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ORCIDRyan P. Adams
Ryan Prescott Adams
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- affiliation: Princeton University, Department of Computer Science, NJ, USA
- affiliation (former): Harvard University, School of Engineering and Applied Sciences
- affiliation (former): University of Cambridge, Cavendish Laboratory
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2020 – today
- 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
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To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-04-02 20:15 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
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dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
