Gaussian process product models for nonparametric nonstationarity. - Ryan Prescott Adams; Oliver Stegle (ICML, 2008)

The Gaussian Process Density Sampler. - Ryan Prescott Adams; Iain Murray 001; David J. C. MacKay (NIPS, 2008)

Archipelago: nonparametric Bayesian semi-supervised learning. - Ryan Prescott Adams; Zoubin Ghahramani (ICML, 2009)

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. - Ryan Prescott Adams; Iain Murray 001; David J. C. MacKay (ICML, 2009)

Tree-Structured Stick Breaking for Hierarchical Data. - Ryan Prescott Adams; Zoubin Ghahramani; Michael I. Jordan (NIPS, 2010)

Slice sampling covariance hyperparameters of latent Gaussian models. - Iain Murray 001; Ryan Prescott Adams (NIPS, 2010)

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. - Ryan Prescott Adams; George E. Dahl; Iain Murray 001 (UAI, 2010)

Learning the Structure of Deep Sparse Graphical Models. - Ryan Prescott Adams; Hanna M. Wallach; Zoubin Ghahramani (AISTATS, 2010)

Elliptical slice sampling. - Iain Murray 001; Ryan Prescott Adams; David J. C. MacKay (AISTATS, 2010)

Ranking via Sinkhorn Propagation - Ryan Prescott Adams; Richard S. Zemel (CoRR, 2011)

Learning structural element patch models with hierarchical palettes. - Jeroen Chua; Inmar E. Givoni; Ryan Prescott Adams; Brendan J. Frey (CVPR, 2012)

Revisiting uncertainty in graph cut solutions. - Daniel Tarlow; Ryan Prescott Adams (CVPR, 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 (EMBC, 2012)

Discovering shared cardiovascular dynamics within a patient cohort. - Shamim Nemati; Li-Wei H. Lehman; Ryan P. Adams; Atul Malhotra (EMBC, 2012)

Training Restricted Boltzmann Machines on Word Observations. - George E. Dahl; Ryan Prescott Adams; Hugo Larochelle (ICML, 2012)

Practical Bayesian Optimization of Machine Learning Algorithms. - Jasper Snoek; Hugo Larochelle; Ryan P. Adams (NIPS, 2012)

Probabilistic n-Choose-k Models for Classification and Ranking. - Kevin Swersky; Daniel Tarlow; Ryan P. Adams; Richard S. Zemel; Brendan J. Frey (NIPS, 2012)

Cardinality Restricted Boltzmann Machines. - Kevin Swersky; Daniel Tarlow; Ilya Sutskever; Ruslan Salakhutdinov; Richard S. Zemel; Ryan P. Adams (NIPS, 2012)

Priors for Diversity in Generative Latent Variable Models. - James Y. Zou; Ryan P. Adams (NIPS, 2012)

Fast Exact Inference for Recursive Cardinality Models. - Daniel Tarlow; Kevin Swersky; Richard S. Zemel; Ryan Prescott Adams; Brendan J. Frey (UAI, 2012)

On Nonparametric Guidance for Learning Autoencoder Representations. - Jasper Snoek; Ryan Prescott Adams; Hugo Larochelle (AISTATS, 2012)

Nonparametric guidance of autoencoder representations using label information. - Jasper Snoek; Ryan P. Adams; Hugo Larochelle (J. Mach. Learn. Res., 2012)

Randomized Optimum Models for Structured Prediction. - Daniel Tarlow; Ryan Prescott Adams; Richard S. Zemel (AISTATS, 2012)

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 (EMBC, 2013)

Learning outcome-discriminative dynamics in multivariate physiological cohort time series. - Shamim Nemati; Li-Wei H. Lehman; Ryan P. Adams (EMBC, 2013)

Gaussian Process Kernels for Pattern Discovery and Extrapolation. - Andrew Gordon Wilson; Ryan Prescott Adams (ICML (3), 2013)

Bootstrap Learning via Modular Concept Discovery. - Eyal Dechter; Jonathan Malmaud; Ryan P. Adams; Joshua B. Tenenbaum (IJCAI, 2013)

Message Passing Inference with Chemical Reaction Networks. - Nils Napp; Ryan Prescott Adams (NIPS, 2013)

A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. - Jasper Snoek; Richard S. Zemel; Ryan Prescott Adams (NIPS, 2013)

Multi-Task Bayesian Optimization. - Kevin Swersky; Jasper Snoek; Ryan Prescott Adams (NIPS, 2013)

Contrastive Learning Using Spectral Methods. - James Y. Zou; Daniel J. Hsu; David C. Parkes; Ryan Prescott Adams (NIPS, 2013)

Computational caches. - Amos Waterland; Elaine Angelino; Ekin D. Cubuk; Efthimios Kaxiras; Ryan P. Adams; Jonathan Appavoo; Margo I. Seltzer (SYSTOR, 2013)

Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation - Andrew Gordon Wilson; Ryan Prescott Adams (CoRR, 2013)

High-Dimensional Probability Estimation with Deep Density Models - Oren Rippel; Ryan Prescott Adams (CoRR, 2013)

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures - Dan Lovell; Jonathan Malmaud; Ryan P. Adams; Vikash K. Mansinghka (CoRR, 2013)

Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition. - Jeroen C. Chua; Inmar E. Givoni; Ryan P. Adams; Brendan J. Frey (Machine Learning for Computer Vision, 2013)

Avoiding pathologies in very deep networks. - David Duvenaud; Oren Rippel; Ryan P. Adams; Zoubin Ghahramani (AISTATS, 2014)

ASC: automatically scalable computation. - Amos Waterland; Elaine Angelino; Ryan P. Adams; Jonathan Appavoo; Margo I. Seltzer (ASPLOS, 2014)

Learning the Parameters of Determinantal Point Process Kernels. - Raja Hafiz Affandi; Emily B. Fox; Ryan P. Adams; Benjamin Taskar (ICML, 2014)

Discovering Latent Network Structure in Point Process Data. - Scott W. Linderman; Ryan P. Adams (ICML, 2014)

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. - Andrew C. Miller; Luke Bornn; Ryan P. Adams; Kirk Goldsberry (ICML, 2014)

Learning Ordered Representations with Nested Dropout. - Oren Rippel; Michael A. Gelbart; Ryan P. Adams (ICML, 2014)

Input Warping for Bayesian Optimization of Non-Stationary Functions. - Jasper Snoek; Kevin Swersky; Richard S. Zemel; Ryan P. Adams (ICML, 2014)

A framework for studying synaptic plasticity with neural spike train data. - Scott W. Linderman; Christopher H. Stock; Ryan P. Adams (NIPS, 2014)

Trick or treat: putting peer prediction to the test. - Xi Alice Gao; Andrew Mao; Yiling Chen 001; Ryan Prescott Adams (EC, 2014)

Accelerating MCMC via Parallel Predictive Prefetching. - Elaine Angelino; Eddie Kohler; Amos Waterland; Margo I. Seltzer; Ryan P. Adams (UAI, 2014)

Bayesian Optimization with Unknown Constraints. - Michael A. Gelbart; Jasper Snoek; Ryan P. Adams (UAI, 2014)

Freeze-Thaw Bayesian Optimization. - Kevin Swersky; Jasper Snoek; Ryan Prescott Adams (CoRR, 2014)

Parallel MCMC with generalized elliptical slice sampling. - Robert Nishihara; Iain Murray 001; Ryan P. Adams (J. Mach. Learn. Res., 2014)

Graph-Sparse LDA: A Topic Model with Structured Sparsity. - Finale Doshi-Velez; Byron C. Wallace; Ryan P. Adams (AAAI, 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 (ICML, 2015)

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. - José Miguel Hernández-Lobato; Ryan P. Adams (ICML, 2015)

Gradient-based Hyperparameter Optimization through Reversible Learning. - Dougal Maclaurin; David Duvenaud; Ryan P. Adams (ICML, 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 (ICML, 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 (ICML, 2015)

Firefly Monte Carlo: Exact MCMC with Subsets of Data. - Dougal Maclaurin; Ryan Prescott Adams (IJCAI, 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 (NIPS, 2015)

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. - Scott W. Linderman; Matthew J. Johnson 002; Ryan P. Adams (NIPS, 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 (NIPS, 2015)

Spectral Representations for Convolutional Neural Networks. - Oren Rippel; Jasper Snoek; Ryan P. Adams (NIPS, 2015)

Sandwiching the marginal likelihood using bidirectional Monte Carlo. - Roger B. Grosse; Zoubin Ghahramani; Ryan P. Adams (CoRR, 2015)

Early Stopping is Nonparametric Variational Inference. - Dougal Maclaurin; David Duvenaud; Ryan P. Adams (CoRR, 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)

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)

Early Stopping as Nonparametric Variational Inference. - David Duvenaud; Dougal Maclaurin; Ryan P. Adams (AISTATS, 2016)

Predictive Entropy Search for Multi-objective Bayesian Optimization. - Daniel Hernández-Lobato; José Miguel Hernández-Lobato; Amar Shah 001; Ryan P. Adams (ICML, 2016)

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. - Ardavan Saeedi; Matthew D. Hoffman; Matthew J. Johnson 002; Ryan P. Adams (ICML, 2016)

Variability and predictability in tactile sensing during grasping. - Qian Wan 006; Ryan P. Adams; Robert D. Howe (ICRA, 2016)

Composing graphical models with neural networks for structured representations and fast inference. - Matthew J. Johnson 002; David Duvenaud; Alexander B. Wiltschko; Ryan P. Adams; Sandeep R. Datta (NIPS, 2016)

Bayesian latent structure discovery from multi-neuron recordings. - Scott W. Linderman; Ryan P. Adams; Jonathan W. Pillow (NIPS, 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)

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. - Akash Srivastava; James Y. Zou; Ryan P. Adams; Charles Sutton (CoRR, 2016)

Patterns of Scalable Bayesian Inference. - Elaine Angelino; Matthew James Johnson; Ryan P. Adams (Found. Trends Mach. Learn., 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)

Taking the Human Out of the Loop: A Review of Bayesian Optimization. - Bobak Shahriari; Kevin Swersky; Ziyu Wang 001; Ryan P. Adams; Nando de Freitas (Proc. IEEE, 2016)

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. - Scott W. Linderman; Matthew J. Johnson 002; Andrew C. Miller; Ryan P. Adams; David M. Blei; Liam Paninski (AISTATS, 2017)

Variational Boosting: Iteratively Refining Posterior Approximations. - Andrew C. Miller; Nicholas J. Foti; Ryan P. Adams (ICML, 2017)

PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. - Jonathan H. Huggins; Ryan P. Adams; Tamara Broderick (NIPS, 2017)

Reducing Reparameterization Gradient Variance. - Andrew C. Miller; Nick Foti; Alexander D'Amour; Ryan P. Adams (NIPS, 2017)

Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. - Ardavan Saeedi; Matthew D. Hoffman; Stephen J. DiVerdi; Asma Ghandeharioun; Matthew J. Johnson 002; Ryan P. Adams (AISTATS, 2018)

A Bayesian Nonparametric View on Count-Min Sketch. - Diana Cai; Michael Mitzenmacher; Ryan P. Adams (NeurIPS, 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)

Compressibility and Generalization in Large-Scale Deep Learning. - Wenda Zhou; Victor Veitch; Morgane Austern; Ryan P. Adams; Peter Orbanz (CoRR, 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)

Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. - Wenda Zhou; Victor Veitch; Morgane Austern; Ryan P. Adams; Peter Orbanz (ICLR (Poster), 2019)

Efficient optimization of loops and limits with randomized telescoping sums. - Alex Beatson; Ryan P. Adams (ICML, 2019)

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. - Igor Fedorov; Ryan P. Adams; Matthew Mattina; Paul N. Whatmough (NeurIPS, 2019)

Discrete Object Generation with Reversible Inductive Construction. - Ari Seff; Wenda Zhou; Farhan N. Damani; Abigail G. Doyle; Ryan P. Adams (NeurIPS, 2019)

A Theoretical Connection Between Statistical Physics and Reinforcement Learning. - Jad Rahme; Ryan P. Adams (CoRR, 2019)

On the Difficulty of Warm-Starting Neural Network Training. - Jordan T. Ash; Ryan P. Adams (CoRR, 2019)

SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. - Yucen Luo; Alex Beatson; Mohammad Norouzi 002; Jun Zhu 001; David Duvenaud; Ryan P. Adams; Ricky T. Q. Chen (ICLR, 2020)

Amortized Finite Element Analysis for Fast PDE-Constrained Optimization. - Tianju Xue; Alex Beatson; Sigrid Adriaenssens; Ryan P. Adams (ICML, 2020)

On Warm-Starting Neural Network Training. - Jordan T. Ash; Ryan P. Adams (NeurIPS, 2020)

Learning Composable Energy Surrogates for PDE Order Reduction. - Alex Beatson; Jordan T. Ash; Geoffrey Roeder; Tianju Xue; Ryan P. Adams (NeurIPS, 2020)

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. - Sulin Liu; Xingyuan Sun; Peter J. Ramadge; Ryan P. Adams (NeurIPS, 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)

Randomized Automatic Differentiation. - Deniz Oktay; Nick McGreivy; Joshua Aduol; Alex Beatson; Ryan P. Adams (ICLR, 2021)

Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. - Dibya Ghosh; Jad Rahme; Aviral Kumar; Amy Zhang 001; Ryan P. Adams; Sergey Levine (NeurIPS, 2021)

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. - Xingyuan Sun; Tianju Xue; Szymon Rusinkiewicz; Ryan P. Adams (NeurIPS, 2021)

Slice Sampling Reparameterization Gradients. - David M. Zoltowski; Diana Cai; Ryan P. Adams (NeurIPS, 2021)

Active multi-fidelity Bayesian online changepoint detection. - Gregory W. Gundersen; Diana Cai; Chuteng Zhou; Barbara E. Engelhardt; Ryan P. Adams (UAI, 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)

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)

Vitruvion: A Generative Model of Parametric CAD Sketches. - Ari Seff; Wenda Zhou; Nick Richardson; Ryan P. Adams (ICLR, 2022)

Multi-fidelity Monte Carlo: a pseudo-marginal approach. - Diana Cai; Ryan P. Adams (NeurIPS, 2022)

Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh. - Tian Qin; Alex Beatson; Deniz Oktay; Nick McGreivy; Ryan P. Adams (CoRR, 2022)

Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. - Deniz Oktay; Mehran Mirramezani; Eder Medina; Ryan P. Adams (ICLR, 2023)

Gradient-Based Dovetail Joint Shape Optimization for Stiffness. - Xingyuan Sun; Chenyue Cai; Ryan P. Adams; Szymon Rusinkiewicz (SCF, 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 (SCF, 2023)

Representing and Learning Functions Invariant Under Crystallographic Groups. - Ryan P. Adams; Peter Orbanz (CoRR, 2023)

JAX FDM: A differentiable solver for inverse form-finding. - Rafael Pastrana; Deniz Oktay; Ryan P. Adams; Sigrid Adriaenssens (CoRR, 2023)

Learning to Learn Functions. - Michael Y. Li; Fred Callaway; William D. Thompson; Ryan P. Adams; Thomas L. Griffiths 001 (Cogn. Sci., 2023)

Fiber Monte Carlo. - Nick Richardson; Deniz Oktay; Yaniv Ovadia; James C. Bowden; Ryan P. Adams (ICLR, 2024)

Generative Marginalization Models. - Sulin Liu; Peter J. Ramadge; Ryan P. Adams (ICML, 2024)

Real-time design of architectural structures with differentiable simulators and neural networks. - Rafael Pastrana; Eder Medina; Isabel M. de Oliveira; Sigrid Adriaenssens; Ryan P. Adams (CoRR, 2024)

A rapid and automated computational approach to the design of multistable soft actuators. - Mehran Mirramezani; Deniz Oktay; Ryan P. Adams (Comput. Phys. Commun., 2024)

Designing Mechanical Meta-Materials by Learning Equivariant Flows. - Mehran Mirramezani; Anne S. Meeussen; Katia Bertoldi; Peter Orbanz; Ryan P. Adams (ICLR, 2025)

Real-time design of architectural structures with differentiable mechanics and neural networks. - Rafael Pastrana; Eder Medina; Isabel M. de Oliveira; Sigrid Adriaenssens; Ryan P. Adams (ICLR, 2025)

Graph Neural Networks Gone Hogwild. - Olga Solodova; Nick Richardson; Deniz Oktay; Ryan P. Adams (ICLR, 2025)

Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. - Kevin Han Huang; Ni Zhan 001; Elif Ertekin; Peter Orbanz; Ryan P. Adams (CoRR, 2025)

Efficiently Vectorized MCMC on Modern Accelerators. - Hugh Dance; Pierre Glaser; Peter Orbanz; Ryan P. Adams (CoRR, 2025)

Nonlinear Computation with Linear Optics via Source-Position Encoding. - Nick Richardson; Cyrill Bösch; Ryan P. Adams (CoRR, 2025)

Space Group Equivariant Crystal Diffusion. - Rees Chang; Angela Pak; Alex Guerra; Ni Zhan 001; Nick Richardson; Elif Ertekin; Ryan P. Adams (CoRR, 2025)

Local Learning Rules for Out-of-Equilibrium Physical Generative Models. - Cyrill Bösch; Geoffrey Roeder; Marc Serra-Garcia; Ryan P. Adams (CoRR, 2025)