Ryan P. Adams

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 70

Top Venues

Publications

Nick Richardson, Deniz Oktay, Yaniv Ovadia, James C. Bowden, Ryan P. Adams
Fiber Monte Carlo. ICLR (2024)
Mehran Mirramezani, Deniz Oktay, Ryan P. Adams
A rapid and automated computational approach to the design of multistable soft actuators. Comput. Phys. Commun. 298 (2024)
Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
Graph Neural Networks Gone Hogwild. CoRR (2024)
Mehran Mirramezani, Deniz Oktay, Ryan P. Adams
A rapid and automated computational approach to the design of multistable soft actuators. CoRR (2023)
Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz
More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. SCF (2023)
Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths
Learning to Learn Functions. Cogn. Sci. 47 (4) (2023)
Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. ICLR (2023)
Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz
Gradient-Based Dovetail Joint Shape Optimization for Stiffness. CoRR (2023)
Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz
Gradient-Based Dovetail Joint Shape Optimization for Stiffness. SCF (2023)
Rafael Pastrana, Deniz Oktay, Ryan P. Adams, Sigrid Adriaenssens
JAX FDM: A differentiable solver for inverse form-finding. CoRR (2023)
Sulin Liu, Peter J. Ramadge, Ryan P. Adams
Generative Marginalization Models. CoRR (2023)
Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. CoRR (2023)
Ryan P. Adams, Peter Orbanz
Representing and Learning Functions Invariant Under Crystallographic Groups. CoRR (2023)
Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
Vitruvion: A Generative Model of Parametric CAD Sketches. ICLR (2022)
Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz
More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. CoRR (2022)
Tian Qin, Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh. CoRR (2022)
Diana Cai, Ryan P. Adams
Multi-fidelity Monte Carlo: a pseudo-marginal approach. NeurIPS (2022)
Diana Cai, Ryan P. Adams
Multi-fidelity Monte Carlo: a pseudo-marginal approach. CoRR (2022)
Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
Vitruvion: A Generative Model of Parametric CAD Sketches. CoRR (2021)
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. NeurIPS (2021)
David M. Zoltowski, Diana Cai, Ryan P. Adams
Slice Sampling Reparameterization Gradients. NeurIPS (2021)
Benjamin J. Shields, Jason Stevens, Jun Li, Marvin Parasram, Farhan N. Damani, Jesus I. Martinez Alvarado, Jacob M. Janey, Ryan P. Adams, Abigail G. Doyle
Bayesian reaction optimization as a tool for chemical synthesis. Nat. 590 (7844) (2021)
Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams
Active multi-fidelity Bayesian online changepoint detection. UAI (2021)
Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams
Randomized Automatic Differentiation. ICLR (2021)
Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams
Active multi-fidelity Bayesian online changepoint detection. CoRR (2021)
Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions. CoRR (2021)
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. CoRR (2021)
Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. NeurIPS (2021)
Xingyuan Sun, Tianju Xue, Szymon M. Rusinkiewicz, Ryan P. Adams
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. CoRR (2021)
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. CoRR (2020)
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. ICLR (2020)
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
Learning Composable Energy Surrogates for PDE Order Reduction. CoRR (2020)
Jordan T. Ash, Ryan P. Adams
On Warm-Starting Neural Network Training. NeurIPS (2020)
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS (2020)
Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization. ICML (2020)
Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. NeurIPS (2020)
Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams
Randomized Automatic Differentiation. CoRR (2020)
Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams
SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design. CoRR (2020)
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS (2019)
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Discrete Object Generation with Reversible Inductive Construction. CoRR (2019)
Alex Beatson, Ryan P. Adams
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums. CoRR (2019)
Jad Rahme, Ryan P. Adams
A Theoretical Connection Between Statistical Physics and Reinforcement Learning. CoRR (2019)
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams
Discrete Object Generation with Reversible Inductive Construction. NeurIPS (2019)
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR (2019)
Jordan T. Ash, Ryan P. Adams
On the Difficulty of Warm-Starting Neural Network Training. CoRR (2019)
Alex Beatson, Ryan P. Adams
Efficient optimization of loops and limits with randomized telescoping sums. ICML (2019)
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) (2019)
Diana Cai, Michael Mitzenmacher, Ryan P. Adams
A Bayesian Nonparametric View on Count-Min Sketch. NeurIPS (2018)
Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl
Motivating the Rules of the Game for Adversarial Example Research. CoRR (2018)
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS (2018)
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR (2018)
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
Compressibility and Generalization in Large-Scale Deep Learning. CoRR (2018)
Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
Variational Boosting: Iteratively Refining Posterior Approximations. ICML (2017)
Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS (2017)
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS (2017)
Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams
Reducing Reparameterization Gradient Variance. NIPS (2017)
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. CoRR (2017)
Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
Bayesian latent structure discovery from multi-neuron recordings. NIPS (2016)
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani
A General Framework for Constrained Bayesian Optimization using Information-based Search. J. Mach. Learn. Res. 17 (2016)
Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR (2016)
David Duvenaud, Dougal Maclaurin, Ryan P. Adams
Early Stopping as Nonparametric Variational Inference. AISTATS (2016)
Elaine Angelino, Matthew James Johnson, Ryan P. Adams
Patterns of Scalable Bayesian Inference. Found. Trends Mach. Learn. 9 (2-3) (2016)
Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
Variational Boosting: Iteratively Refining Posterior Approximations. CoRR (2016)
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas
Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE 104 (1) (2016)
Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik
Automatic chemical design using a data-driven continuous representation of molecules. CoRR (2016)
Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR (2016)
Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta
Composing graphical models with neural networks for structured representations and fast inference. NIPS (2016)
Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML (2016)
Qian Wan, Ryan P. Adams, Robert D. Howe
Variability and predictability in tactile sensing during grasping. ICRA (2016)
Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams
Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML (2016)
Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams
Scalable Bayesian Optimization Using Deep Neural Networks. ICML (2015)
Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. NIPS (2015)
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
Convolutional Networks on Graphs for Learning Molecular Fingerprints. CoRR (2015)
Li-Wei H. Lehman, Ryan P. Adams, Louis Mayaud, George B. Moody, Atul Malhotra, Roger G. Mark, Shamim Nemati
A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction. IEEE J. Biomed. Health Informatics 19 (3) (2015)
José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. ICML (2015)
Oren Rippel, Jasper Snoek, Ryan P. Adams
Spectral Representations for Convolutional Neural Networks. NIPS (2015)
Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. IEEE Trans. Pattern Anal. Mach. Intell. 37 (2) (2015)
Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
Sandwiching the marginal likelihood using bidirectional Monte Carlo. CoRR (2015)
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
Convolutional Networks on Graphs for Learning Molecular Fingerprints. NIPS (2015)
Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams
Graph-Sparse LDA: A Topic Model with Structured Sparsity. AAAI (2015)
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Gradient-based Hyperparameter Optimization through Reversible Learning. CoRR (2015)
Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams
A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS (2015)
Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat
Celeste: Variational inference for a generative model of astronomical images. ICML (2015)
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Early Stopping is Nonparametric Variational Inference. CoRR (2015)
Oren Rippel, Jasper Snoek, Ryan P. Adams
Spectral Representations for Convolutional Neural Networks. CoRR (2015)
José Miguel Hernández-Lobato, Ryan P. Adams
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. ICML (2015)
Dougal Maclaurin, David Duvenaud, Ryan P. Adams
Gradient-based Hyperparameter Optimization through Reversible Learning. ICML (2015)
Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. ICML (2014)
Scott W. Linderman, Ryan P. Adams
Discovering Latent Network Structure in Point Process Data. ICML (2014)
David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
Avoiding pathologies in very deep networks. AISTATS (2014)
Scott W. Linderman, Ryan P. Adams
Discovering Latent Network Structure in Point Process Data. CoRR (2014)
Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR (2014)
David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
Avoiding pathologies in very deep networks. CoRR (2014)
Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams
Graph-Sparse LDA: A Topic Model with Structured Sparsity. CoRR (2014)
Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML (2014)
Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
A framework for studying synaptic plasticity with neural spike train data. NIPS (2014)
Amos Waterland, Elaine Angelino, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer
ASC: automatically scalable computation. ASPLOS (2014)
Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Ben Taskar
Learning the Parameters of Determinantal Point Process Kernels. CoRR (2014)
Michael A. Gelbart, Jasper Snoek, Ryan P. Adams
Bayesian Optimization with Unknown Constraints. UAI (2014)
Dougal Maclaurin, Ryan P. Adams
Firefly Monte Carlo: Exact MCMC with Subsets of Data. UAI (2014)

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Publication Activity (10 Years)

Top Topics

Top Venues

Ryan P. Adams