David Duvenaud

Affiliations:
  • University of Toronto, Department of Computer Science, Canada
  • Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, USA (former)
  • University of Cambridge, Department of Engineering, UK (PhD)


According to our database1, David Duvenaud authored at least 80 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models.
CoRR, 2024

LLM Processes: Numerical Predictive Distributions Conditioned on Natural Language.
CoRR, 2024

Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.
CoRR, 2024

Experts Don't Cheat: Learning What You Don't Know By Predicting Pairs.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards Understanding Sycophancy in Language Models.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Sorting Out Quantum Monte Carlo.
CoRR, 2023

Towards Understanding Sycophancy in Language Models.
CoRR, 2023

Tools for Verifying Neural Models' Training Data.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Differential Equations and Continuous-Time Deep Learning (Dagstuhl Seminar 22332).
Dagstuhl Reports, 2022

On Implicit Bias in Overparameterized Bilevel Optimization.
Proceedings of the International Conference on Machine Learning, 2022

Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Complex Momentum for Optimization in Games.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming.
Proc. ACM Program. Lang., 2021

Complex Momentum for Learning in Games.
CoRR, 2021

Meta-learning to Improve Pre-training.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions.
Proceedings of the 38th International Conference on Machine Learning, 2021

Teaching with Commentaries.
Proceedings of the 9th International Conference on Learning Representations, 2021

No MCMC for me: Amortized sampling for fast and stable training of energy-based models.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
A Study of Gradient Variance in Deep Learning.
CoRR, 2020

What went wrong and when? Instance-wise Feature Importance for Time-series Models.
CoRR, 2020

Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling.
CoRR, 2020

What went wrong and when? Instance-wise feature importance for time-series black-box models.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Differential Equations that are Easy to Solve.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling.
Proceedings of the 37th International Conference on Machine Learning, 2020

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models.
Proceedings of the 8th International Conference on Learning Representations, 2020

Your classifier is secretly an energy based model and you should treat it like one.
Proceedings of the 8th International Conference on Learning Representations, 2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.
Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

Optimizing Millions of Hyperparameters by Implicit Differentiation.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Scalable Gradients for Stochastic Differential Equations.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Neural Networks with Cheap Differential Operators.
CoRR, 2019

Efficient Graph Generation with Graph Recurrent Attention Networks.
CoRR, 2019

Latent ODEs for Irregularly-Sampled Time Series.
CoRR, 2019

Residual Flows for Invertible Generative Modeling.
CoRR, 2019

Latent-space Dynamics for Reduced Deformable Simulation.
Comput. Graph. Forum, 2019

Latent Ordinary Differential Equations for Irregularly-Sampled Time Series.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient Graph Generation with Graph Recurrent Attention Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Neural Networks with Cheap Differential Operators.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Residual Flows for Invertible Generative Modeling.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Invertible Residual Networks.
Proceedings of the 36th International Conference on Machine Learning, 2019

Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.
Proceedings of the 7th International Conference on Learning Representations, 2019

FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models.
Proceedings of the 7th International Conference on Learning Representations, 2019

Explaining Image Classifiers by Counterfactual Generation.
Proceedings of the 7th International Conference on Learning Representations, 2019

Towards Understanding Linear Word Analogies.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Understanding Undesirable Word Embedding Associations.
Proceedings of the 57th Conference of the Association for Computational Linguistics, 2019

Scalable Gradients and Variational Inference for Stochastic Differential Equations.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018
Invertible Residual Networks.
CoRR, 2018

Stochastic Combinatorial Ensembles for Defending Against Adversarial Examples.
CoRR, 2018

Explaining Image Classifiers by Adaptive Dropout and Generative In-filling.
CoRR, 2018

Scalable Recommender Systems through Recursive Evidence Chains.
CoRR, 2018

Stochastic Hyperparameter Optimization through Hypernetworks.
CoRR, 2018

Neural Ordinary Differential Equations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Isolating Sources of Disentanglement in Variational Autoencoders.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Noisy Natural Gradient as Variational Inference.
Proceedings of the 35th International Conference on Machine Learning, 2018

Inference Suboptimality in Variational Autoencoders.
Proceedings of the 35th International Conference on Machine Learning, 2018

Stochastic Gradient Langevin dynamics that Exploit Neural Network Structure.
Proceedings of the 6th International Conference on Learning Representations, 2018

Backpropagation through the Void: Optimizing control variates for black-box gradient estimation.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Generating and designing DNA with deep generative models.
CoRR, 2017

Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference.
CoRR, 2017

Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Reinterpreting Importance-Weighted Autoencoders.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Automatic chemical design using a data-driven continuous representation of molecules.
CoRR, 2016

Probing the Compositionality of Intuitive Functions.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Composing graphical models with neural networks for structured representations and fast inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

ChordRipple: Recommending Chords to Help Novice Composers Go Beyond the Ordinary.
Proceedings of the 21st International Conference on Intelligent User Interfaces, 2016

Early Stopping as Nonparametric Variational Inference.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Early Stopping is Nonparametric Variational Inference.
CoRR, 2015

Convolutional Networks on Graphs for Learning Molecular Fingerprints.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Gradient-based Hyperparameter Optimization through Reversible Learning.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Testing MCMC code.
CoRR, 2014

Probabilistic ODE Solvers with Runge-Kutta Means.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Active learning of intuitive control knobs for synthesizers using gaussian processes.
Proceedings of the 19th International Conference on Intelligent User Interfaces, 2014

Avoiding pathologies in very deep networks.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Automatic Construction and Natural-Language Description of Nonparametric Regression Models.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

2013
Warped Mixtures for Nonparametric Cluster Shapes.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013

Structure Discovery in Nonparametric Regression through Compositional Kernel Search.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Optimally-Weighted Herding is Bayesian Quadrature.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Active Learning of Model Evidence Using Bayesian Quadrature.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011
Additive Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Multiscale Conditional Random Fields for Semi-supervised Labeling and Classification.
Proceedings of the Canadian Conference on Computer and Robot Vision, 2011

2010
Causal learning without DAGs.
Proceedings of the Causality: Objectives and Assessment (NIPS 2008 Workshop), 2010


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