David Kristjanson Duvenaud

I'm an assistant professor at the University of Toronto. My research focuses on constructing deep probabilistic models to help predict, explain and design things. For example:

Previously, I was a postdoc in the Harvard Intelligent Probabilistic Systems group with Ryan Adams. I did my Ph.D. at the University of Cambridge, where my advisors were Carl Rasmussen and Zoubin Ghahramani. My M.Sc. advisor was Kevin Murphy at the University of British Columbia. I spent a summer working on probabilistic numerics at the Max Planck Institute for Intelligent Systems, and the two summers before that at Google Research, doing machine vision. I co-founded Invenia, an energy forecasting and trading firm where I still consult. I'm also a founding member of the Vector Institute.

Curriculum Vitae

E-mail: duvenaud@cs.toronto.edu

Current research students: How to join

Selected papers

Link Getting to the Point: Index Sets and Parallelism-Preserving Autodiff for Pointful Array Programming

We attempt to combine the clarity and safety of high-level functional languages with the efficiency and parallelism of low-level numerical languages. We treat arrays as eagerly-memoized functions on typed index sets, allowing abstract function manipulations, such as currying, to work on arrays. In contrast to composing primitive bulk-array operations, we argue for an explicit nested indexing style that mirrors application of functions to arguments. We also introduce a fine-grained typed effects system which affords concise and automatically-parallelized in-place updates.

Adam Paszke, Daniel D. Johnson, David Duvenaud, Dimitrios Vytiniotis, Alexey Radul, Matthew Johnson, Jonathan Ragan-Kelley, Dougal Maclaurin
In Submission, 2021
paper | repo
Link Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respect to its discrete inputs to propose updates in a Metropolis-Hastings sampler. We show empirically that this approach outperforms generic samplers in a number of difficult settings including Ising models, Potts models, restricted Boltzmann machines, and factorial hidden Markov models. We also demonstrate the use of our improved sampler for training deep energy-based models on high dimensional discrete data. This approach outperforms variational auto-encoders and existing energy-based models. Finally, we give bounds showing that our approach is near-optimal in the class of samplers which propose local updates.

Will Grathwohl, Milad Hashemi, Kevin Swersky, David Duvenaud, Chris Maddison
In Submission, 2021
paper | slides | bibtex
Link Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations

We perform scalable approximate inference in a recently-proposed family of continuous-depth Bayesian neural networks. In this model class, uncertainty about separate weights in each layer produces dynamics that follow a stochastic differential equation (SDE). We demonstrate gradient-based stochastic variational inference in this infinite-parameter setting, producing arbitrarily-flexible approximate posteriors. We also derive a novel gradient estimator that approaches zero variance as the approximate posterior approaches the true posterior. This approach inherits the memory-efficient training and tunable precision of neural ODEs.

Winnie Xu, Ricky Tian Qi Chen, Xuechen Li, David Duvenaud
In Submission, 2021
paper | code | slides | bibtex
Link Complex Momentum for Learning in Games

We generalize gradient descent with momentum for learning in differentiable games to have complex-valued momentum. We give theoretical motivation for our method by proving convergence on bilinear zero-sum games for simultaneous and alternating updates. Our method gives real-valued parameter updates, making it a drop-in replacement for standard optimizers. We empirically demonstrate that complex-valued momentum can improve convergence in adversarial games-like generative adversarial networks-by showing we can find better solutions with an almost identical computational cost. We also show a practical generalization to a complex-valued Adam variant, which we use to train BigGAN to better inception scores on CIFAR-10.

Jonathan Lorraine, Paul Vicol, David Acuna, David Duvenaud
In submission, 2021
paper | bibtex
Link Teaching with Commentaries

We meta-learn information helpful for training on a particular task or dataset, leveraging recent work on implicit differentiation. We explore applications such as learning weights for individual training examples, parameterizing label-dependent data augmentation policies, and representing attention masks that highlight salient image regions.

Aniruddh Raghu, Maithra Raghu, Simon Kornblith, David Duvenaud, Geoffrey Hinton
International Conference on Learning Representations, 2021
paper | bibtex
Link No MCMC for me: Amortized Sampling for Fast and Stable Training of Energy-Based Models

Energy-Based Models (EBMs) present a flexible and appealing way to represent uncertainty. In this work, we present a simple method for training EBMs at scale which uses an entropy-regularized generator to amortize the MCMC sampling typically used in EBM training. We apply our estimator to the recently proposed Joint Energy Model (JEM), where we match the original performance with faster and stable training. This allows us to extend JEM models to semi-supervised classification on tabular data from a variety of continuous domains.

Will Grathwohl*, Jacob Kelly*, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud
International Conference on Learning Representations, 2021
paper | code | bibtex
Link Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering

We explore the use of exact per-sample Hessian-vector products and gradients to construct optimizers that are self-tuning and hyperparameter-free. Based on a dynamical model, we derive a curvature-corrected, noise-adaptive online gradient estimate. We prove that our model-based procedure converges in the noisy quadratic setting. Though we do not see similar gains in deep learning tasks, we match the performance of well-tuned optimizers. Our initial experiments indicate that when training deep nets our optimizer works too well, in a sense - it descends into regions of high variance and high curvature early on in the optimization, and gets stuck there.

Ricky Tian Qi Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
NeurIPS Workshop on "I Can't Believe It's Not Better!", 2020
paper | slides | talk | bibtex
Link Learning Differential Equations that are Easy to Solve

Neural ODEs become expensive to solve numerically as training progresses. We introduce a differentiable surrogate for the time cost of standard numerical solvers using higher-order derivatives of solution trajectories. These derivatives are efficient to compute with Taylor-mode automatic differentiation. Optimizing this additional objective trades model performance against the time cost of solving the learned dynamics.

Jacob Kelly*, Jesse Bettencourt*, Matthew Johnson, David Duvenaud
Neural Information Processing Systems, 2020.
paper | code | bibtex
Link Scalable Gradients for Stochastic Differential Equations

We generalize the adjoint sensitivity method to stochastic differential equations, allowing time-efficient and constant-memory computation of gradients with high-order adaptive solvers. Specifically, we derive a stochastic differential equation whose solution is the gradient, a memory-efficient algorithm for caching noise, and conditions under which numerical solutions converge. In addition, we combine our method with gradient-based stochastic variational inference for latent stochastic differential equations. We use our method to fit stochastic dynamics defined by neural networks, achieving competitive performance on a 50-dimensional motion capture dataset.

Xuechen Li, Ting-Kam Leonard Wong, Ricky Tian Qi Chen, David Duvenaud
Artificial Intelligence and Statistics, 2020.
paper | slides | talk | bibtex
Link Optimizing Millions of Hyperparameters by Implicit Differentiation

We use the implicit function theorem to scalably approximate gradients of the validation loss with respect to hyperparameters. This lets us train networks with millions of weights and millions of hyperparameters. For instance, we learn a data-augmentation network - where every weight is a hyperparameter tuned for validation performance - that outputs augmented training examples, from scratch. We also learn a distilled dataset where each feature in each datapoint is a hyperparameter, and tune millions of regularization hyperparameters.

Jonathan Lorraine, Paul Vicol, David Duvenaud
Artificial Intelligence and Statistics, 2020.
paper | slides | bibtex
Link Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

We show that you can reinterpret standard classification architectures as energy-based generative models and train them as such. Doing this allows us to achieve state-of-the-art performance at both generative and discriminative modeling in a single model. Adding this energy-based training also improves calibration, out-of-distribution detection, and adversarial robustness.

Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
International Conference on Learning Representations, 2020.
paper | code | video poster | bibtex
Link SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models. In an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator. We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost.

Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky Tian Qi Chen
International Conference on Learning Representations, 2020.
paper | video poster | bibtex
Link Neural Networks with Cheap Differential Operators

We introduce a family of restricted neural network architectures that allow efficient computation of a family of differential operators involving dimension-wise derivatives, such as the divergence. Our proposed architecture has a Jacobian matrix composed of diagonal and hollow (zero-diagonal) components. We demonstrate these cheap differential operators on root-finding problems, exact density evaluation for continuous normalizing flows, and evaluating the Fokker-Planck equation.

Ricky Tian Qi Chen and David Duvenaud
Neural Information Processing Systems, 2019.
paper | slides | bibtex
Link Efficient Graph Generation with Graph Recurrent Attention Networks

We propose a new family of efficient and expressive deep generative models of graphs. We use graph neural networks to generate new edges conditioned on the already-sampled parts of the graph, reducing dependence on node ordering and bypasses the bottleneck caused by the sequential nature of RNNs. We achieve state-of-the-art time efficiency and sample quality compared to previous models, and generate graphs of up to 5000 nodes.

Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, Rich Zemel
Neural Information Processing Systems, 2019.
paper | code | bibtex
Link Latent ODEs for Irregularly-Sampled Time Series

Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks. We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations. These models can naturally handle arbitrary time gaps between observations, and can explicitly model the probability of observation times using Poisson processes.

Yulia Rubanova, Ricky Tian Qi Chen, David Duvenaud
Neural Information Processing Systems, 2019.
paper | code | bibtex
Link to residual flows paper Residual Flows for Invertible Generative Modeling

Invertible residual networks provide transformations where only Lipschitz conditions rather than architectural constraints are needed for enforcing invertibility. We give a tractable unbiased estimate of the log density, and improve these models in other ways. The resulting approach, called Residual Flows, achieves state-of-the-art performance on density estimation amongst flow-based models.

Ricky Tian Qi Chen, Jens Behrmann, David Duvenaud, Jörn-Henrik Jacobsen
Neural Information Processing Systems, 2019.
paper | code | slides | bibtex
Link to hyperOpt2019 paper Invertible Residual Networks

We show that standard ResNet architectures can be made invertible, allowing the same model to be used for classification, density estimation, and generation. Our approach only requires adding a simple normalization step during training. Invertible ResNets define a generative model which can be trained by maximum likelihood on unlabeled data. To compute likelihoods, we introduce a tractable approximation to the Jacobian log-determinant of a residual block. Our empirical evaluation shows that invertible ResNets perform competitively with both stateof-the-art image classifiers and flow-based generative models, something that has not been previously achieved with a single architecture.

Jens Behrmann, Will Grathwohl, Ricky Tian Qi Chen, David Duvenaud, Jörn-Henrik Jacobsen
International Conference on Machine Learning, 2019.
paper | code | slides | bibtex
Link to hyperOpt2019 paper Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions

Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases. We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer.

Matthew MacKay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse
International Conference on Learning Representations, 2019.
paper | code | slides | bibtex
Link to FFJORD paper FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Training normalized generative models such as Real NVP or Glow requires restricting their architectures to allow cheap computation of Jacobian determinants. Alternatively, if the transformation is specified by an ordinary differential equation, then the Jacobian's trace can be used. We use Hutchinson's trace estimator to give a scalable unbiased estimate of the log-density. The result is a continuous-time invertible generative model with unbiased density estimation and one-pass sampling, while allowing unrestricted neural network architectures. We demonstrate our approach on high-dimensional density estimation, image generation, and variational inference, improving the state-of-the-art among exact likelihood methods with efficient sampling.

Will Grathwohl, Ricky Tian Qi Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
International Conference on Learning Representations, 2019. Oral presentation.
paper | slides | code | bibtex
Link to counterfactual paper Explaining Image Classifiers by Counterfactual Generation

When an image classifier makes a prediction, which parts of the image are relevant and why? We can rephrase this question to ask: which parts of the image, if they were not seen by the classifier, would most change its decision? Producing an answer requires marginalizing over images that could have been seen but weren't. We can sample plausible image in-fills by conditioning a generative model on the rest of the image. We then optimize to find the image regions that most change the classifier's decision after in-fill. Our approach contrasts with ad-hoc in-filling approaches, such as blurring or injecting noise, which generate inputs far from the data distribution, and ignore informative relationships between different parts of the image. Our method produces more compact and relevant saliency maps, with fewer artifacts compared to previous methods.

Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
International Conference on Learning Representations, 2019.
paper | code | slides | bibtex
Link to hyperOpt2017 paper Stochastic Hyperparameter Optimization Through Hypernetworks

Models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters. We collapse this nested optimization into joint stochastic optimization of weights and hyperparameters. Our method trains a neural net to output approximately optimal weights as a function of hyperparameters. This method converges to locally optimal weights and hyperparameters for sufficiently large hypernetworks. We compare this method to standard hyperparameter optimization strategies and demonstrate its effectiveness for tuning thousands of hyperparameters.

Jonathan Lorraine, David Duvenaud
paper | bibtex | slides | code
Link to ODE paper Neural Ordinary Differential Equations

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.

Ricky Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
Neural Information Processing Systems, 2018. Best paper award.
paper | bibtex | slides | talk | code
Isolating Sources of Disentanglement in Variational Autoencoders

Variational autoencoders can be regularized to produce disentangled representations, in which each latent dimension has a distinct meaning. However, existing regularization schemes also hurt the model's ability to model the data. We show a simple method to regularize only the part that causes disentanglement. We also give a principled, classifier-free measure of disentanglement called the mutual information gap.

Ricky Tian Qi Chen, Xuechen Li, Roger Grosse, David Duvenaud
Neural Information Processing Systems, 2018. Oral presentation.
paper | bibtex | slides | code
Noisy Natural Gradient as Variational Inference

Bayesian neural nets combine the flexibility of deep learning with uncertainty estimation, but are usually approximated using a fully-factorized Guassian. We show that natural gradient ascent with adaptive weight noise implicitly fits a variational Gassuain posterior. This insight allows us to train full-covariance, fully factorized, or matrix-variate Gaussian variational posteriors using noisy versions of natural gradient, Adam, and K-FAC, respectively, allowing us to scale to modern-size convnets. Our noisy K-FAC algorithm makes better predictions and has better-calibrated uncertainty than existing methods. This leads to more efficient exploration in active learning and reinforcement learning.

Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
International Conference on Machine Learning, 2018
paper | bibtex | video | code
Inference Suboptimality in Variational Autoencoders

Amortized inference allows latent-variable models to scale to large datasets. The quality of approximate inference is determined by two factors: a) the capacity of the variational distribution to match the true posterior and b) the ability of the recognition net to produce good variational parameters for each datapoint. We show that the recognition net giving bad variational parameters is often a bigger problem than using a Gaussian approximate posterior, because the generator can adapt to it.
Chris Cremer, Xuechen Li, David Duvenaud
International Conference on Machine Learning, 2018
paper | bibtex | slides

link to pdf Backpropagation through the Void: Optimizing control variates for black-box gradient estimation

We learn low-variance, unbiased gradient estimators for any function of random variables. We backprop through a neural net surrogate of the original function, which is optimized to minimize gradient variance during the optimization of the original objective. We train discrete latent-variable models, and do continuous and discrete reinforcement learning with an adaptive, action-conditional baseline.

Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud
International Conference on Learning Representations, 2018
paper | code | slides | bibtex
link to pdf Automatic chemical design using a data-driven continuous representation of molecules

We develop a molecular autoencoder, which converts discrete representations of molecules to and from a continuous representation. This allows gradient-based optimization through the space of chemical compounds. Continuous representations also let us generate novel chemicals by interpolating between molecules.

Rafa Gómez-Bombarelli, Jennifer Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Sheberla, Dennis, Jorge Aguilera-Iparraguirre, Timothy Hirzel, Ryan P. Adams, Alán Aspuru-Guzik
American Chemical Society Central Science, 2018
paper | bibtex | slides | code
link to pdf Sticking the landing: Simple, lower-variance gradient estimators for variational inference

We give a simple recipe for reducing the variance of the gradient of the variational evidence lower bound. The entire trick is just removing one term from the gradient. Removing this term leaves an unbiased gradient estimator whose variance approaches zero as the approximate posterior approaches the exact posterior. We also generalize this trick to mixtures and importance-weighted posteriors.

Geoff Roeder, Yuhuai Wu, David Duvenaud
Neural Information Processing Systems, 2017
paper | bibtex | code
link to pdf Reinterpreting importance-weighted autoencoders

The standard interpretation of importance-weighted autoencoders is that they maximize a tighter, multi-sample lower bound than the standard evidence lower bound. We give an alternate interpretation: it optimizes the standard lower bound, but using a more complex distribution, which we show how to visualize.

Chris Cremer, Quaid Morris, David Duvenaud
ICLR Workshop track, 2017
paper | bibtex
link to pdf Composing graphical models with neural networks for structured representations and fast inference

We propose a general modeling and inference framework that combines the complementary strengths of probabilistic graphical models and deep learning methods. Our model family composes latent graphical models with neural network observation likelihoods. All components are trained simultaneously. We use this framework to automatically segment and categorize mouse behavior from raw depth video.

Matthew Johnson, David Duvenaud, Alex Wiltschko, Bob Datta, Ryan P. Adams
Neural Information Processing Systems, 2016
paper | video | code | slides | bibtex | animation
link to pdf Probing the Compositionality of Intuitive Functions

How do people learn about complex functional structure? We propose that humans use compositionality: complex structure is decomposed into simpler building blocks. We formalize this idea using a grammar over Gaussian process kernels. We show that people prefer compositional extrapolations, and argue that this is consistent with broad principles of human cognition.

Eric Shulz, Joshua B. Tenenbaum, David Duvenaud, Maarten Speekenbrink, Samuel J. Gershman
Neural Information Processing Systems, 2016
paper | video | bibtex
link to pdf Black-box stochastic variational inference in five lines of Python

We emphasize how easy it is to construct scalable inference methods using only automatic differentiation. We present code that computes stochastic gradients of the evidence lower bound for any differentiable posterior. For example, we do stochastic variational inference in a deep Bayesian neural network.

David Duvenaud, Ryan P. Adams
NIPS Workshop on Black-box Learning and Inference, 2015
paper | code | bibtex | video
link to pdf Autograd: Reverse-mode differentiation of native Python

Autograd automatically differentiates native Python and Numpy code. It can handle loops, ifs, recursion and closures, and it can even take derivatives of its own derivatives. It uses reverse-mode differentiation (a.k.a. backpropagation), which means it's efficient for gradient-based optimization. Check out the tutorial and the examples directory.

Dougal Maclaurin, David Duvenaud, Matthew Johnson
code | bibtex | slides
link to pdf Early Stopping as Nonparametric Variational Inference

Stochastic gradient descent samples from a nonparametric distribution, implicitly defined by the transformation of the initial distribution by an optimizer. We track the loss of entropy during optimization to get a scalable estimate of the marginal likelihood. This Bayesian interpretation of SGD gives a theoretical foundation for popular tricks such as early stopping and ensembling. We evaluate our marginal likelihood estimator on neural network models.

David Duvenaud, Dougal Maclaurin, Ryan P. Adams
Artificial Intelligence and Statistics, 2016
paper | slides | code | bibtex
link to pdf Convolutional Networks on Graphs for Learning Molecular Fingerprints

We introduce a convolutional neural network that operates directly on graphs, allowing end-to-end learning of the entire feature pipeline. This architecture generalizes standard molecular fingerprints. These data-driven features are more interpretable, and have better predictive performance on a variety of tasks. Related work led to our Nature Materials paper.

David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafa Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
Neural Information Processing Systems, 2015
pdf | slides | code | bibtex
link to pdf Gradient-based Hyperparameter Optimization through Reversible Learning

Tuning hyperparameters of learning algorithms is hard because gradients are usually unavailable. We compute exact gradients of the validation loss with respect to all hyperparameters by differentiating through the entire training procedure. This lets us optimize thousands of hyperparameters, including step-size and momentum schedules, weight initialization distributions, richly parameterized regularization schemes, and neural net architectures.

Dougal Maclaurin, David Duvenaud, Ryan P. Adams
International Conference on Machine Learning, 2015
pdf | slides | code | bibtex
link to pdf Probabilistic ODE Solvers with Runge-Kutta Means

We show that some standard differential equation solvers are equivalent to Gaussian process predictive means, giving them a natural way to handle uncertainty. This work is part of the larger probabilistic numerics research agenda, which interprets numerical algorithms as inference procedures so they can be better understood and extended.

Michael Schober, David Duvenaud, Philipp Hennig
Neural Information Processing Systems, 2014. Oral presentation.
pdf | slides | bibtex
Testing MCMC Code

When can we trust our experiments? We've collected some simple sanity checks that catch a wide class of bugs. Related: Richard Mann wrote a gripping blog post about the aftermath of finding a subtle bug in one of his landmark papers.

Roger Grosse, David Duvenaud
NIPS Workshop on Software Engineering for Machine Learning, 2014
link to pdf PhD Thesis: Automatic Model Construction with Gaussian Processes pdf | code | bibtex | kernel cookbook
link to pdf Automatic Construction and Natural-Language Description of Nonparametric Regression Models

We wrote a program which automatically writes reports summarizing automatically constructed models. A prototype for the automatic statistician project.

James Robert Lloyd, David Duvenaud, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
Association for the Advancement of Artificial Intelligence (AAAI), 2014
pdf | code | slides | example report - airline | example report - solar | more examples | bibtex
link to pdf Avoiding Pathologies in Very Deep Networks

To suggest better neural network architectures, we analyze the properties of different priors on compositions of functions. We study deep Gaussian processes, a type of infinitely-wide, deep neural net. We also examine infinitely deep covariance functions. Finally, we show that you get additive covariance if you do dropout on Gaussian processes.

David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
Artificial Intelligence and Statistics, 2014
pdf | code | slides | video of 50-layer warping | video of 50-layer density | bibtex
link to pdf Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces

To optimize the overall architecture of a neural network along with its hyperparameters, we must be able to relate the performance of nets having differing numbers of hyperparameters. To address this problem, we define a new kernel for conditional parameter spaces that explicitly includes information about which parameters are relevant in a given structure.

Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, Michael Osborne
NIPS workshop on Bayesian optimization, 2013
pdf | code | bibtex
link to pdf Warped Mixtures for Nonparametric Cluster Shapes

If you fit a mixture of Gaussians to a single cluster that is curved or heavy-tailed, your model will report that the data contains many clusters! To fix this problem, we warp a latent mixture of Gaussians into nonparametric cluster shapes. The low-dimensional latent mixture model summarizes the properties of the high-dimensional density manifolds describing the data.

Tomoharu Iwata, David Duvenaud, Zoubin Ghahramani
Uncertainty in Artificial Intelligence, 2013
pdf | code | slides | talk | bibtex
link to arxiv Structure Discovery in Nonparametric Regression through Compositional Kernel Search

How could an AI do statistics? To search through an open-ended class of structured, nonparametric regression models, we introduce a simple grammar which specifies composite kernels. These structured models often allow an interpretable decomposition of the function being modeled, as well as long-range extrapolation. Many common regression methods are special cases of this large family of models.

David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
International Conference on Machine Learning, 2013
pdf | code | short slides | long slides | bibtex
link to video HarlMCMC Shake

Two short animations illustrate the differences between a Metropolis-Hastings (MH) sampler and a Hamiltonian Monte Carlo (HMC) sampler, to the tune of the Harlem shake. This inspired several followup videos - benchmark your MCMC algorithm on these distributions!

by Tamara Broderick and David Duvenaud
video | code
link to pdf Active Learning of Model Evidence using Bayesian Quadrature

Instead of the usual Monte-Carlo based methods for computing integrals of likelihood functions, we instead construct a surrogate model of the likelihood function, and infer its integral conditioned on a set of evaluations. This allows us to evaluate the likelihood wherever is most informative, instead of running a Markov chain. This means fewer evaluations to estimate integrals.

Michael Osborne, David Duvenaud, Roman Garnett, Carl Rasmussen, Stephen Roberts, Zoubin Ghahramani
Neural Information Processing Systems, 2012
pdf | code | slides | related talk | bibtex
link to pdf Optimally-Weighted Herding is Bayesian Quadrature

We prove several connections between a numerical integration method that minimizes a worst-case bound (herding), and a model-based way of estimating integrals (Bayesian quadrature). It turns out that both optimize the same criterion, and that Bayesian Quadrature does this optimally.

Ferenc Huszár and David Duvenaud
Uncertainty in Artificial Intelligence, 2012. Oral presentation.
pdf | code | slides | talk | bibtex
link to pdf Additive Gaussian Processes

When functions have additive structure, we can extrapolate further than with standard Gaussian process models. We show how to efficiently integrate over exponentially-many ways of modeling a function as a sum of low-dimensional functions.

David Duvenaud, Hannes Nickisch, Carl Rasmussen
Neural Information Processing Systems, 2011
pdf | code | slides | bibtex
link to pdf Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification

How can we take advantage of images labeled only by what objects they contain? By combining information across different scales, we use image-level labels (such as this image contains a cat) to infer what different classes of objects look like at the pixel-level, and where they occur in images. This work formed my M.Sc. thesis at UBC.

David Duvenaud, Benjamin Marlin, Kevin Murphy
Canadian Conference on Computer and Robot Vision, 2011
pdf | code | slides | bibtex