Ryan P. Adams

According to our database, Ryan P. Adams authored at least 114 papers between 2008 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Learning to Learn Functions.

Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths

Cogn. Sci., April, 2023

Generative Marginalization Models.

Sulin Liu, Peter J. Ramadge, Ryan P. Adams

CoRR, 2023

A rapid and automated computational approach to the design of multistable soft actuators.

Mehran Mirramezani, Deniz Oktay, Ryan P. Adams

CoRR, 2023

JAX FDM: A differentiable solver for inverse form-finding.

Rafael Pastrana, Deniz Oktay, Ryan P. Adams, Sigrid Adriaenssens

CoRR, 2023

Representing and Learning Functions Invariant Under Crystallographic Groups.

Ryan P. Adams, Peter Orbanz

CoRR, 2023

More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites.

Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz

Proceedings of the 8th ACM Symposium on Computational Fabrication, 2023

Gradient-Based Dovetail Joint Shape Optimization for Stiffness.

Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz

Proceedings of the 8th ACM Symposium on Computational Fabrication, 2023

Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity.

Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh.

Tian Qin, Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams

CoRR, 2022

Multi-fidelity Monte Carlo: a pseudo-marginal approach.

Diana Cai, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Vitruvion: A Generative Model of Parametric CAD Sketches.

Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Bayesian reaction optimization as a tool for chemical synthesis.

Benjamin J. Shields, Jason M. Stevens, Jun Li, Marvin Parasram, Farhan N. Damani, Jesus I. Martinez Alvarado, Jacob M. Janey, Ryan P. Adams, Abigail G. Doyle

Nat., 2021

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions.

Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge

CoRR, 2021

Active multi-fidelity Bayesian online changepoint detection.

Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Slice Sampling Reparameterization Gradients.

David M. Zoltowski, Diana Cai, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate.

Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability.

Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Randomized Automatic Differentiation.

Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design.

Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams

CoRR, 2020

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters.

Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Composable Energy Surrogates for PDE Order Reduction.

Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

On Warm-Starting Neural Network Training.

Jordan T. Ash, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Amortized Finite Element Analysis for Fast PDE-Constrained Optimization.

Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams

Proceedings of the 37th International Conference on Machine Learning, 2020

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models.

Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

On the Difficulty of Warm-Starting Neural Network Training.

Jordan T. Ash, Ryan P. Adams

CoRR, 2019

A Theoretical Connection Between Statistical Physics and Reinforcement Learning.

Jad Rahme, Ryan P. Adams

CoRR, 2019

Discrete Object Generation with Reversible Inductive Construction.

Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers.

Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Efficient optimization of loops and limits with randomized telescoping sums.

Alex Beatson, Ryan P. Adams

Proceedings of the 36th International Conference on Machine Learning, 2019

Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Motivating the Rules of the Game for Adversarial Example Research.

Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl

CoRR, 2018

Compressibility and Generalization in Large-Scale Deep Learning.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

CoRR, 2018

Approximate Inference for Constructing Astronomical Catalogs from Images.

Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

CoRR, 2018

A Bayesian Nonparametric View on Count-Min Sketch.

Diana Cai, Michael Mitzenmacher, Ryan P. Adams

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

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

Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

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

2017

Reducing Reparameterization Gradient Variance.

Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams

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.

Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

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.

Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams

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

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems.

Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski

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.

Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas

Proc. IEEE, 2016

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

José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

J. Mach. Learn. Res., 2016

Patterns of Scalable Bayesian Inference.

Elaine Angelino, Matthew James Johnson, Ryan P. Adams

Found. Trends Mach. Learn., 2016

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation.

Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton

CoRR, 2016

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

Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik

CoRR, 2016

Bayesian latent structure discovery from multi-neuron recordings.

Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow

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.

Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta

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.

Qian Wan, Ryan P. Adams, Robert D. Howe

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

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

Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams

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

Predictive Entropy Search for Multi-objective Bayesian Optimization.

Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams

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

Early Stopping as Nonparametric Variational Inference.

David Duvenaud, Dougal Maclaurin, Ryan P. Adams

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.

Li-Wei H. Lehman, Ryan P. Adams, Louis Mayaud, George B. Moody, Atul Malhotra, Roger G. Mark, Shamim Nemati

IEEE J. Biomed. Health Informatics, 2015

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

Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh

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

Early Stopping is Nonparametric Variational Inference.

Dougal Maclaurin, David Duvenaud, Ryan P. Adams

CoRR, 2015

Sandwiching the marginal likelihood using bidirectional Monte Carlo.

Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams

CoRR, 2015

Spectral Representations for Convolutional Neural Networks.

Oren Rippel, Jasper Snoek, Ryan P. Adams

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.

Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams

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.

Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams

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.

David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams

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

Scalable Bayesian Optimization Using Deep Neural Networks.

Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams

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

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

Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat

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

Gradient-based Hyperparameter Optimization through Reversible Learning.

Dougal Maclaurin, David Duvenaud, Ryan P. Adams

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

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks.

José Miguel Hernández-Lobato, Ryan P. Adams

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

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints.

José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani

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

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

Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams

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

2014

Parallel MCMC with generalized elliptical slice sampling.

Robert Nishihara, Iain Murray, Ryan P. Adams

J. Mach. Learn. Res., 2014

Freeze-Thaw Bayesian Optimization.

Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

CoRR, 2014

Firefly Monte Carlo: Exact MCMC with Subsets of Data.

Dougal Maclaurin, Ryan P. Adams

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

Bayesian Optimization with Unknown Constraints.

Michael A. Gelbart, Jasper Snoek, Ryan P. Adams

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

Accelerating MCMC via Parallel Predictive Prefetching.

Elaine Angelino, Eddie Kohler, Amos Waterland, Margo I. Seltzer, Ryan P. Adams

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

Trick or treat: putting peer prediction to the test.

Xi Alice Gao, Andrew Mao, Yiling Chen, Ryan Prescott Adams

Proceedings of the ACM Conference on Economics and Computation, 2014

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

Scott W. Linderman, Christopher H. Stock, Ryan P. Adams

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.

Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams

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

Learning Ordered Representations with Nested Dropout.

Oren Rippel, Michael A. Gelbart, Ryan P. Adams

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

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

Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry

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

Discovering Latent Network Structure in Point Process Data.

Scott W. Linderman, Ryan P. Adams

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

Learning the Parameters of Determinantal Point Process Kernels.

Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Benjamin Taskar

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

ASC: automatically scalable computation.

Amos Waterland, Elaine Angelino, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer

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

Avoiding pathologies in very deep networks.

David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

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

2013

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

Jeroen C. Chua, Inmar E. Givoni, Ryan P. Adams, Brendan J. Frey

Proceedings of the Machine Learning for Computer Vision, 2013

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures

Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka

CoRR, 2013

High-Dimensional Probability Estimation with Deep Density Models

Oren Rippel, Ryan Prescott Adams

CoRR, 2013

Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation

Andrew Gordon Wilson, Ryan Prescott Adams

CoRR, 2013

Computational caches.

Amos Waterland, Elaine Angelino, Ekin D. Cubuk, Efthimios Kaxiras, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer

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

Contrastive Learning Using Spectral Methods.

James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan Prescott Adams

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.

Kevin Swersky, Jasper Snoek, Ryan Prescott Adams

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.

Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams

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.

Nils Napp, Ryan Prescott Adams

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

Bootstrap Learning via Modular Concept Discovery.

Eyal Dechter, Jonathan Malmaud, Ryan P. Adams, Joshua B. Tenenbaum

Proceedings of the IJCAI 2013, 2013

Gaussian Process Kernels for Pattern Discovery and Extrapolation.

Andrew Gordon Wilson, Ryan Prescott Adams

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

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

Shamim Nemati, Li-Wei H. Lehman, Ryan P. Adams

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.

Li-Wei H. Lehman, Shamim Nemati, Ryan P. Adams, George B. Moody, Atul Malhotra, Roger G. Mark

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

2012

Randomized Optimum Models for Structured Prediction.

Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel

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

Nonparametric guidance of autoencoder representations using label information.

Jasper Snoek, Ryan P. Adams, Hugo Larochelle

J. Mach. Learn. Res., 2012

On Nonparametric Guidance for Learning Autoencoder Representations.

Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle

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

Fast Exact Inference for Recursive Cardinality Models.

Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey

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

Priors for Diversity in Generative Latent Variable Models.

James Y. Zou, Ryan P. Adams

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.

Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams

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.

Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey

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.

Jasper Snoek, Hugo Larochelle, Ryan P. Adams

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

Training Restricted Boltzmann Machines on Word Observations.

George E. Dahl, Ryan Prescott Adams, Hugo Larochelle

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

Discovering shared cardiovascular dynamics within a patient cohort.

Shamim Nemati, Li-Wei H. Lehman, Ryan P. Adams, Atul Malhotra

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.

Li-Wei H. Lehman, Shamim Nemati, Ryan P. Adams, Roger G. Mark

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

Revisiting uncertainty in graph cut solutions.

Daniel Tarlow, Ryan Prescott Adams

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

Learning structural element patch models with hierarchical palettes.

Jeroen Chua, Inmar E. Givoni, Ryan Prescott Adams, Brendan J. Frey

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

2011

Ranking via Sinkhorn Propagation

Ryan Prescott Adams, Richard S. Zemel

CoRR, 2011

2010

Elliptical slice sampling.

Iain Murray, Ryan Prescott Adams, David J. C. MacKay

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

Learning the Structure of Deep Sparse Graphical Models.

Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghahramani

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

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes.

Ryan Prescott Adams, George E. Dahl, Iain Murray

Proceedings of the UAI 2010, 2010

Slice sampling covariance hyperparameters of latent Gaussian models.

Iain Murray, Ryan Prescott Adams

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.

Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan

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.

Ryan Prescott Adams, Iain Murray, David J. C. MacKay

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

Archipelago: nonparametric Bayesian semi-supervised learning.

Ryan Prescott Adams, Zoubin Ghahramani

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

2008

The Gaussian Process Density Sampler.

Ryan Prescott Adams, Iain Murray, David J. C. MacKay

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

Gaussian process product models for nonparametric nonstationarity.

Ryan Prescott Adams, Oliver Stegle

Proceedings of the Machine Learning, 2008