David Duvenaud

121 papers A* 23A 10Journal 66Unranked 22

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	International AI Safety Report 2026. CoRR Yoshua Bengio, Stephen Clare, Carina Prunkl, Maksym Andriushchenko, Ben Bucknall, Malcolm Murray, Rishi Bommasani, Stephen Casper, Tom Davidson, Raymond Douglas, David Duvenaud, Philip Fox, Usman Gohar, Rose Hadshar, Anson Ho, Tiancheng Hu, Cameron Jones, Sayash Kapoor, Atoosa Kasirzadeh, Sam Manning, Nestor Maslej, Vasilios Mavroudis, Conor McGlynn, Richard Moulange, Jessica Newman, Kwan Yee Ng, Patricia Paskov, Shalaleh Rismani, Girish Sastry, Elizabeth Seger, Scott Singer, Charlotte Stix, Lucia Velasco, Nicole Wheeler
2026	J	`jnl`	The Artificial Self: Characterising the landscape of AI identity. CoRR Raymond Douglas, Jan Kulveit, Ondrej Havlicek, Theia Pearson-Vogel, Owen Cotton-Barratt, David Duvenaud
2026	J	`jnl`	Who's in Charge? Disempowerment Patterns in Real-World LLM Usage. CoRR Mrinank Sharma, Miles McCain, Raymond Douglas, David Duvenaud
2025	J	`jnl`	A Definition of AGI. CoRR Dan Hendrycks, Dawn Song, Christian Szegedy, Honglak Lee, Yarin Gal, Erik Brynjolfsson, Sharon Li, Andy Zou, Lionel Levine, Bo Han, Jie Fu, Ziwei Liu, Jinwoo Shin, Kimin Lee, Mantas Mazeika, Long Phan, George Ingebretsen, Adam Khoja, Cihang Xie, Olawale Salaudeen, Matthias Hein, Kevin Zhao, Alexander Pan, David Duvenaud, Bo Li, Steve Omohundro, Gabriel Alfour, Max Tegmark, Kevin McGrew, Gary Marcus, Jaan Tallinn, Eric Schmidt, Yoshua Bengio
2025	J	`jnl`	Full-Stack Alignment: Co-Aligning AI and Institutions with Thick Models of Value. CoRR Joe Edelman, Tan Zhi-Xuan, Ryan Lowe, Oliver Klingefjord, Vincent Wang-Mascianica, Matija Franklin, Ryan Othniel Kearns, Ellie Hain, Atrisha Sarkar, Michiel A. Bakker, Fazl Barez, David Duvenaud, Jakob N. Foerster, Iason Gabriel, Joseph Gubbels, Bryce Goodman, Andreas Haupt, Jobst Heitzig, Julian Jara-Ettinger, Atoosa Kasirzadeh, James Ravi Kirkpatrick, Andrew Koh, W. Bradley Knox, Philipp E. Koralus, Joel Lehman, Sydney Levine, Samuele Marro, Manon Revel, Toby Shorin, Morgan Sutherland, Michael Henry Tessler, Ivan Vendrov, James Wilken-Smith
2025	J	`jnl`	Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development. CoRR Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, David Duvenaud
2025	J	`jnl`	JoLT: Joint Probabilistic Predictions on Tabular Data Using LLMs. CoRR Aliaksandra Shysheya, John Bronskill, James Requeima, Shoaib Ahmed Siddiqui, Javier González, David Duvenaud, Richard E. Turner
2025	—	`conf`	Position: Humanity Faces Existential Risk from Gradual Disempowerment. ICML (Position Papers) Jan Kulveit, Raymond Douglas, Nora Ammann, Deger Turan, David Krueger, David Duvenaud
2024	J	`jnl`	Alignment faking in large language models. CoRR Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Samuel Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, Evan Hubinger
2024	A*	`conf`	Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. ICML Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison
2024	J	`jnl`	Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs. CoRR Daniel D. Johnson, Daniel Tarlow, David Duvenaud, Chris J. Maddison
2024	J	`jnl`	LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language. CoRR James Requeima, John Bronskill, Dami Choi, Richard E. Turner, David Duvenaud
2024	J	`jnl`	Sabotage Evaluations for Frontier Models. CoRR Joe Benton, Misha Wagner, Eric Christiansen, Cem Anil, Ethan Perez, Jai Srivastav, Esin Durmus, Deep Ganguli, Shauna Kravec, Buck Shlegeris, Jared Kaplan, Holden Karnofsky, Evan Hubinger, Roger B. Grosse, Samuel R. Bowman, David Duvenaud
2024	J	`jnl`	Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training. CoRR 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 B. 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
2024	J	`jnl`	Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models. CoRR 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
2024	A*	`conf`	Towards Understanding Sycophancy in Language Models. ICLR 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
2023	J	`jnl`	Sorting Out Quantum Monte Carlo. CoRR Jack Richter-Powell, Luca A. Thiede, Alán Aspuru-Guzik, David Duvenaud
2023	J	`jnl`	Tools for Verifying Neural Models' Training Data. CoRR Dami Choi, Yonadav Shavit, David Duvenaud
2023	J	`jnl`	Towards Understanding Sycophancy in Language Models. CoRR 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
2022	A	`conf`	Complex Momentum for Optimization in Games. AISTATS Jonathan P. Lorraine, David Acuna, Paul Vicol, David Duvenaud
2022	J	`jnl`	Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332). Dagstuhl Reports David Duvenaud, Markus Heinonen, Michael Tiemann, Max Welling
2022	A	`conf`	Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. AISTATS Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
2022	A*	`conf`	On Implicit Bias in Overparameterized Bilevel Optimization. ICML Paul Vicol, Jonathan P. Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
2022	J	`jnl`	On Implicit Bias in Overparameterized Bilevel Optimization. CoRR Paul Vicol, Jonathan Lorraine, Fabian Pedregosa, David Duvenaud, Roger B. Grosse
2021	J	`jnl`	Complex Momentum for Learning in Games. CoRR Jonathan Lorraine, David Acuna, Paul Vicol, David Duvenaud
2021	J	`jnl`	Getting to the Point. Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming. CoRR Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin
2021	J	`jnl`	Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming. Proc. ACM Program. Lang. Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew J. Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin
2021	J	`jnl`	Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. CoRR Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
2021	J	`jnl`	Meta-Learning to Improve Pre-Training. CoRR Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
2021	A*	`conf`	Meta-learning to Improve Pre-training. NeurIPS Aniruddh Raghu, Jonathan Lorraine, Simon Kornblith, Matthew McDermott, David Duvenaud
2021	A*	`conf`	No MCMC for me: Amortized sampling for fast and stable training of energy-based models. ICLR Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
2021	A*	`conf`	Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. ICML Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
2021	J	`jnl`	Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. CoRR Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison
2021	A*	`conf`	Teaching with Commentaries. ICLR Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton
2020	J	`jnl`	A Study of Gradient Variance in Deep Learning. CoRR Fartash Faghri, David Duvenaud, David J. Fleet, Jimmy Ba
2020	J	`jnl`	Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling. CoRR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	A*	`conf`	Learning Differential Equations that are Easy to Solve. NeurIPS Jacob Kelly, Jesse Bettencourt, Matthew J. Johnson, David Duvenaud
2020	J	`jnl`	Learning Differential Equations that are Easy to Solve. CoRR Jacob Kelly, Jesse Bettencourt, Matthew James Johnson, David Duvenaud
2020	A*	`conf`	Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling. ICML Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Richard S. Zemel
2020	J	`jnl`	No MCMC for me: Amortized sampling for fast and stable training of energy-based models. CoRR Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
2020	A	`conf`	Optimizing Millions of Hyperparameters by Implicit Differentiation. AISTATS Jonathan Lorraine, Paul Vicol, David Duvenaud
2020	A*	`conf`	SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. ICLR Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
2020	J	`jnl`	SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. CoRR Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
2020	A	`conf`	Scalable Gradients for Stochastic Differential Equations. AISTATS Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2020	J	`jnl`	Scalable Gradients for Stochastic Differential Equations. CoRR Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2020	—	`conf`	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2020	J	`jnl`	Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
2020	J	`jnl`	Teaching with Commentaries. CoRR Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey E. Hinton
2020	J	`jnl`	What went wrong and when? Instance-wise Feature Importance for Time-series Models. CoRR Sana Tonekaboni, Shalmali Joshi, David Duvenaud, Anna Goldenberg
2020	A*	`conf`	What went wrong and when? Instance-wise feature importance for time-series black-box models. NeurIPS Sana Tonekaboni, Shalmali Joshi, Kieran Campbell, David Duvenaud, Anna Goldenberg
2020	A*	`conf`	Your classifier is secretly an energy based model and you should treat it like one. ICLR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019	A*	`conf`	Efficient Graph Generation with Graph Recurrent Attention Networks. NeurIPS Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
2019	J	`jnl`	Efficient Graph Generation with Graph Recurrent Attention Networks. CoRR Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Richard S. Zemel
2019	—	`conf`	Explaining Image Classifiers by Counterfactual Generation. ICLR (Poster) Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
2019	—	`conf`	FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models. ICLR Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
2019	A*	`conf`	Invertible Residual Networks. ICML Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, Jörn-Henrik Jacobsen
2019	J	`jnl`	Latent ODEs for Irregularly-Sampled Time Series. CoRR Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
2019	A*	`conf`	Latent Ordinary Differential Equations for Irregularly-Sampled Time Series. NeurIPS Yulia Rubanova, Tian Qi Chen, David Duvenaud
2019	J	`jnl`	Latent-space Dynamics for Reduced Deformable Simulation. Comput. Graph. Forum Lawson Fulton, Vismay Modi, David Duvenaud, David I. W. Levin, Alec Jacobson
2019	A*	`conf`	Neural Networks with Cheap Differential Operators. NeurIPS Tian Qi Chen, David Duvenaud
2019	J	`jnl`	Neural Networks with Cheap Differential Operators. CoRR Ricky T. Q. Chen, David Duvenaud
2019	J	`jnl`	Optimizing Millions of Hyperparameters by Implicit Differentiation. CoRR Jonathan Lorraine, Paul Vicol, David Duvenaud
2019	A*	`conf`	Residual Flows for Invertible Generative Modeling. NeurIPS Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2019	J	`jnl`	Residual Flows for Invertible Generative Modeling. CoRR Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2019	—	`conf`	Scalable Gradients and Variational Inference for Stochastic Differential Equations. AABI Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2019	—	`conf`	Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. ICLR (Poster) Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
2019	J	`jnl`	Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions. CoRR Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger B. Grosse
2019	—	`conf`	Towards Understanding Linear Word Analogies. ACL (1) Kawin Ethayarajh, David Duvenaud, Graeme Hirst
2019	—	`conf`	Understanding Undesirable Word Embedding Associations. ACL (1) Kawin Ethayarajh, David Duvenaud, Graeme Hirst
2019	J	`jnl`	Understanding Undesirable Word Embedding Associations. CoRR Kawin Ethayarajh, David Duvenaud, Graeme Hirst
2019	J	`jnl`	Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One. CoRR Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2018	—	`conf`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. ICLR (Poster) Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
2018	J	`jnl`	Explaining Image Classifiers by Adaptive Dropout and Generative In-filling. CoRR Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
2018	J	`jnl`	FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models. CoRR Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
2018	A*	`conf`	Inference Suboptimality in Variational Autoencoders. ICML Chris Cremer, Xuechen Li, David Duvenaud
2018	J	`jnl`	Inference Suboptimality in Variational Autoencoders. CoRR Chris Cremer, Xuechen Li, David Duvenaud
2018	J	`jnl`	Invertible Residual Networks. CoRR Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
2018	—	`conf`	Isolating Sources of Disentanglement in Variational Autoencoders. ICLR (Workshop) Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	A*	`conf`	Isolating Sources of Disentanglement in Variational Autoencoders. NeurIPS Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	J	`jnl`	Isolating Sources of Disentanglement in Variational Autoencoders. CoRR Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	A*	`conf`	Neural Ordinary Differential Equations. NeurIPS Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018	J	`jnl`	Neural Ordinary Differential Equations. CoRR Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018	A*	`conf`	Noisy Natural Gradient as Variational Inference. ICML Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
2018	J	`jnl`	Scalable Recommender Systems through Recursive Evidence Chains. CoRR Elias Tragas, Calvin Luo, Maxime Gazeau, Kevin Luk, David Duvenaud
2018	J	`jnl`	Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples. CoRR George-Alexandru Adam, Petr Smirnov, Anna Goldenberg, David Duvenaud, Benjamin Haibe-Kains
2018	—	`conf`	Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure. ICLR (Workshop) Zachary Nado, Jasper Snoek, Roger B. Grosse, David Duvenaud, Bowen Xu, James Martens
2018	J	`jnl`	Stochastic Hyperparameter Optimization through Hypernetworks. CoRR Jonathan Lorraine, David Duvenaud
2018	J	`jnl`	Towards Understanding Linear Word Analogies. CoRR Kawin Ethayarajh, David Duvenaud, Graeme Hirst
2017	J	`jnl`	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation. CoRR Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
2017	J	`jnl`	Generating and designing DNA with deep generative models. CoRR Nathan Killoran, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey
2017	J	`jnl`	Noisy Natural Gradient as Variational Inference. CoRR Guodong Zhang, Shengyang Sun, David Duvenaud, Roger B. Grosse
2017	—	`conf`	Reinterpreting Importance-Weighted Autoencoders. ICLR (Workshop) Chris Cremer, Quaid Morris, David Duvenaud
2017	J	`jnl`	Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference. CoRR Geoffrey Roeder, Yuhuai Wu, David Duvenaud
2017	—	`conf`	Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference. NIPS Geoffrey Roeder, Yuhuai Wu, David Duvenaud
2016	J	`jnl`	Automatic chemical design using a data-driven continuous representation of molecules. CoRR Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik
2016	A	`conf`	ChordRipple: Recommending Chords to Help Novice Composers Go Beyond the Ordinary. IUI Cheng-Zhi Anna Huang, David Duvenaud, Krzysztof Z. Gajos
2016	—	`conf`	Composing graphical models with neural networks for structured representations and fast inference. NIPS Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta
2016	A	`conf`	Early Stopping as Nonparametric Variational Inference. AISTATS David Duvenaud, Dougal Maclaurin, Ryan P. Adams
2016	—	`conf`	Probing the Compositionality of Intuitive Functions. NIPS Eric Schulz, Josh Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
2015	—	`conf`	Convolutional Networks on Graphs for Learning Molecular Fingerprints. NIPS David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
2015	J	`jnl`	Convolutional Networks on Graphs for Learning Molecular Fingerprints. CoRR David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
2015	J	`jnl`	Early Stopping is Nonparametric Variational Inference. CoRR Dougal Maclaurin, David Duvenaud, Ryan P. Adams
2015	A*	`conf`	Gradient-based Hyperparameter Optimization through Reversible Learning. ICML Dougal Maclaurin, David Duvenaud, Ryan P. Adams
2015	J	`jnl`	Gradient-based Hyperparameter Optimization through Reversible Learning. CoRR Dougal Maclaurin, David Duvenaud, Ryan P. Adams
2014	A	`conf`	Active learning of intuitive control knobs for synthesizers using gaussian processes. IUI Cheng-Zhi Anna Huang, David Duvenaud, Kenneth C. Arnold, Brenton Partridge, Josiah W. Oberholtzer, Krzysztof Z. Gajos
2014	A*	`conf`	Automatic Construction and Natural-Language Description of Nonparametric Regression Models. AAAI James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2014	J	`jnl`	Automatic Construction and Natural-Language Description of Nonparametric Regression Models. CoRR James Robert Lloyd, David Duvenaud, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2014	A	`conf`	Avoiding pathologies in very deep networks. AISTATS David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
2014	J	`jnl`	Avoiding pathologies in very deep networks. CoRR David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
2014	—	`conf`	Probabilistic ODE Solvers with Runge-Kutta Means. NIPS Michael Schober, David Duvenaud, Philipp Hennig
2014	J	`jnl`	Probabilistic ODE Solvers with Runge-Kutta Means. CoRR Michael Schober, David Duvenaud, Philipp Hennig
2014	J	`jnl`	Warped Mixtures for Nonparametric Cluster Shapes. CoRR Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
2013	J	`jnl`	Structure Discovery in Nonparametric Regression through Compositional Kernel Search CoRR David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2013	—	`conf`	Structure Discovery in Nonparametric Regression through Compositional Kernel Search. ICML (3) David Duvenaud, James Robert Lloyd, Roger B. Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
2013	A	`conf`	Warped Mixtures for Nonparametric Cluster Shapes. UAI Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
2012	—	`conf`	Active Learning of Model Evidence Using Bayesian Quadrature. NIPS Michael A. Osborne, David Duvenaud, Roman Garnett, Carl E. Rasmussen, Stephen J. Roberts, Zoubin Ghahramani
2012	A	`conf`	Optimally-Weighted Herding is Bayesian Quadrature. UAI Ferenc Huszar, David Duvenaud
2011	J	`jnl`	Additive Gaussian Processes CoRR David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
2011	—	`conf`	Additive Gaussian Processes. NIPS David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
2011	—	`conf`	Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification. CRV David Duvenaud, Benjamin M. Marlin, Kevin P. Murphy
2010	—	`conf`	Causal learning without DAGs. NIPS Causality: Objectives and Assessment David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mark Schmidt