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Author Information
Ben Letham (Facebook)
David Duvenaud (University of Toronto)
David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.
Dustin Tran (Google Brain)
Dustin Tran is a research scientist at Google Brain. His research contributions examine the intersection of probability and deep learning, particularly in the areas of probabilistic programming, variational inference, giant models, and Bayesian neural networks. He completed his Ph.D. at Columbia under David Blei. He’s received awards such as the John M. Chambers Statistical Software award and the Google Ph.D. Fellowship in Machine Learning. He served as Area Chair at NeurIPS, ICML, ICLR, IJCAI, and AISTATS and organized "Approximate Inference" and "Uncertainty & Robustness" workshops at NeurIPS and UAI.
Aki Vehtari (Aalto University)
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2022 Workshop: The Symbiosis of Deep Learning and Differential Equations II »
Michael Poli · Winnie Xu · Estefany Kelly Buchanan · Maryam Hosseini · Luca Celotti · Martin Magill · Ermal Rrapaj · Qiyao Wei · Stefano Massaroli · Patrick Kidger · Archis Joglekar · Animesh Garg · David Duvenaud -
2021 : Dependent Types for Machine Learning in Dex - David Duvenaud - University of Toronto »
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2021 Poster: Meta-learning to Improve Pre-training »
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2021 Poster: Challenges and Opportunities in High Dimensional Variational Inference »
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2020 : Panel discussion 2 »
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2020 : Invited Talk David Duvenaud »
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2020 Poster: Robust, Accurate Stochastic Optimization for Variational Inference »
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2020 Poster: Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond »
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2020 Tutorial: (Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization Q&A »
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2020 Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning Q&A »
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2020 Poster: BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization »
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2020 Poster: Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization »
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2020 Poster: What went wrong and when? Instance-wise feature importance for time-series black-box models »
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2020 Poster: High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization »
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2020 Spotlight: High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization »
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2020 Poster: Learning Differential Equations that are Easy to Solve »
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2020 Tutorial: (Track3) Deep Implicit Layers: Neural ODEs, Equilibrium Models, and Differentiable Optimization »
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2020 Tutorial: (Track2) Practical Uncertainty Estimation and Out-of-Distribution Robustness in Deep Learning »
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2019 Workshop: Program Transformations for ML »
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2019 : Molecules and Genomes »
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2019 Poster: Latent Ordinary Differential Equations for Irregularly-Sampled Time Series »
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2019 Poster: Residual Flows for Invertible Generative Modeling »
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2019 Spotlight: Residual Flows for Invertible Generative Modeling »
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2019 Poster: Efficient Graph Generation with Graph Recurrent Attention Networks »
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2019 Poster: Neural Networks with Cheap Differential Operators »
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2019 Spotlight: Neural Networks with Cheap Differential Operators »
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2018 Poster: Isolating Sources of Disentanglement in Variational Autoencoders »
Tian Qi Chen · Xuechen (Chen) Li · Roger Grosse · David Duvenaud -
2018 Oral: Isolating Sources of Disentanglement in Variational Autoencoders »
Tian Qi Chen · Xuechen (Chen) Li · Roger Grosse · David Duvenaud -
2018 Poster: Neural Ordinary Differential Equations »
Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud -
2018 Oral: Neural Ordinary Differential Equations »
Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud -
2017 Workshop: Aligned Artificial Intelligence »
Dylan Hadfield-Menell · Jacob Steinhardt · David Duvenaud · David Krueger · Anca Dragan -
2017 : Automatic Chemical Design Using a Data-driven Continuous Representation of Molecules »
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2017 Poster: Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference »
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2016 : Generating Class-conditional Images with Gradient-based Inference »
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2016 : David Duvenaud – No more mini-languages: The power of autodiffing full-featured Python »
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2016 Workshop: Reliable Machine Learning in the Wild »
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2016 Poster: Composing graphical models with neural networks for structured representations and fast inference »
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2016 Poster: Probing the Compositionality of Intuitive Functions »
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2015 : *David Duvenaud* Automatic Differentiation: The most criminally underused tool in probabilistic numerics »
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2015 Poster: Convolutional Networks on Graphs for Learning Molecular Fingerprints »
David Duvenaud · Dougal Maclaurin · Jorge Iparraguirre · Rafael Bombarell · Timothy Hirzel · Alan Aspuru-Guzik · Ryan Adams -
2014 Poster: Probabilistic ODE Solvers with Runge-Kutta Means »
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2014 Oral: Probabilistic ODE Solvers with Runge-Kutta Means »
Michael Schober · David Duvenaud · Philipp Hennig -
2012 Poster: Active Learning of Model Evidence Using Bayesian Quadrature »
Michael A Osborne · David Duvenaud · Roman Garnett · Carl Edward Rasmussen · Stephen J Roberts · Zoubin Ghahramani -
2011 Poster: Additive Gaussian Processes »
David Duvenaud · Hannes Nickisch · Carl Edward Rasmussen -
2009 Poster: Gaussian process regression with Student-t likelihood »
Jarno Vanhatalo · Pasi Jylänki · Aki Vehtari