David Duvenaud
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
35
Venues
7
Active years
2012–2025
Best venue rank
A*
Where they publish
Papers
35 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICML | Position: Humanity Faces Existential Risk from Gradual Disempowerment. | Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, David Duvenaud |
| 2024 | ICLR | 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, Kamal Ndousse, Oliver Rausch, Nicholas Schiefer, Da Yan, Miranda Zhang, Ethan Perez |
| 2024 | ICML | Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. | Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison |
| 2022 | AISTATS | Complex Momentum for Optimization in Games. | Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud |
| 2022 | AISTATS | Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. | Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud |
| 2022 | ICML | On Implicit Bias in Overparameterized Bilevel Optimization. | Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse |
| 2021 | ICLR | 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 |
| 2021 | ICLR | Teaching with Commentaries. | Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton |
| 2021 | ICML | Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. | Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison |
| 2020 | AISTATS | Scalable Gradients for Stochastic Differential Equations. | Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud |
| 2020 | AISTATS | Optimizing Millions of Hyperparameters by Implicit Differentiation. | Jonathan Lorraine, Paul Vicol, David Duvenaud |
| 2020 | ICLR | Your classifier is secretly an energy based model and you should treat it like one. | Will Grathwohl, Kuan-Chieh Wang, Jrn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky |
| 2020 | ICLR | 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 |
| 2020 | ICML | Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. | Will Grathwohl, Kuan-Chieh Wang, Jrn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel |
| 2019 | ACL | Understanding Undesirable Word Embedding Associations. | Kawin Ethayarajh, David Duvenaud, Graeme Hirst |
| 2019 | ACL | Towards Understanding Linear Word Analogies. | Kawin Ethayarajh, David Duvenaud, Graeme Hirst |
| 2019 | ICLR | Explaining Image Classifiers by Counterfactual Generation. | Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud |
| 2019 | ICLR | FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. | Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud |
| 2019 | ICLR | Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. | Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse |
| 2019 | ICML | Invertible Residual Networks. | Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jrn-Henrik Jacobsen |
| 2018 | ICLR | Isolating Sources of Disentanglement in Variational Autoencoders. | Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud |
| 2018 | ICLR | Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. | Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud |
| 2018 | ICLR | Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. | Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens |
| 2018 | ICML | Inference Suboptimality in Variational Autoencoders. | Chris Cremer, Xuechen Li, David Duvenaud |
| 2018 | ICML | Noisy Natural Gradient as Variational Inference. | Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse |
| 2017 | ICLR | Reinterpreting Importance-Weighted Autoencoders. | Chris Cremer, Quaid Morris, David Duvenaud |
| 2016 | AISTATS | Early Stopping as Nonparametric Variational Inference. | David Duvenaud, Dougal Maclaurin, Ryan P. Adams |
| 2016 | IUI | ChordRipple: Recommending Chords to Help Novice Composers Go Beyond the Ordinary. | Cheng-Zhi Anna Huang, David Duvenaud, Krzysztof Z. Gajos |
| 2015 | ICML | Gradient-based Hyperparameter Optimization through Reversible Learning. | Dougal Maclaurin, David Duvenaud, Ryan P. Adams |
| 2014 | AAAI | Automatic Construction and Natural-Language Description of Nonparametric Regression Models. | James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani |
| 2014 | AISTATS | Avoiding pathologies in very deep networks. | David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani |
| 2014 | IUI | 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 |
| 2013 | ICML | Structure Discovery in Nonparametric Regression through Compositional Kernel Search. | David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani |
| 2013 | UAI | Warped Mixtures for Nonparametric Cluster Shapes. | Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani |
| 2012 | UAI | Optimally-Weighted Herding is Bayesian Quadrature. | Ferenc Huszar, David Duvenaud |