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ORCIDRyan P. Adams
Ryan Prescott Adams
Person information
- affiliation: Princeton University, Department of Computer Science, Princeton, NJ, USA
- affiliation (former): Google LLC, Mountain View, CA, USA
- affiliation (former): Twitter, San Francisco, CA, USA
- affiliation (former): Harvard University, School of Engineering and Applied Sciences, Cambridge, MA, USA
- affiliation (former): University of Cambridge, Cavendish Laboratory, Cambridge, UK
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2020 – today
- 2024
[j10]Mehran Mirramezani, Deniz Oktay, Ryan P. Adams:
A rapid and automated computational approach to the design of multistable soft actuators. Comput. Phys. Commun. 298: 109090 (2024)- [c86]Nick Richardson, Deniz Oktay, Yaniv Ovadia, James C. Bowden, Ryan P. Adams:
Fiber Monte Carlo. ICLR 2024 - [c85]Sulin Liu, Peter J. Ramadge, Ryan P. Adams:
Generative Marginalization Models. ICML 2024 - [i57]Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams:
Graph Neural Networks Gone Hogwild. CoRR abs/2407.00494 (2024) - [i56]Rafael Pastrana, Eder Medina, Isabel M. de Oliveira, Sigrid Adriaenssens, Ryan P. Adams:
Real-time design of architectural structures with differentiable simulators and neural networks. CoRR abs/2409.02606 (2024) - [i55]Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams:
Designing Mechanical Meta-Materials by Learning Equivariant Flows. CoRR abs/2410.02385 (2024) - 2023
- [j9]Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths:
Learning to Learn Functions. Cogn. Sci. 47(4) (2023) - [c84]Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams:
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. ICLR 2023 - [c83]Xingyuan Sun
, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz:
Gradient-Based Dovetail Joint Shape Optimization for Stiffness. SCF 2023: 5:1-5:8 - [c82]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: 8:1-8:14 - [i54]Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams:
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. CoRR abs/2302.00032 (2023) - [i53]Ryan P. Adams, Peter Orbanz:
Representing and Learning Functions Invariant Under Crystallographic Groups. CoRR abs/2306.05261 (2023) - [i52]Rafael Pastrana, Deniz Oktay, Ryan P. Adams, Sigrid Adriaenssens:
JAX FDM: A differentiable solver for inverse form-finding. CoRR abs/2307.12407 (2023) - [i51]Mehran Mirramezani, Deniz Oktay, Ryan P. Adams:
A rapid and automated computational approach to the design of multistable soft actuators. CoRR abs/2309.04970 (2023) - [i50]Sulin Liu, Peter J. Ramadge, Ryan P. Adams:
Generative Marginalization Models. CoRR abs/2310.12920 (2023) - [i49]Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz:
Gradient-Based Dovetail Joint Shape Optimization for Stiffness. CoRR abs/2310.19798 (2023) - 2022
- [c81]Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams:
Vitruvion: A Generative Model of Parametric CAD Sketches. ICLR 2022 - [c80]Diana Cai, Ryan P. Adams:
Multi-fidelity Monte Carlo: a pseudo-marginal approach. NeurIPS 2022 - [i48]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 abs/2205.16008 (2022) - [i47]Diana Cai, Ryan P. Adams:
Multi-fidelity Monte Carlo: a pseudo-marginal approach. CoRR abs/2210.01534 (2022) - [i46]Tian Qin, Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams:
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh. CoRR abs/2211.01604 (2022) - 2021
- [j8]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:
Bayesian reaction optimization as a tool for chemical synthesis. Nat. 590(7844): 89-96 (2021) - [c79]Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams:
Randomized Automatic Differentiation. ICLR 2021 - [c78]Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams:
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. NeurIPS 2021: 18891-18906 - [c77]David M. Zoltowski, Diana Cai, Ryan P. Adams:
Slice Sampling Reparameterization Gradients. NeurIPS 2021: 23532-23544 - [c76]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: 25502-25515 - [c75]Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams:
Active multi-fidelity Bayesian online changepoint detection. UAI 2021: 1916-1926 - [i45]Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams:
Active multi-fidelity Bayesian online changepoint detection. CoRR abs/2103.14224 (2021) - [i44]Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams:
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. CoRR abs/2106.09019 (2021) - [i43]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 abs/2107.06277 (2021) - [i42]Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams:
Vitruvion: A Generative Model of Parametric CAD Sketches. CoRR abs/2109.14124 (2021) - [i41]Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge:
ProBF: Learning Probabilistic Safety Certificates with Barrier Functions. CoRR abs/2112.12210 (2021) - 2020
- [c74]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 - [c73]Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams:
Amortized Finite Element Analysis for Fast PDE-Constrained Optimization. ICML 2020: 10638-10647 - [c72]Jordan T. Ash, Ryan P. Adams:
On Warm-Starting Neural Network Training. NeurIPS 2020 - [c71]Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams:
Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS 2020 - [c70]Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams:
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. NeurIPS 2020 - [e1]Ryan P. Adams, Vibhav Gogate:
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020. Proceedings of Machine Learning Research 124, AUAI Press 2020 [contents] - [i40]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 abs/2004.00353 (2020) - [i39]Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams:
Learning Composable Energy Surrogates for PDE Order Reduction. CoRR abs/2005.06549 (2020) - [i38]Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams:
SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design. CoRR abs/2007.08506 (2020) - [i37]Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams:
Randomized Automatic Differentiation. CoRR abs/2007.10412 (2020)
2010 – 2019
- 2019
- [c69]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 - [c68]Alex Beatson, Ryan P. Adams:
Efficient optimization of loops and limits with randomized telescoping sums. ICML 2019: 534-543 - [c67]Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS 2019: 4978-4990 - [c66]Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams:
Discrete Object Generation with Reversible Inductive Construction. NeurIPS 2019: 10353-10363 - [i36]Alex Beatson, Ryan P. Adams:
Efficient Optimization of Loops and Limits with Randomized Telescoping Sums. CoRR abs/1905.07006 (2019) - [i35]Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough:
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR abs/1905.12107 (2019) - [i34]Jad Rahme, Ryan P. Adams:
A Theoretical Connection Between Statistical Physics and Reinforcement Learning. CoRR abs/1906.10228 (2019) - [i33]Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams:
Discrete Object Generation with Reversible Inductive Construction. CoRR abs/1907.08268 (2019) - [i32]Jordan T. Ash, Ryan P. Adams:
On the Difficulty of Warm-Starting Neural Network Training. CoRR abs/1910.08475 (2019) - 2018
- [c65]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: 1309-1317 - [c64]Diana Cai, Michael Mitzenmacher, Ryan P. Adams:
A Bayesian Nonparametric View on Count-Min Sketch. NeurIPS 2018: 8782-8791 - [i31]Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat:
Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR abs/1803.00113 (2018) - [i30]Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Compressibility and Generalization in Large-Scale Deep Learning. CoRR abs/1804.05862 (2018) - [i29]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 abs/1807.06732 (2018) - 2017
- [c63]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: 914-922 - [c62]Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams:
Variational Boosting: Iteratively Refining Posterior Approximations. ICML 2017: 2420-2429 - [c61]Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick:
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS 2017: 3611-3621 - [c60]Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams:
Reducing Reparameterization Gradient Variance. NIPS 2017: 3708-3718 - [i28]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 abs/1704.04997 (2017) - 2016
- [j7]Elaine Angelino, Matthew James Johnson, Ryan P. Adams:
Patterns of Scalable Bayesian Inference. Found. Trends Mach. Learn. 9(2-3): 119-247 (2016) - [j6]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: 160:1-160:53 (2016) - [j5]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): 148-175 (2016) - [c59]David Duvenaud, Dougal Maclaurin, Ryan P. Adams:
Early Stopping as Nonparametric Variational Inference. AISTATS 2016: 1070-1077 - [c58]Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams:
Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML 2016: 1492-1501 - [c57]Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams:
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML 2016: 2682-2691 - [c56]Qian Wan, Ryan P. Adams, Robert D. Howe:
Variability and predictability in tactile sensing during grasping. ICRA 2016: 158-164 - [c55]Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow:
Bayesian latent structure discovery from multi-neuron recordings. NIPS 2016: 2002-2010 - [c54]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: 2946-2954 - [i27]Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton:
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR abs/1602.06886 (2016) - [i26]Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton:
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR abs/1606.05896 (2016) - [i25]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 abs/1610.02415 (2016) - [i24]Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams:
Variational Boosting: Iteratively Refining Posterior Approximations. CoRR abs/1611.06585 (2016) - 2015
- [j4]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): 209-211 (2015) - [j3]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): 1068-1076 (2015) - [c53]Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams:
Graph-Sparse LDA: A Topic Model with Structured Sparsity. AAAI 2015: 2575-2581 - [c52]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: 1699-1707 - [c51]José Miguel Hernández-Lobato, Ryan P. Adams:
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. ICML 2015: 1861-1869 - [c50]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: 2095-2103 - [c49]Dougal Maclaurin, David Duvenaud, Ryan P. Adams:
Gradient-based Hyperparameter Optimization through Reversible Learning. ICML 2015: 2113-2122 - [c48]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: 2171-2180 - [c47]Dougal Maclaurin, Ryan Prescott Adams:
Firefly Monte Carlo: Exact MCMC with Subsets of Data. IJCAI 2015: 4289-4295 - [c46]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: 2224-2232 - [c45]Oren Rippel, Jasper Snoek, Ryan P. Adams:
Spectral Representations for Convolutional Neural Networks. NIPS 2015: 2449-2457 - [c44]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: 2494-2502 - [c43]Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams:
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. NIPS 2015: 3456-3464 - [i23]Dougal Maclaurin, David Duvenaud, Ryan P. Adams:
Gradient-based Hyperparameter Optimization through Reversible Learning. CoRR abs/1502.03492 (2015) - [i22]Dougal Maclaurin, David Duvenaud, Ryan P. Adams:
Early Stopping is Nonparametric Variational Inference. CoRR abs/1504.01344 (2015) - [i21]Oren Rippel, Jasper Snoek, Ryan P. Adams:
Spectral Representations for Convolutional Neural Networks. CoRR abs/1506.03767 (2015) - [i20]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 abs/1509.09292 (2015) - [i19]Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams:
Sandwiching the marginal likelihood using bidirectional Monte Carlo. CoRR abs/1511.02543 (2015) - 2014
- [j2]Robert Nishihara, Iain Murray, Ryan P. Adams:
Parallel MCMC with generalized elliptical slice sampling. J. Mach. Learn. Res. 15(1): 2087-2112 (2014) - [c42]David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani:
Avoiding pathologies in very deep networks. AISTATS 2014: 202-210 - [c41]Amos Waterland, Elaine Angelino, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer:
ASC: automatically scalable computation. ASPLOS 2014: 575-590 - [c40]Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry:
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. ICML 2014: 235-243 - [c39]Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Benjamin Taskar:
Learning the Parameters of Determinantal Point Process Kernels. ICML 2014: 1224-1232 - [c38]Scott W. Linderman, Ryan P. Adams:
Discovering Latent Network Structure in Point Process Data. ICML 2014: 1413-1421 - [c37]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML 2014: 1674-1682 - [c36]Oren Rippel, Michael A. Gelbart, Ryan P. Adams:
Learning Ordered Representations with Nested Dropout. ICML 2014: 1746-1754 - [c35]Scott W. Linderman, Christopher H. Stock, Ryan P. Adams:
A framework for studying synaptic plasticity with neural spike train data. NIPS 2014: 2330-2338 - [c34]Xi Alice Gao, Andrew Mao, Yiling Chen, Ryan Prescott Adams:
Trick or treat: putting peer prediction to the test. EC 2014: 507-524 - [c33]Elaine Angelino, Eddie Kohler, Amos Waterland, Margo I. Seltzer, Ryan P. Adams:
Accelerating MCMC via Parallel Predictive Prefetching. UAI 2014: 22-31 - [c32]Michael A. Gelbart, Jasper Snoek, Ryan P. Adams:
Bayesian Optimization with Unknown Constraints. UAI 2014: 250-259 - [c31]Dougal Maclaurin, Ryan P. Adams:
Firefly Monte Carlo: Exact MCMC with Subsets of Data. UAI 2014: 543-552 - [i18]Scott W. Linderman, Ryan P. Adams:
Discovering Latent Network Structure in Point Process Data. CoRR abs/1402.0914 (2014) - [i17]Oren Rippel, Michael A. Gelbart, Ryan P. Adams:
Learning Ordered Representations with Nested Dropout. CoRR abs/1402.0915 (2014) - [i16]Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams:
Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR abs/1402.0929 (2014) - [i15]Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Ben Taskar:
Learning the Parameters of Determinantal Point Process Kernels. CoRR abs/1402.4862 (2014) - [i14]David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani:
Avoiding pathologies in very deep networks. CoRR abs/1402.5836 (2014) - [i13]Michael A. Gelbart, Jasper Snoek, Ryan P. Adams:
Bayesian Optimization with Unknown Constraints. CoRR abs/1403.5607 (2014) - [i12]Dougal Maclaurin, Ryan P. Adams:
Firefly Monte Carlo: Exact MCMC with Subsets of Data. CoRR abs/1403.5693 (2014) - [i11]Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Freeze-Thaw Bayesian Optimization. CoRR abs/1406.3896 (2014) - [i10]Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. CoRR abs/1408.2039 (2014) - [i9]Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams:
Graph-Sparse LDA: A Topic Model with Structured Sparsity. CoRR abs/1410.4510 (2014) - 2013
- [c30]Li-Wei H. Lehman, Shamim Nemati, Ryan P. Adams, George B. Moody, Atul Malhotra, Roger G. Mark:
Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series. EMBC 2013: 7072-7075 - [c29]Shamim Nemati, Li-Wei H. Lehman, Ryan P. Adams:
Learning outcome-discriminative dynamics in multivariate physiological cohort time series. EMBC 2013: 7104-7107 - [c28]Andrew Gordon Wilson, Ryan Prescott Adams:
Gaussian Process Kernels for Pattern Discovery and Extrapolation. ICML (3) 2013: 1067-1075 - [c27]Eyal Dechter, Jonathan Malmaud, Ryan P. Adams, Joshua B. Tenenbaum:
Bootstrap Learning via Modular Concept Discovery. IJCAI 2013: 1302-1309 - [c26]Jasper Snoek, Richard S. Zemel, Ryan Prescott Adams:
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. NIPS 2013: 1932-1940 - [c25]Kevin Swersky, Jasper Snoek, Ryan Prescott Adams:
Multi-Task Bayesian Optimization. NIPS 2013: 2004-2012 - [c24]James Y. Zou, Daniel J. Hsu, David C. Parkes, Ryan Prescott Adams:
Contrastive Learning Using Spectral Methods. NIPS 2013: 2238-2246 - [c23]Nils Napp, Ryan Prescott Adams:
Message Passing Inference with Chemical Reaction Networks. NIPS 2013: 2247-2255 - [c22]Amos Waterland, Elaine Angelino, Ekin D. Cubuk, Efthimios Kaxiras, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer:
Computational caches. SYSTOR 2013: 8:1-8:7 - [p1]Jeroen C. Chua, Inmar E. Givoni, Ryan P. Adams, Brendan J. Frey:
Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition. Machine Learning for Computer Vision 2013: 89-117 - [i8]Andrew Gordon Wilson, Ryan Prescott Adams:
Gaussian Process Covariance Kernels for Pattern Discovery and Extrapolation. CoRR abs/1302.4245 (2013) - [i7]Oren Rippel, Ryan Prescott Adams:
High-Dimensional Probability Estimation with Deep Density Models. CoRR abs/1302.5125 (2013) - [i6]Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka:
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures. CoRR abs/1304.2302 (2013) - 2012
- [j1]Jasper Snoek, Ryan P. Adams, Hugo Larochelle:
Nonparametric guidance of autoencoder representations using label information. J. Mach. Learn. Res. 13: 2567-2588 (2012) - [c21]Jeroen Chua, Inmar E. Givoni, Ryan Prescott Adams, Brendan J. Frey:
Learning structural element patch models with hierarchical palettes. CVPR 2012: 2416-2423 - [c20]Daniel Tarlow, Ryan Prescott Adams:
Revisiting uncertainty in graph cut solutions. CVPR 2012: 2440-2447 - [c19]Li-Wei H. Lehman, Shamim Nemati, Ryan P. Adams, Roger G. Mark:
Discovering shared dynamics in physiological signals: Application to patient monitoring in ICU. EMBC 2012: 5939-5942 - [c18]Shamim Nemati, Li-Wei H. Lehman, Ryan P. Adams, Atul Malhotra:
Discovering shared cardiovascular dynamics within a patient cohort. EMBC 2012: 6526-6529 - [c17]George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. ICML 2012 - [c16]Jasper Snoek, Hugo Larochelle, Ryan P. Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. NIPS 2012: 2960-2968 - [c15]James Y. Zou, Ryan P. Adams:
Priors for Diversity in Generative Latent Variable Models. NIPS 2012: 3005-3013 - [c14]Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey:
Probabilistic n-Choose-k Models for Classification and Ranking. NIPS 2012: 3059-3067 - [c13]Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams:
Cardinality Restricted Boltzmann Machines. NIPS 2012: 3302-3310 - [c12]Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey:
Fast Exact Inference for Recursive Cardinality Models. UAI 2012: 825-834 - [c11]Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle:
On Nonparametric Guidance for Learning Autoencoder Representations. AISTATS 2012: 1073-1080 - [c10]Daniel Tarlow, Ryan Prescott Adams, Richard S. Zemel:
Randomized Optimum Models for Structured Prediction. AISTATS 2012: 1221-1229 - [i5]George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. CoRR abs/1202.5695 (2012) - [i4]Jasper Snoek, Hugo Larochelle, Ryan Prescott Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. CoRR abs/1206.2944 (2012) - [i3]Daniel Tarlow, Kevin Swersky, Richard S. Zemel, Ryan Prescott Adams, Brendan J. Frey:
Fast Exact Inference for Recursive Cardinality Models. CoRR abs/1210.4899 (2012) - 2011
- [i2]Ryan Prescott Adams, Richard S. Zemel:
Ranking via Sinkhorn Propagation. CoRR abs/1106.1925 (2011) - 2010
- [c9]Ryan Prescott Adams, Zoubin Ghahramani, Michael I. Jordan:
Tree-Structured Stick Breaking for Hierarchical Data. NIPS 2010: 19-27 - [c8]Iain Murray, Ryan Prescott Adams:
Slice sampling covariance hyperparameters of latent Gaussian models. NIPS 2010: 1732-1740 - [c7]Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. UAI 2010: 1-9 - [c6]Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghahramani:
Learning the Structure of Deep Sparse Graphical Models. AISTATS 2010: 1-8 - [c5]Iain Murray, Ryan Prescott Adams, David J. C. MacKay:
Elliptical slice sampling. AISTATS 2010: 541-548 - [i1]Ryan Prescott Adams, George E. Dahl, Iain Murray:
Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes. CoRR abs/1003.4944 (2010)
2000 – 2009
- 2009
- [c4]Ryan Prescott Adams, Zoubin Ghahramani:
Archipelago: nonparametric Bayesian semi-supervised learning. ICML 2009: 1-8 - [c3]Ryan Prescott Adams, Iain Murray, David J. C. MacKay:
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. ICML 2009: 9-16 - 2008
- [c2]Ryan Prescott Adams, Oliver Stegle:
Gaussian process product models for nonparametric nonstationarity. ICML 2008: 1-8 - [c1]Ryan Prescott Adams, Iain Murray, David J. C. MacKay:
The Gaussian Process Density Sampler. NIPS 2008: 9-16
Coauthor Index
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