# Ryan P. Adams

According to our database

Collaborative distances:

^{1}, Ryan P. Adams authored at least 72 papers between 2008 and 2018.Collaborative distances:

## Timeline

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## Bibliography

2018

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models.

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Reducing Reparameterization Gradient Variance.

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference.

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Variational Boosting: Iteratively Refining Posterior Approximations.

Proceedings of the 34th International Conference on Machine Learning, 2017

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Taking the Human Out of the Loop: A Review of Bayesian Optimization.

Proceedings of the IEEE, 2016

A General Framework for Constrained Bayesian Optimization using Information-based Search.

Journal of Machine Learning Research, 2016

Patterns of Scalable Bayesian Inference.

Foundations and Trends in Machine Learning, 2016

Bayesian latent structure discovery from multi-neuron recordings.

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

Variability and predictability in tactile sensing during grasping.

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 2016

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM.

Proceedings of the 33nd International Conference on Machine Learning, 2016

Predictive Entropy Search for Multi-objective Bayesian Optimization.

Proceedings of the 33nd International Conference on Machine Learning, 2016

Early Stopping as Nonparametric Variational Inference.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction.

IEEE J. Biomedical and Health Informatics, 2015

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics.

IEEE Trans. Pattern Anal. Mach. Intell., 2015

Spectral Representations for Convolutional Neural Networks.

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

A Gaussian Process Model of Quasar Spectral Energy Distributions.

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation.

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Convolutional Networks on Graphs for Learning Molecular Fingerprints.

Firefly Monte Carlo: Exact MCMC with Subsets of Data.

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Scalable Bayesian Optimization Using Deep Neural Networks.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Celeste: Variational inference for a generative model of astronomical images.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Gradient-based Hyperparameter Optimization through Reversible Learning.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.

Proceedings of the 32nd International Conference on Machine Learning, 2015

Graph-Sparse LDA: A Topic Model with Structured Sparsity.

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Parallel MCMC with generalized elliptical slice sampling.

Journal of Machine Learning Research, 2014

Firefly Monte Carlo: Exact MCMC with Subsets of Data.

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Bayesian Optimization with Unknown Constraints.

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Accelerating MCMC via Parallel Predictive Prefetching.

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Trick or treat: putting peer prediction to the test.

Proceedings of the ACM Conference on Economics and Computation, 2014

A framework for studying synaptic plasticity with neural spike train data.

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Input Warping for Bayesian Optimization of Non-Stationary Functions.

Proceedings of the 31th International Conference on Machine Learning, 2014

Learning Ordered Representations with Nested Dropout.

Proceedings of the 31th International Conference on Machine Learning, 2014

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball.

Proceedings of the 31th International Conference on Machine Learning, 2014

Discovering Latent Network Structure in Point Process Data.

Proceedings of the 31th International Conference on Machine Learning, 2014

Learning the Parameters of Determinantal Point Process Kernels.

Proceedings of the 31th International Conference on Machine Learning, 2014

ASC: automatically scalable computation.

Proceedings of the Architectural Support for Programming Languages and Operating Systems, 2014

Avoiding pathologies in very deep networks.

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition.

Proceedings of the Machine Learning for Computer Vision, 2013

Computational caches.

Proceedings of the 6th Annual International Systems and Storage Conference, 2013

Contrastive Learning Using Spectral Methods.

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Multi-Task Bayesian Optimization.

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data.

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Message Passing Inference with Chemical Reaction Networks.

Bootstrap Learning via Modular Concept Discovery.

Proceedings of the IJCAI 2013, 2013

Gaussian Process Kernels for Pattern Discovery and Extrapolation.

Proceedings of the 30th International Conference on Machine Learning, 2013

Learning outcome-discriminative dynamics in multivariate physiological cohort time series.

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series.

Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2013

2012

Randomized Optimum Models for Structured Prediction.

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Nonparametric guidance of autoencoder representations using label information.

Journal of Machine Learning Research, 2012

On Nonparametric Guidance for Learning Autoencoder Representations.

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Fast Exact Inference for Recursive Cardinality Models.

Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Priors for Diversity in Generative Latent Variable Models.

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

Cardinality Restricted Boltzmann Machines.

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

Probabilistic n-Choose-k Models for Classification and Ranking.

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

Practical Bayesian Optimization of Machine Learning Algorithms.

Training Restricted Boltzmann Machines on Word Observations.

Proceedings of the 29th International Conference on Machine Learning, 2012

Discovering shared cardiovascular dynamics within a patient cohort.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

Discovering shared dynamics in physiological signals: Application to patient monitoring in ICU.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012

Revisiting uncertainty in graph cut solutions.

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

Learning structural element patch models with hierarchical palettes.

Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012

2010

Elliptical slice sampling.

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Learning the Structure of Deep Sparse Graphical Models.

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes.

Proceedings of the UAI 2010, 2010

Slice sampling covariance hyperparameters of latent Gaussian models.

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Tree-Structured Stick Breaking for Hierarchical Data.

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities.

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Archipelago: nonparametric Bayesian semi-supervised learning.

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008

The Gaussian Process Density Sampler.

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Gaussian process product models for nonparametric nonstationarity.

Proceedings of the Machine Learning, 2008