Ryan P. Adams

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Semiparametric Latent Variable Models for Guided Representation

Jasper Snoek, Ryan P. Adams, Hugo Larochelle, 2011 .

Archipelago: nonparametric Bayesian semi-supervised learning

Ryan P. Adams, Zoubin Ghahramani, Zoubin Ghahramani, 2009, ICML '09.

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems

Scott W. Linderman, Ryan P. Adams, Matthew J. Johnson, 2017, AISTATS.

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

Scott W. Linderman, Ryan P. Adams, Matthew J. Johnson, 2015, NIPS.

Bootstrap Learning via Modular Concept Discovery

Joshua B. Tenenbaum, Ryan P. Adams, Jonathan Malmaud, 2013, IJCAI.

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Alán Aspuru-Guzik, Ryan P. Adams, David Duvenaud, 2015, NIPS.

The Gaussian Process Density Sampler

Ryan P. Adams, Iain Murray, David J. C. MacKay, 2008, NIPS.

A Bayesian Nonparametric View on Count-Min Sketch

Ryan P. Adams, Michael Mitzenmacher, Diana Cai, 2018, NeurIPS.

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

Shamim Nemati, Ryan P. Adams, Li-Wei H. Lehman, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Learning Composable Energy Surrogates for PDE Order Reduction pdf

Ryan P. Adams, Alex Beatson, Jordan T. Ash, 2020, NeurIPS.

On Nonparametric Guidance for Learning Autoencoder Representations

Jasper Snoek, Ryan P. Adams, Hugo Larochelle, 2011, AISTATS.

Predictive Entropy Search for Multi-objective Bayesian Optimization

Daniel Hern'andez-Lobato, Ryan P. Adams, Jos'e Miguel Hern'andez-Lobato, 2015, ICML.

Priors for Diversity in Generative Latent Variable Models

Ryan P. Adams, James Y. Zou, James Y. Zou, 2012, NIPS.

Incorporating side information into probabilistic matrix factorization using Gaussian Processes

Ryan P. Adams, Iain Murray, George E. Dahl, 2010 .

ASC: automatically scalable computation

Margo I. Seltzer, Ryan P. Adams, Jonathan Appavoo, 2014, ASPLOS.

Computational caches

Margo I. Seltzer, Ryan P. Adams, Efthimios Kaxiras, 2013, SYSTOR '13.

Practical Bayesian Optimization of Machine Learning Algorithms

Jasper Snoek, Ryan P. Adams, Hugo Larochelle, 2012, NIPS.

Compressibility and Generalization in Large-Scale Deep Learning pdf

Ryan P. Adams, Victor Veitch, Peter Orbanz, 2018, ArXiv.

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

Ryan P. Adams, Wenda Zhou, Ari Seff, 2020, ArXiv.

Bayesian latent structure discovery from multi-neuron recordings

Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow, 2016, NIPS.

Fast Exact Inference for Recursive Cardinality Models

Brendan J. Frey, Ryan P. Adams, Richard S. Zemel, 2012, UAI.

Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters

Ryan P. Adams, Sulin Liu, Peter J. Ramadge, 2020, NeurIPS.

Training Restricted Boltzmann Machines on Word Observations pdf

Ryan P. Adams, Hugo Larochelle, George E. Dahl, 2012, ICML.

Variability and predictability in tactile sensing during grasping

Robert D. Howe, Ryan P. Adams, Qian Wan, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Accelerating MCMC via Parallel Predictive Prefetching

Eddie Kohler, Margo I. Seltzer, Ryan P. Adams, 2014, UAI.

Supervised Learning in Dynamic Bayesian Networks

Shamim Nemati, Ryan P. Adams, S. Nemati, 2014, NIPS 2014.

Elliptical slice sampling

Ryan P. Adams, Iain Murray, David J. C. MacKay, 2009, AISTATS.

Sandwiching the marginal likelihood using bidirectional Monte Carlo pdf

Ryan P. Adams, Zoubin Ghahramani, Roger B. Grosse, 2015, ArXiv.

Trick or treat: putting peer prediction to the test

Ryan P. Adams, Xi Alice Gao, Yiling Chen, 2014 .

Bayesian Optimization with Unknown Constraints pdf

Jasper Snoek, Ryan P. Adams, Michael A. Gelbart, 2014, UAI.

Firefly Monte Carlo: Exact MCMC with Subsets of Data

Ryan P. Adams, Dougal Maclaurin, D. Maclaurin, 2014, UAI.

Nonparametric guidance of autoencoder representations using label information

Jasper Snoek, Ryan P. Adams, Hugo Larochelle, 2012, J. Mach. Learn. Res..

Graph-Sparse LDA: A Topic Model with Structured Sparsity

Ryan P. Adams, Byron C. Wallace, Finale Doshi-Velez, 2014, AAAI.

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

Shamim Nemati, Ryan P. Adams, Li-Wei H. Lehman, 2014, IEEE Journal of Biomedical and Health Informatics.

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

Ryan P. Adams, Matthew Mattina, Paul N. Whatmough, 2019, NeurIPS.

Variational Boosting: Iteratively Refining Posterior Approximations pdf

Ryan P. Adams, Andrew C. Miller, Nicholas J. Foti, 2016, ICML.

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

Ryan P. Adams, Tamara Broderick, Jonathan H. Huggins, 2017, NIPS.

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

Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani, 2015, J. Mach. Learn. Res..

Gaussian process product models for nonparametric nonstationarity

Ryan P. Adams, Oliver Stegle, O. Stegle, 2008, ICML '08.

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

Ryan P. Adams, Iain Murray, George E. Dahl, 2010 .

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

Ryan P. Adams, Charles A. Sutton, Akash Srivastava, 2016, ICML 2016.

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

Ryan P. Adams, Matthew J. Johnson, Matthew D. Hoffman, 2017, AISTATS.

The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Ryan P. Adams, Matthew J. Johnson, Matthew D. Hoffman, 2016, ICML.

Scalable Bayesian Optimization Using Deep Neural Networks

Prabhat, Jasper Snoek, Ryan P. Adams, 2015, ICML.

Recurrent switching linear dynamical systems

Scott W. Linderman, Ryan P. Adams, Matthew J. Johnson, 2016, 1610.08466.

Learning outcome-discriminative dynamics in multivariate physiological cohort time series

Shamim Nemati, Ryan P. Adams, Li-Wei H. Lehman, 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

A Theoretical Connection Between Statistical Physics and Reinforcement Learning pdf

Ryan P. Adams, Jad Rahme, Jad Rahme, 2019, ArXiv.

High-Dimensional Probability Estimation with Deep Density Models pdf

Ryan P. Adams, Oren Rippel, Oren Rippel, 2013, ArXiv.

Discrete Object Generation with Reversible Inductive Construction

Ryan P. Adams, Ari Seff, Farhan N. Damani, 2019, NeurIPS.

On Warm-Starting Neural Network Training

Ryan P. Adams, Jordan T. Ash, J. Ash, 2019, NeurIPS.

Randomized Optimum Models for Structured Prediction

Ryan P. Adams, Richard S. Zemel, Daniel Tarlow, 2012, AISTATS.

Spectral Representations for Convolutional Neural Networks

Jasper Snoek, Ryan P. Adams, Oren Rippel, 2015, NIPS.

Reducing Reparameterization Gradient Variance

Alexander D'Amour, Ryan P. Adams, Andrew C. Miller, 2017, NIPS.

Discovering Implicit Networks from Point Process Data

Scott W. Linderman, Ryan P. Adams, 2013 .

Randomized Automatic Differentiation pdf

Ryan P. Adams, Alex Beatson, Deniz Oktay, 2020, ICLR.

Patterns of Scalable Bayesian Inference

Ryan P. Adams, Matthew J. Johnson, Elaine Angelino, 2016, Found. Trends Mach. Learn..

Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation pdf

Andrew Gordon Wilson, Ryan P. Adams, A. Wilson, 2013, ArXiv.

Estimating the Spectral Density of Large Implicit Matrices

Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, 2018, 1802.03451.

Efficient Optimization of Loops and Limits with Randomized Telescoping Sums pdf

Ryan P. Adams, Alex Beatson, 2019, ICML.

Predicting Electron-Ionization Mass Spectrometry using Neural Networks

D. Sculley, Ryan P. Adams, David Belanger, 2018 .

Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks

Ryan P. Adams, José Miguel Hernández-Lobato, 2015, ICML.

Gradient-based Hyperparameter Optimization through Reversible Learning

Ryan P. Adams, David Duvenaud, Dougal Maclaurin, 2015, ICML.

On the Difficulty of Warm-Starting Neural Network Training pdf

Ryan P. Adams, Jordan T. Ash, 2019, ArXiv.

Tree-Structured Stick Breaking for Hierarchical Data

Michael I. Jordan, Ryan P. Adams, Zoubin Ghahramani, 2010, NIPS.

Multi-Task Bayesian Optimization

Jasper Snoek, Ryan P. Adams, Kevin Swersky, 2013, NIPS.

Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics

Yee Whye Teh, Ryan P. Adams, Emily B. Fox, 2015, IEEE Trans. Pattern Anal. Mach. Intell..

Motivating the Rules of the Game for Adversarial Example Research pdf

Ryan P. Adams, Ian J. Goodfellow, George E. Dahl, 2018, ArXiv.

Gaussian Process Kernels for Pattern Discovery and Extrapolation

Andrew Gordon Wilson, Ryan P. Adams, A. G. Wilson, 2013, ICML.

Learning structural element patch models with hierarchical palettes

Brendan J. Frey, Ryan P. Adams, Inmar E. Givoni, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Freeze-Thaw Bayesian Optimization pdf

Jasper Snoek, Ryan P. Adams, Kevin Swersky, 2014, ArXiv.

Amortized Finite Element Analysis for Fast PDE-Constrained Optimization

Ryan P. Adams, Sigrid Adriaenssens, Tianju Xue, 2020, ICML.

Revisiting uncertainty in graph cut solutions

Ryan P. Adams, Daniel Tarlow, Daniel Tarlow, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

Discovering Latent Network Structure in Point Process Data

Scott W. Linderman, Ryan P. Adams, 2014, ICML.

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes pdf

Ryan P. Adams, Iain Murray, George E. Dahl, 2010, UAI.

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures pdf

Ryan P. Adams, Jonathan Malmaud, Vikash K. Mansinghka, 2013, ArXiv.

Composing graphical models with neural networks for structured representations and fast inference

Ryan P. Adams, Matthew J. Johnson, David Duvenaud, 2016, NIPS.

A Gaussian Process Model of Quasar Spectral Energy Distributions

Prabhat, Albert Wu, Ryan P. Adams, 2015, NIPS.

Toward machine-guided design of proteins

Ryan P. Adams, George M. Church, Gleb Kuznetsov, 2018, bioRxiv.

A framework for studying synaptic plasticity with neural spike train data

Scott W. Linderman, Ryan P. Adams, Christopher H. Stock, 2014, NIPS.

Probabilistic n-Choose-k Models for Classification and Ranking

Brendan J. Frey, Ryan P. Adams, Richard S. Zemel, 2012, NIPS.

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

Nando de Freitas, Ryan P. Adams, Ziyu Wang, 2016, Proceedings of the IEEE.

Predictive Entropy Search for Bayesian Optimization with Unknown Constraints

Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani, 2015, ICML.

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

Shamim Nemati, Ryan P. Adams, Li-Wei H. Lehman, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

Brendan J. Frey, Ryan P. Adams, Inmar E. Givoni, 2013, Machine Learning for Computer Vision.

Input Warping for Bayesian Optimization of Non-Stationary Functions

Jasper Snoek, Ryan P. Adams, Richard S. Zemel, 2014, ICML.

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

Ryan P. Adams, Iain Murray, David J. C. MacKay, 2009, ICML '09.

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

Ryan P. Adams, Mohammad Norouzi, David Duvenaud, 2020, ICLR.

Contrastive Learning Using Spectral Methods

Ryan P. Adams, David C. Parkes, Daniel J. Hsu, 2013, NIPS.

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

Ryan P. Adams, Kirk Goldsberry, Luke Bornn, 2014, ICML.

Learning the Parameters of Determinantal Point Process Kernels

Ben Taskar, Ryan P. Adams, Emily B. Fox, 2014, ICML.

Early Stopping as Nonparametric Variational Inference pdf

Ryan P. Adams, David Duvenaud, Dougal Maclaurin, 2015, AISTATS.

Structured VAEs: Composing Probabilistic Graphical Models and Variational Autoencoders

Ryan P. Adams, Matthew J. Johnson, David Duvenaud, 2016 .

Approximate Inference for Constructing Astronomical Catalogs from Images pdf

Prabhat, Ryan P. Adams, David Schlegel, 2018, The Annals of Applied Statistics.

Parallel MCMC with generalized elliptical slice sampling

Ryan P. Adams, Iain Murray, Robert Nishihara, 2012, J. Mach. Learn. Res..

Learning the Structure of Deep Sparse Graphical Models

Ryan P. Adams, Zoubin Ghahramani, Hanna M. Wallach, 2009, AISTATS.

Slice sampling covariance hyperparameters of latent Gaussian models

Ryan P. Adams, Iain Murray, Iain Murray, 2010, NIPS.

Discovering shared cardiovascular dynamics within a patient cohort

Ryan P. Adams, Shamim Nemati, Li-wei H. Lehman, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

Jasper Snoek, Ryan P. Adams, Richard S. Zemel, 2013, NIPS.

Cardinality Restricted Boltzmann Machines

Ryan P. Adams, Ruslan Salakhutdinov, Richard S. Zemel, 2012, NIPS.

Ranking via Sinkhorn Propagation pdf

Ryan P. Adams, Richard S. Zemel, R. Zemel, 2011, ArXiv.

Learning Ordered Representations with Nested Dropout pdf

Ryan P. Adams, Oren Rippel, Michael A. Gelbart, 2014, ICML.

Scalable Bayesian Inference for Excitatory Point Process Networks

Scott W. Linderman, Ryan P. Adams, 2015, 1507.03228.

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Alán Aspuru-Guzik, Ryan P. Adams, David Duvenaud, 2016, ACS central science.

Bayesian inference for Mat´ ern repulsive processes

David B. Dunson, Vinayak A. Rao, Ryan P. Adams, 2013, 1308.1136.

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

Ryan P. Adams, Victor Veitch, Peter Orbanz, 2018, ICLR.

Message Passing Inference with Chemical Reaction Networks

Ryan P. Adams, Nils Napp, N. Napp, 2013, NIPS.

Avoiding pathologies in very deep networks

Ryan P. Adams, Zoubin Ghahramani, David Duvenaud, 2014, AISTATS.

Celeste: Variational inference for a generative model of astronomical images

Prabhat, Ryan P. Adams, Matthew D. Hoffman, 2015, ICML.

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate pdf

Ryan P. Adams, Tianju Xue, Xingyuan Sun, 2021, ArXiv.

ProBF: Learning Probabilistic Safety Certificates with Barrier Functions pdf

Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge, 2021, ArXiv.

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

Sergey Levine, Ryan P. Adams, Aviral Kumar, 2021, NeurIPS.

Vitruvion: A Generative Model of Parametric CAD Sketches pdf

Ryan P. Adams, Nick Richardson, Ari Seff, 2021, ArXiv.

Active multi-fidelity Bayesian online changepoint detection

Ryan P. Adams, Barbara E. Engelhardt, Diana Cai, 2021, UAI.

Opportunity Cost in Bayesian Optimization

Ryan P. Adams, H. Larochelle, Jasper Snoek, 2011 .

Tree-Structured Stick Breaking Processes for Hierarchical Data

Michael I. Jordan, Ryan P. Adams, Zoubin Ghahramani, 2010, 1006.1062.

Mapping Sub-Second Structure in Mouse Behavior

Ryan P. Adams, Matthew J. Johnson, Alexander B. Wiltschko, 2015, Neuron.

Bayesian Structured Sparsity from Gaussian Fields

Ryan P. Adams, B. Engelhardt, 2014, 1407.2235.

Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach.

Ryan P. Adams, D. Duvenaud, D. Maclaurin, 2016, Nature materials.

DISCOVERING SWITCHING AUTOREGRESSIVE DYNAMICS IN NEURAL SPIKE TRAIN RECORDINGS

Scott W. Linderman, Ryan P. Adams, Matthew J. Johnson, 2015 .

Celeste : Scalable variational inference for a generative model of astronomical images

Prabhat, Ryan P. Adams, Jon D. McAuliffe, 2014 .

Bayesian Online Changepoint Detection

Ryan P. Adams, David J.C. MacKay, Ryan Prescott Adams, 2007, 0710.3742.

Bayesian inference for Matérn repulsive processes

Vinayak A. Rao, Ryan P. Adams, David D. Dunson, 2013 .

On the Difficulty of Warm-Starting Neural Network Training

Ryan P. Adams, J. Ash, 2019, ArXiv.

Design of e cient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach

Ryan P. Adams, D. Duvenaud, D. Maclaurin, 2016 .

Learning Graphical Concepts

Ryan P. Adams, Kevin Ellis, Joshua B. Tenenebaum, 2013 .

Bayesian nonparametric learning of switching dynamics in cohort physiological time series: application in critical care patient monitoring

Shamim Nemati, Ryan P. Adams, Matthew J. Johnson, 2015 .

On Warm-Starting Neural Network Training

Ryan P. Adams, Jordan T. Ash, 2020, NeurIPS.

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Ryan P. Adams, David R. Kelley, Yakir A Reshef, 2017, bioRxiv.

An Introduction to Wishart Matrix Moments

Ryan P. Adams, Bharath K. Sriperumbudur, R. Zadeh, 2018 .

Generalizing Elliptical Slice Sampling for Parallel MCMC

Ryan P. Adams, Robert Nishihara, 2012 .

34.4: Invited Paper: Combinatorial Design of OLED‐Emitting Materials

Ryan P. Adams, Alán Aspuru-Guzik, J. Aguilera-Iparraguirre, 2015 .

Vitruvion: A Generative Model of Parametric CAD Sketches

Ryan P. Adams, Ari Seff, Wenda Zhou, 2021, ICLR.

Opportunity Cost in Bayesian Optimization

Ryan P. Adams, H. Larochelle, Jasper Snoek, 2011 .

Multi-fidelity Monte Carlo: a pseudo-marginal approach

Ryan P. Adams, Diana Cai, 2022, NeurIPS.

Bayesian reaction optimization as a tool for chemical synthesis

Ryan P. Adams, Jason M. Stevens, Farhan N. Damani, 2021, Nature.

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Ryan P. Adams, Luca Pinello, Alexander Gusev, 2017, bioRxiv.

Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate

Ryan P. Adams, S. Rusinkiewicz, Tianju Xue, 2021, NeurIPS.

Nonparametric Bayesian Density Modeling with Gaussian Processes

Ryan P. Adams, Iain Murray, D. MacKay, 2009, 0912.4896.

Rapid Prediction of Electron–Ionization Mass Spectrometry Using Neural Networks

Ryan P. Adams, David Belanger, Jennifer N Wei, 2018, ACS central science.

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization.

Ryan P. Adams, Jason M. Stevens, A. Doyle, 2022, Journal of the American Chemical Society.

Celeste: Variational inference for a generative model of astronomical images

Prabhat, Ryan P. Adams, Andrew C. Miller, 2015, ICML.

Learning to Learn Functions

Ryan P. Adams, T. Griffiths, Frederick Callaway, 2023, Cogn. Sci..

Representing and Learning Functions Invariant Under Crystallographic Groups

Ryan P. Adams, Peter Orbanz, 2023, ArXiv.

Accelerating MCMC via Parallel Predictive Prefetching

Ryan P. Adams, E. Kohler, E. Angelino, 2014, UAI.

Efficient Optimization of Loops and Limits with Randomized Telescoping Sums

Ryan P. Adams, Alex Beatson, 2019, ICML.

Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Ryan P. Adams, David R. Kelley, N. Patterson, 2017, Nature Genetics.

Gradient-based Hyperparameter Optimization through Reversible Learning

Ryan P. Adams, D. Duvenaud, D. Maclaurin, 2015, International Conference on Machine Learning.

Multi-Task Bayesian Optimization

Ryan P. Adams, K. Q. Weinberger, L. Bottou, 2013, NIPS.

Trick or treat: putting peer prediction to the test

Ryan P. Adams, Yiling Chen, Andrew Mao, 2014, EC.