Duvenaud, David

36 publications

ICML 2025 Position: Humanity Faces Existential Risk from Gradual Disempowerment Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, David Duvenaud

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

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

ICMLW 2024 LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language James Requeima, John F Bronskill, Dami Choi, Richard E. Turner, David Duvenaud

NeurIPS 2024 Many-Shot Jailbreaking Cem Anil, Esin Durmus, Nina Panickssery, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel Ford, Fracesco Mosconi, Rajashree Agrawal, Rylan Schaeffer, Naomi Bashkansky, Samuel Svenningsen, Mike Lambert, Ansh Radhakrishnan, Carson Denison, Evan J Hubinger, Yuntao Bai, Trenton Bricken, Timothy Maxwell, Nicholas Schiefer, James Sully, Alex Tamkin, Tamera Lanhan, Karina Nguyen, Tomasz Korbak, Jared Kaplan, Deep Ganguli, Samuel R. Bowman, Ethan Perez, Roger Baker Grosse, David Duvenaud

ICMLW 2024 Sorting Out Quantum Monte Carlo Jack Richter-Powell, Luca Thiede, Alan Aspuru-Guzik, David Duvenaud

ICLR 2024 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 M Kravec, Timothy Maxwell, Sam McCandlish, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez

AISTATS 2022 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, David Duvenaud

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

ICLR 2021 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, Kevin Swersky, David Duvenaud

ICML 2021 Oops I Took a Gradient: Scalable Sampling for Discrete Distributions Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison

ICLR 2021 Teaching with Commentaries Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton

ICML 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models Without Sampling Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Richard Zemel

AISTATS 2020 Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine, Paul Vicol, David Duvenaud

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

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

NeurIPSW 2020 Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

ICLR 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, Kevin Swersky

ICLR 2019 Explaining Image Classifiers by Counterfactual Generation Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud

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

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

ICLR 2019 Self-Tuning Networks: Bilevel Optimization of Hyperparameters Using Structured Best-Response Functions Matthew Mackay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse

NeurIPSW 2019 Taylor-Mode Automatic Differentiation for Higher-Order Derivatives in JAX Jesse Bettencourt, Matthew J. Johnson, David Duvenaud

ICLR 2018 Backpropagation Through the Void: Optimizing Control Variates for Black-Box Gradient Estimation Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud

ICML 2018 Inference Suboptimality in Variational Autoencoders Chris Cremer, Xuechen Li, David Duvenaud

ICML 2018 Noisy Natural Gradient as Variational Inference Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse

ICLR 2017 Reinterpreting Importance-Weighted Autoencoders Chris Cremer, Quaid Morris, David Duvenaud

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

ICML 2015 Gradient-Based Hyperparameter Optimization Through Reversible Learning Dougal Maclaurin, David Duvenaud, Ryan Adams

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

AISTATS 2014 Avoiding Pathologies in Very Deep Networks David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

ICML 2013 Structure Discovery in Nonparametric Regression Through Compositional Kernel Search David Duvenaud, James Lloyd, Roger Grosse, Joshua Tenenbaum, Ghahramani Zoubin

UAI 2013 Warped Mixtures for Nonparametric Cluster Shapes Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani

UAI 2012 Optimally-Weighted Herding Is Bayesian Quadrature Ferenc Huszar, David Duvenaud