David Duvenaud

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 87

Top Venues

CoRR

NeurIPS

ICML

AISTATS

Publications

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, Ethan Perez, Evan Hubinger
Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. 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)
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, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez
Towards Understanding Sycophancy in Language Models. ICLR (2024)
James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR (2024)
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, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul F. Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, Ethan Perez
Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. CoRR (2024)
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, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez
Towards Understanding Sycophancy in Language Models. CoRR (2023)
Jack Richter-Powell, Luca A. Thiede, Alán Aspuru-Guzik, David Duvenaud
Sorting Out Quantum Monte Carlo. CoRR (2023)
Dami Choi, Yonadav Shavit, David Duvenaud
Tools for Verifying Neural Models' Training Data. CoRR (2023)
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. AISTATS (2022)
Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
On Implicit Bias in Overparameterized Bilevel Optimization. CoRR (2022)
Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
On Implicit Bias in Overparameterized Bilevel Optimization. ICML (2022)
David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling
Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). Dagstuhl Reports 12 (8) (2022)
Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud
Complex Momentum for Optimization in Games. AISTATS (2022)
Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin
Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. Proc. ACM Program. Lang. 5 (POPL) (2021)
Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton
Teaching with Commentaries. ICLR (2021)
Jonathan Lorraine, David Acuna, Paul Vicol, David Duvenaud
Complex Momentum for Learning in Games. 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)
Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin
Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming. CoRR (2021)
Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. CoRR (2021)
Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. ICLR (2021)
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
Meta-Learning to Improve Pre-Training. CoRR (2021)
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML (2021)
Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
Meta-learning to Improve Pre-training. NeurIPS (2021)
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. CoRR (2020)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
Your classifier is secretly an energy based model and you should treat it like one. ICLR (2020)
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. ICLR (2020)
Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba
A Study of Gradient Variance in Deep Learning. CoRR (2020)
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
Scalable Gradients for Stochastic Differential Equations. AISTATS (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS (2020)
Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg
What went wrong and when? Instance-wise feature importance for time-series black-box models. NeurIPS (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR (2020)
Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg
What went wrong and when? Instance-wise Feature Importance for Time-series Models. CoRR (2020)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. ICML (2020)
Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
Scalable Gradients for Stochastic Differential Equations. CoRR (2020)
Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud
Learning Differential Equations that are Easy to Solve. NeurIPS (2020)
Jacob Kelly, Jesse Bettencourt, Matthew James Johnson, David Duvenaud
Learning Differential Equations that are Easy to Solve. CoRR (2020)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. CoRR (2020)
Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton
Teaching with Commentaries. CoRR (2020)
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
No MCMC for me: Amortized sampling for fast and stable training of energy-based models. CoRR (2020)
Jonathan Lorraine, Paul Vicol, David Duvenaud
Optimizing Millions of Hyperparameters by Implicit Differentiation. AISTATS (2020)
Lawson Fulton, Vismay Modi, David Duvenaud, David I. W. Levin, Alec Jacobson
Latent-space Dynamics for Reduced Deformable Simulation. Comput. Graph. Forum 38 (2) (2019)
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. CoRR (2019)
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
Explaining Image Classifiers by Counterfactual Generation. ICLR (Poster) (2019)
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen
Invertible Residual Networks. ICML (2019)
Ricky T. Q. Chen, David Duvenaud
Neural Networks with Cheap Differential Operators. CoRR (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR (2019)
Jonathan Lorraine, Paul Vicol, David Duvenaud
Optimizing Millions of Hyperparameters by Implicit Differentiation. CoRR (2019)
Tian Qi Chen, David Duvenaud
Neural Networks with Cheap Differential Operators. NeurIPS (2019)
Yulia Rubanova, Tian Qi Chen, David Duvenaud
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. NeurIPS (2019)
Kawin Ethayarajh, David Duvenaud, Graeme Hirst
Understanding Undesirable Word Embedding Associations. ACL (1) (2019)
Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. ICLR (Poster) (2019)
Kawin Ethayarajh, David Duvenaud, Graeme Hirst
Towards Understanding Linear Word Analogies. ACL (1) (2019)
Kawin Ethayarajh, David Duvenaud, Graeme Hirst
Understanding Undesirable Word Embedding Associations. CoRR (2019)
Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
Latent ODEs for Irregularly-Sampled Time Series. CoRR (2019)
Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Residual Flows for Invertible Generative Modeling. NeurIPS (2019)
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Residual Flows for Invertible Generative Modeling. CoRR (2019)
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR (2019)
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. CoRR (2019)
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS (2019)
Chris Cremer, Xuechen Li, David Duvenaud
Inference Suboptimality in Variational Autoencoders. ICML (2018)
Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
Noisy Natural Gradient as Variational Inference. ICML (2018)
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders. CoRR (2018)
Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. CoRR (2018)
Jonathan Lorraine, David Duvenaud
Stochastic Hyperparameter Optimization through Hypernetworks. CoRR (2018)
Kawin Ethayarajh, David Duvenaud, Graeme Hirst
Towards Understanding Linear Word Analogies. CoRR (2018)
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders. ICLR (Workshop) (2018)
Elias Tragas, Calvin Luo, Maxime Gazeau, Kevin Luk, David Duvenaud
Scalable Recommender Systems through Recursive Evidence Chains. CoRR (2018)
Chris Cremer, Xuechen Li, David Duvenaud
Inference Suboptimality in Variational Autoencoders. CoRR (2018)
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
Neural Ordinary Differential Equations. NeurIPS (2018)
Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens
Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. ICLR (Workshop) (2018)
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR (2018)
Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders. NeurIPS (2018)
Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
Neural Ordinary Differential Equations. CoRR (2018)
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) (2018)
Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Invertible Residual Networks. CoRR (2018)
George-Alexandru Adam, Petr Smirnov, Anna Goldenberg, David Duvenaud, Benjamin Haibe-Kains
Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples. CoRR (2018)
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. NIPS (2017)
Chris Cremer, Quaid Morris, David Duvenaud
Reinterpreting Importance-Weighted Autoencoders. ICLR (Workshop) (2017)
Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
Noisy Natural Gradient as Variational Inference. CoRR (2017)
Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey
Generating and designing DNA with deep generative models. CoRR (2017)
Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR (2017)
Geoffrey Roeder, Yuhuai Wu, David Duvenaud
Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. CoRR (2017)
David Duvenaud, Dougal Maclaurin, Ryan P. Adams
Early Stopping as Nonparametric Variational Inference. AISTATS (2016)
Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik
Automatic chemical design using a data-driven continuous representation of molecules. CoRR (2016)
Eric Schulz, Josh Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
Probing the Compositionality of Intuitive Functions. NIPS (2016)
Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta
Composing graphical models with neural networks for structured representations and fast inference. NIPS (2016)
Cheng-Zhi Anna Huang, David Duvenaud, Krzysztof Z. Gajos
ChordRipple: Recommending Chords to Help Novice Composers Go Beyond the Ordinary. IUI (2016)
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
Convolutional Networks on Graphs for Learning Molecular Fingerprints. CoRR (2015)
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
Convolutional Networks on Graphs for Learning Molecular Fingerprints. NIPS (2015)
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Gradient-based Hyperparameter Optimization through Reversible Learning. CoRR (2015)
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Early Stopping is Nonparametric Variational Inference. CoRR (2015)
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Gradient-based Hyperparameter Optimization through Reversible Learning. ICML (2015)
David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
Avoiding pathologies in very deep networks. AISTATS (2014)
David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
Avoiding pathologies in very deep networks. CoRR (2014)
Michael Schober, David Duvenaud, Philipp Hennig
Probabilistic ODE Solvers with Runge-Kutta Means. NIPS (2014)
James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
Automatic Construction and Natural-Language Description of Nonparametric Regression Models. CoRR (2014)
James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
Automatic Construction and Natural-Language Description of Nonparametric Regression Models. AAAI (2014)
Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
Warped Mixtures for Nonparametric Cluster Shapes. CoRR (2014)
Michael Schober, David Duvenaud, Philipp Hennig
Probabilistic ODE Solvers with Runge-Kutta Means. CoRR (2014)
Cheng-Zhi Anna Huang, David Duvenaud, Kenneth C. Arnold, Brenton Partridge, Josiah W. Oberholtzer, Krzysztof Z. Gajos
Active learning of intuitive control knobs for synthesizers using gaussian processes. IUI (2014)

David Duvenaud.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

David Duvenaud