-Lobato; Jorge Aguilera-Iparraguirre; Timothy D. Hirzel; Ryan P. Adams; Alán Aspuru-Guzik (CoRR, 2016)

Reinterpreting Importance-Weighted Autoencoders. - Chris Cremer; Quaid Morris; David Duvenaud (ICLR (Workshop), 2017)

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. - Geoffrey Roeder; Yuhuai Wu; David Duvenaud (NIPS, 2017)

Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. - Geoffrey Roeder; Yuhuai Wu; David Duvenaud (CoRR, 2017)

Generating and designing DNA with deep generative models. - Nathan Killoran; Leo J. Lee; Andrew Delong; David Duvenaud; Brendan J. Frey (CoRR, 2017)

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. - Will Grathwohl; Dami Choi; Yuhuai Wu; Geoffrey Roeder; David Duvenaud (ICLR (Poster), 2018)

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. - Zachary Nado; Jasper Snoek; Roger B. Grosse; David Duvenaud; Bowen Xu; James Martens (ICLR (Workshop), 2018)

Inference Suboptimality in Variational Autoencoders. - Chris Cremer; Xuechen Li 005; David Duvenaud (ICML, 2018)

Noisy Natural Gradient as Variational Inference. - Guodong Zhang 006; Shengyang Sun; David Duvenaud; Roger B. Grosse (ICML, 2018)

Isolating Sources of Disentanglement in Variational Autoencoders. - Tian Qi Chen; Xuechen Li 005; Roger B. Grosse; David Duvenaud (NeurIPS, 2018)

Neural Ordinary Differential Equations. - Tian Qi Chen; Yulia Rubanova; Jesse Bettencourt; David Duvenaud (NeurIPS, 2018)

Stochastic Hyperparameter Optimization through Hypernetworks. - Jonathan Lorraine; David Duvenaud (CoRR, 2018)

Scalable Recommender Systems through Recursive Evidence Chains. - Elias Tragas; Calvin Luo; Maxime Gazeau; Kevin Luk; David Duvenaud (CoRR, 2018)

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. - Chun-Hao Chang; Elliot Creager; Anna Goldenberg; David Duvenaud (CoRR, 2018)

Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples. - George-Alexandru Adam; Petr Smirnov; Anna Goldenberg; David Duvenaud; Benjamin Haibe-Kains (CoRR, 2018)

Invertible Residual Networks. - Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 2018)

Scalable Gradients and Variational Inference for Stochastic Differential Equations. - Xuechen Li 005; Ting-Kam Leonard Wong; Ricky T. Q. Chen; David Duvenaud (AABI, 2019)

Understanding Undesirable Word Embedding Associations. - Kawin Ethayarajh; David Duvenaud; Graeme Hirst (ACL (1), 2019)

Towards Understanding Linear Word Analogies. - Kawin Ethayarajh; David Duvenaud; Graeme Hirst (ACL (1), 2019)

Explaining Image Classifiers by Counterfactual Generation. - Chun-Hao Chang; Elliot Creager; Anna Goldenberg; David Duvenaud (ICLR (Poster), 2019)

FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. - Will Grathwohl; Ricky T. Q. Chen; Jesse Bettencourt; Ilya Sutskever; David Duvenaud (ICLR, 2019)

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. - Matthew MacKay; Paul Vicol; Jonathan Lorraine; David Duvenaud; Roger B. Grosse (ICLR (Poster), 2019)

Invertible Residual Networks. - Jens Behrmann; Will Grathwohl; Ricky T. Q. Chen; David Duvenaud; Jörn-Henrik Jacobsen (ICML, 2019)

Residual Flows for Invertible Generative Modeling. - Tian Qi Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (NeurIPS, 2019)

Neural Networks with Cheap Differential Operators. - Tian Qi Chen; David Duvenaud (NeurIPS, 2019)

Efficient Graph Generation with Graph Recurrent Attention Networks. - Renjie Liao; Yujia Li 001; Yang Song 011; Shenlong Wang; William L. Hamilton; David Duvenaud; Raquel Urtasun; Richard S. Zemel (NeurIPS, 2019)

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. - Yulia Rubanova; Tian Qi Chen; David Duvenaud (NeurIPS, 2019)

Latent-space Dynamics for Reduced Deformable Simulation. - Lawson Fulton; Vismay Modi; David Duvenaud; David I. W. Levin; Alec Jacobson (Comput. Graph. Forum, 2019)

Residual Flows for Invertible Generative Modeling. - Ricky T. Q. Chen; Jens Behrmann; David Duvenaud; Jörn-Henrik Jacobsen (CoRR, 2019)

Latent ODEs for Irregularly-Sampled Time Series. - Yulia Rubanova; Ricky T. Q. Chen; David Duvenaud (CoRR, 2019)

Efficient Graph Generation with Graph Recurrent Attention Networks. - Renjie Liao; Yujia Li 001; Yang Song 011; Shenlong Wang; Charlie Nash; William L. Hamilton; David Duvenaud; Raquel Urtasun; Richard S. Zemel (CoRR, 2019)

Neural Networks with Cheap Differential Operators. - Ricky T. Q. Chen; David Duvenaud (CoRR, 2019)

Scalable Gradients for Stochastic Differential Equations. - Xuechen Li 005; Ting-Kam Leonard Wong; Ricky T. Q. Chen; David Duvenaud (AISTATS, 2020)

Optimizing Millions of Hyperparameters by Implicit Differentiation. - Jonathan Lorraine; Paul Vicol; David Duvenaud (AISTATS, 2020)

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. - Ricky T. Q. Chen; Dami Choi; Lukas Balles; David Duvenaud; Philipp Hennig (ICBINB@NeurIPS, 2020)

Your classifier is secretly an energy based model and you should treat it like one. - Will Grathwohl; Kuan-Chieh Wang; Jörn-Henrik Jacobsen; David Duvenaud; Mohammad Norouzi 002; Kevin Swersky (ICLR, 2020)

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. - Yucen Luo; Alex Beatson; Mohammad Norouzi 002; Jun Zhu 001; David Duvenaud; Ryan P. Adams; Ricky T. Q. Chen (ICLR, 2020)

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. - Will Grathwohl; Kuan-Chieh Wang; Jörn-Henrik Jacobsen; David Duvenaud; Richard S. Zemel (ICML, 2020)

Learning Differential Equations that are Easy to Solve. - Jacob Kelly; Jesse Bettencourt; Matthew J. Johnson 002; David Duvenaud (NeurIPS, 2020)

What went wrong and when? Instance-wise feature importance for time-series black-box models. - Sana Tonekaboni; Shalmali Joshi; Kieran Campbell; David Duvenaud; Anna Goldenberg (NeurIPS, 2020)

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. - Will Grathwohl; Kuan-Chieh Wang; Jörn-Henrik Jacobsen; David Duvenaud; Richard S. Zemel (CoRR, 2020)

What went wrong and when? Instance-wise Feature Importance for Time-series Models. - Sana Tonekaboni; Shalmali Joshi; David Duvenaud; Anna Goldenberg (CoRR, 2020)

A Study of Gradient Variance in Deep Learning. - Fartash Faghri; David Duvenaud; David J. Fleet; Jimmy Ba (CoRR, 2020)

No MCMC for me: Amortized sampling for fast and stable training of energy-based models. - Will Sussman Grathwohl; Jacob Jin Kelly; Milad Hashemi; Mohammad Norouzi 002; Kevin Swersky; David Duvenaud (ICLR, 2021)

Teaching with Commentaries. - Aniruddh Raghu; Maithra Raghu; Simon Kornblith; David Duvenaud; Geoffrey E. Hinton (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)

Meta-learning to Improve Pre-training. - Aniruddh Raghu; Jonathan Lorraine; Simon Kornblith; Matthew McDermott; David Duvenaud (NeurIPS, 2021)

Complex Momentum for Learning in Games. - Jonathan Lorraine; David Acuna; Paul Vicol; David Duvenaud (CoRR, 2021)

Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. - Adam Paszke; Daniel D. Johnson 001; David Duvenaud; Dimitrios Vytiniotis; Alexey Radul; Matthew J. Johnson 002; Jonathan Ragan-Kelley; Dougal Maclaurin (Proc. ACM Program. Lang., 2021)

Complex Momentum for Optimization in Games. - Jonathan P. Lorraine; David Acuna; Paul Vicol; David Duvenaud (AISTATS, 2022)

Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. - Winnie Xu; Ricky T. Q. Chen; Xuechen Li 005; David Duvenaud (AISTATS, 2022)

On Implicit Bias in Overparameterized Bilevel Optimization. - Paul Vicol; Jonathan P. Lorraine; Fabian Pedregosa; David Duvenaud; Roger B. Grosse (ICML, 2022)

Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). - David Duvenaud; Markus Heinonen; Michael Tiemann 001; Max Welling (Dagstuhl Reports, 2022)

Tools for Verifying Neural Models' Training Data. - Dami Choi; Yonadav Shavit; David Kristjanson Duvenaud (NeurIPS, 2023)

Towards Understanding Sycophancy in Language Models. - Mrinank Sharma; Meg Tong; Tomasz Korbak; David Duvenaud; Amanda Askell; Samuel R. Bowman; Newton Cheng; Esin Durmus; Zac Hatfield-Dodds; Scott R. Johnston; Shauna Kravec; Timothy Maxwell; et al. (CoRR, 2023)

Sorting Out Quantum Monte Carlo. - Jack Richter-Powell; Luca A. Thiede; Alán Aspuru-Guzik; David Duvenaud (CoRR, 2023)

Towards Understanding Sycophancy in Language Models. - Mrinank Sharma; Meg Tong; Tomasz Korbak; David Duvenaud; Amanda Askell; Samuel R. Bowman; Esin Durmus; Zac Hatfield-Dodds; Scott R. Johnston; Shauna Kravec; Timothy Maxwell; Sam McCandlish; et al. (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)

Many-shot Jailbreaking. - Cem Anil; Esin Durmus; Nina Panickssery; Mrinank Sharma; Joe Benton; Sandipan Kundu; Joshua Batson; Meg Tong; Jesse Mu; Daniel Ford; Francesco Mosconi; Rajashree Agrawal; et al. (NeurIPS, 2024)

LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. - James Requeima; John Bronskill; Dami Choi; Richard E. Turner; David Kristjanson Duvenaud (NeurIPS, 2024)

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. - Evan Hubinger; Carson Denison; Jesse Mu; Mike Lambert; Meg Tong; Monte MacDiarmid; Tamera Lanham; Daniel M. Ziegler; Tim Maxwell; Newton Cheng; Adam S. Jermyn; Amanda Askell; et al. (CoRR, 2024)

Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. - Carson Denison; Monte MacDiarmid; Fazl Barez; David Duvenaud; Shauna Kravec; Samuel Marks; Nicholas Schiefer; Ryan Soklaski; Alex Tamkin; Jared Kaplan; Buck Shlegeris; Samuel R. Bowman; et al. (CoRR, 2024)

Sabotage Evaluations for Frontier Models. - Joe Benton; Misha Wagner; Eric Christiansen; Cem Anil; Ethan Perez; Jai Srivastav; Esin Durmus; Deep Ganguli; Shauna Kravec; Buck Shlegeris; Jared Kaplan; Holden Karnofsky; et al. (CoRR, 2024)

Alignment faking in large language models. - Ryan Greenblatt; Carson Denison; Benjamin Wright; Fabien Roger; Monte MacDiarmid; Samuel Marks; Johannes Treutlein; Tim Belonax; Jack Chen; David Duvenaud; Akbir Khan; Julian Michael; et al. (CoRR, 2024)

Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development. - Jan Kulveit; Raymond Douglas; Nora Ammann; Deger Turan; David Krueger 001; David Duvenaud (CoRR, 2025)

JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs. - Aliaksandra Shysheya; John Bronskill; James Requeima; Shoaib Ahmed Siddiqui; Javier Gonzalez; David Duvenaud; Richard E. Turner (CoRR, 2025)