Ryan P. Adams - RankMe

136 papers A* 40A 9Misc 1Journal 64Unranked 21

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Single Architecture for Representing Invariance Under Any Space Group. CoRR Cindy Y. Zhang, Elif Ertekin, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Designing Mechanical Meta-Materials by Learning Equivariant Flows. ICLR Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. ICML Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams
2025	J	`jnl`	Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. CoRR Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Efficiently Vectorized MCMC on Modern Accelerators. ICML Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P. Adams
2025	J	`jnl`	Efficiently Vectorized MCMC on Modern Accelerators. CoRR Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Graph Neural Networks Gone Hogwild. ICLR Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
2025	J	`jnl`	Local Learning Rules for Out-of-Equilibrium Physical Generative Models. CoRR Cyrill Bösch, Geoffrey Roeder, Marc Serra-Garcia, Ryan P. Adams
2025	J	`jnl`	Nonlinear Computation with Linear Optics via Source-Position Encoding. CoRR Nick Richardson, Cyrill Bösch, Ryan P. Adams
2025	A*	`conf`	Real-time design of architectural structures with differentiable mechanics and neural networks. ICLR Rafael Pastrana, Eder Medina, Isabel M. de Oliveira, Sigrid Adriaenssens, Ryan P. Adams
2025	J	`jnl`	Space Group Equivariant Crystal Diffusion. CoRR Rees Chang, Angela Pak, Alex Guerra, Ni Zhan, Nick Richardson, Elif Ertekin, Ryan P. Adams
2024	J	`jnl`	A rapid and automated computational approach to the design of multistable soft actuators. Comput. Phys. Commun. Mehran Mirramezani, Deniz Oktay, Ryan P. Adams
2024	J	`jnl`	Designing Mechanical Meta-Materials by Learning Equivariant Flows. CoRR Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams
2024	A*	`conf`	Fiber Monte Carlo. ICLR Nick Richardson, Deniz Oktay, Yaniv Ovadia, James C. Bowden, Ryan P. Adams
2024	A*	`conf`	Generative Marginalization Models. ICML Sulin Liu, Peter J. Ramadge, Ryan P. Adams
2024	J	`jnl`	Graph Neural Networks Gone Hogwild. CoRR Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
2024	—	`conf`	Measuring Cooperation with Counterfactual Planning. GameSec (1) Samuel A. Barnett, Kathryn Wantlin, Ryan P. Adams
2024	J	`jnl`	Real-time design of architectural structures with differentiable simulators and neural networks. CoRR Rafael Pastrana, Eder Medina, Isabel M. de Oliveira, Sigrid Adriaenssens, Ryan P. Adams
2023	J	`jnl`	A rapid and automated computational approach to the design of multistable soft actuators. CoRR Mehran Mirramezani, Deniz Oktay, Ryan P. Adams
2023	J	`jnl`	Generative Marginalization Models. CoRR Sulin Liu, Peter J. Ramadge, Ryan P. Adams
2023	—	`conf`	Gradient-Based Dovetail Joint Shape Optimization for Stiffness. SCF Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz
2023	J	`jnl`	Gradient-Based Dovetail Joint Shape Optimization for Stiffness. CoRR Xingyuan Sun, Chenyue Cai, Ryan P. Adams, Szymon Rusinkiewicz
2023	J	`jnl`	JAX FDM: A differentiable solver for inverse form-finding. CoRR Rafael Pastrana, Deniz Oktay, Ryan P. Adams, Sigrid Adriaenssens
2023	J	`jnl`	Learning to Learn Functions. Cogn. Sci. Michael Y. Li, Fred Callaway, William D. Thompson, Ryan P. Adams, Thomas L. Griffiths
2023	—	`conf`	More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. SCF Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz
2023	A*	`conf`	Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. ICLR Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams
2023	J	`jnl`	Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. CoRR Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams
2023	J	`jnl`	Representing and Learning Functions Invariant Under Crystallographic Groups. CoRR Ryan P. Adams, Peter Orbanz
2022	J	`jnl`	Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh. CoRR Tian Qin, Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams
2022	J	`jnl`	More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. CoRR Xingyuan Sun, Geoffrey Roeder, Tianju Xue, Ryan P. Adams, Szymon Rusinkiewicz
2022	A*	`conf`	Multi-fidelity Monte Carlo: a pseudo-marginal approach. NeurIPS Diana Cai, Ryan P. Adams
2022	J	`jnl`	Multi-fidelity Monte Carlo: a pseudo-marginal approach. CoRR Diana Cai, Ryan P. Adams
2022	A*	`conf`	Vitruvion: A Generative Model of Parametric CAD Sketches. ICLR Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
2021	A	`conf`	Active multi-fidelity Bayesian online changepoint detection. UAI Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams
2021	J	`jnl`	Active multi-fidelity Bayesian online changepoint detection. CoRR Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams
2021	A*	`conf`	Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. NeurIPS Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams
2021	J	`jnl`	Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate. CoRR Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams
2021	J	`jnl`	Bayesian reaction optimization as a tool for chemical synthesis. Nat. 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
2021	J	`jnl`	ProBF: Learning Probabilistic Safety Certificates with Barrier Functions. CoRR Athindran Ramesh Kumar, Sulin Liu, Jaime F. Fisac, Ryan P. Adams, Peter J. Ramadge
2021	A*	`conf`	Randomized Automatic Differentiation. ICLR Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams
2021	A*	`conf`	Slice Sampling Reparameterization Gradients. NeurIPS David M. Zoltowski, Diana Cai, Ryan P. Adams
2021	J	`jnl`	Vitruvion: A Generative Model of Parametric CAD Sketches. CoRR Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams
2021	A*	`conf`	Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. NeurIPS Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
2021	J	`jnl`	Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability. CoRR Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine
2020	A*	`conf`	Amortized Finite Element Analysis for Fast PDE-Constrained Optimization. ICML Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams
2020	A*	`conf`	Learning Composable Energy Surrogates for PDE Order Reduction. NeurIPS Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
2020	J	`jnl`	Learning Composable Energy Surrogates for PDE Order Reduction. CoRR Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams
2020	A*	`conf`	On Warm-Starting Neural Network Training. NeurIPS Jordan T. Ash, Ryan P. Adams
2020	A	`ed.`	Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, UAI 2020, virtual online, August 3-6, 2020 UAI Ryan P. Adams, Vibhav Gogate
2020	J	`jnl`	Randomized Automatic Differentiation. CoRR Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams
2020	A*	`conf`	SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. ICLR Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
2020	J	`jnl`	SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. CoRR Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
2020	J	`jnl`	SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design. CoRR Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams
2020	A*	`conf`	Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters. NeurIPS Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams
2019	J	`jnl`	A Theoretical Connection Between Statistical Physics and Reinforcement Learning. CoRR Jad Rahme, Ryan P. Adams
2019	A*	`conf`	Discrete Object Generation with Reversible Inductive Construction. NeurIPS Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams
2019	J	`jnl`	Discrete Object Generation with Reversible Inductive Construction. CoRR Ari Seff, Wenda Zhou, Farhan N. Damani, Abigail G. Doyle, Ryan P. Adams
2019	J	`jnl`	Efficient Optimization of Loops and Limits with Randomized Telescoping Sums. CoRR Alex Beatson, Ryan P. Adams
2019	A*	`conf`	Efficient optimization of loops and limits with randomized telescoping sums. ICML Alex Beatson, Ryan P. Adams
2019	—	`conf`	Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2019	J	`jnl`	On the Difficulty of Warm-Starting Neural Network Training. CoRR Jordan T. Ash, Ryan P. Adams
2019	A*	`conf`	SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. NeurIPS Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
2019	J	`jnl`	SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers. CoRR Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough
2018	A*	`conf`	A Bayesian Nonparametric View on Count-Min Sketch. NeurIPS Diana Cai, Michael Mitzenmacher, Ryan P. Adams
2018	J	`jnl`	Approximate Inference for Constructing Astronomical Catalogs from Images. CoRR Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat
2018	J	`jnl`	Compressibility and Generalization in Large-Scale Deep Learning. CoRR Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2018	J	`jnl`	Motivating the Rules of the Game for Adversarial Example Research. CoRR Justin Gilmer, Ryan P. Adams, Ian J. Goodfellow, David G. Andersen, George E. Dahl
2018	A	`conf`	Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. AISTATS Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
2017	A	`conf`	Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
2017	J	`jnl`	Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. CoRR Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams
2017	—	`conf`	PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference. NIPS Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick
2017	—	`conf`	Reducing Reparameterization Gradient Variance. NIPS Andrew C. Miller, Nick Foti, Alexander D'Amour, Ryan P. Adams
2017	A*	`conf`	Variational Boosting: Iteratively Refining Posterior Approximations. ICML Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
2016	J	`jnl`	A General Framework for Constrained Bayesian Optimization using Information-based Search. J. Mach. Learn. Res. José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani
2016	J	`jnl`	Automatic chemical design using a data-driven continuous representation of molecules. CoRR Rafael Gómez-Bombarelli, David Duvenaud, José Miguel Hernández-Lobato, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik
2016	—	`conf`	Bayesian latent structure discovery from multi-neuron recordings. NIPS Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow
2016	J	`jnl`	Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
2016	J	`jnl`	Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. CoRR Akash Srivastava, James Y. Zou, Ryan P. Adams, Charles Sutton
2016	—	`conf`	Composing graphical models with neural networks for structured representations and fast inference. NIPS Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Ryan P. Adams, Sandeep R. Datta
2016	A	`conf`	Early Stopping as Nonparametric Variational Inference. AISTATS David Duvenaud, Dougal Maclaurin, Ryan P. Adams
2016	J	`jnl`	Patterns of Scalable Bayesian Inference. Found. Trends Mach. Learn. Elaine Angelino, Matthew James Johnson, Ryan P. Adams
2016	A*	`conf`	Predictive Entropy Search for Multi-objective Bayesian Optimization. ICML Daniel Hernández-Lobato, José Miguel Hernández-Lobato, Amar Shah, Ryan P. Adams
2016	J	`jnl`	Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proc. IEEE Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, Nando de Freitas
2016	A*	`conf`	The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. ICML Ardavan Saeedi, Matthew D. Hoffman, Matthew J. Johnson, Ryan P. Adams
2016	A*	`conf`	Variability and predictability in tactile sensing during grasping. ICRA Qian Wan, Ryan P. Adams, Robert D. Howe
2016	J	`jnl`	Variational Boosting: Iteratively Refining Posterior Approximations. CoRR Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams
2015	—	`conf`	A Gaussian Process Model of Quasar Spectral Energy Distributions. NIPS Andrew C. Miller, Albert Wu, Jeffrey Regier, Jon McAuliffe, Dustin Lang, Prabhat, David Schlegel, Ryan P. Adams
2015	J	`jnl`	A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction. IEEE J. Biomed. Health Informatics Li-Wei H. Lehman, Ryan P. Adams, Louis Mayaud, George B. Moody, Atul Malhotra, Roger G. Mark, Shamim Nemati
2015	A*	`conf`	Celeste: Variational inference for a generative model of astronomical images. ICML Jeffrey Regier, Andrew C. Miller, Jon McAuliffe, Ryan P. Adams, Matthew D. Hoffman, Dustin Lang, David Schlegel, Prabhat
2015	—	`conf`	Convolutional Networks on Graphs for Learning Molecular Fingerprints. NIPS David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
2015	J	`jnl`	Convolutional Networks on Graphs for Learning Molecular Fingerprints. CoRR David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams
2015	—	`conf`	Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. NIPS Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams
2015	J	`jnl`	Early Stopping is Nonparametric Variational Inference. CoRR Dougal Maclaurin, David Duvenaud, Ryan P. Adams
2015	A*	`conf`	Gradient-based Hyperparameter Optimization through Reversible Learning. ICML Dougal Maclaurin, David Duvenaud, Ryan P. Adams
2015	J	`jnl`	Gradient-based Hyperparameter Optimization through Reversible Learning. CoRR Dougal Maclaurin, David Duvenaud, Ryan P. Adams
2015	A*	`conf`	Graph-Sparse LDA: A Topic Model with Structured Sparsity. AAAI Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams
2015	J	`jnl`	Guest Editors' Introduction to the Special Issue on Bayesian Nonparametrics. IEEE Trans. Pattern Anal. Mach. Intell. Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh
2015	A*	`conf`	Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. ICML José Miguel Hernández-Lobato, Michael A. Gelbart, Matthew W. Hoffman, Ryan P. Adams, Zoubin Ghahramani
2015	A*	`conf`	Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. ICML José Miguel Hernández-Lobato, Ryan P. Adams
2015	J	`jnl`	Sandwiching the marginal likelihood using bidirectional Monte Carlo. CoRR Roger B. Grosse, Zoubin Ghahramani, Ryan P. Adams
2015	A*	`conf`	Scalable Bayesian Optimization Using Deep Neural Networks. ICML Jasper Snoek, Oren Rippel, Kevin Swersky, Ryan Kiros, Nadathur Satish, Narayanan Sundaram, Md. Mostofa Ali Patwary, Prabhat, Ryan P. Adams
2015	—	`conf`	Spectral Representations for Convolutional Neural Networks. NIPS Oren Rippel, Jasper Snoek, Ryan P. Adams
2015	J	`jnl`	Spectral Representations for Convolutional Neural Networks. CoRR Oren Rippel, Jasper Snoek, Ryan P. Adams
2014	—	`conf`	A framework for studying synaptic plasticity with neural spike train data. NIPS Scott W. Linderman, Christopher H. Stock, Ryan P. Adams
2014	A*	`conf`	ASC: automatically scalable computation. ASPLOS Amos Waterland, Elaine Angelino, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer
2014	A	`conf`	Accelerating MCMC via Parallel Predictive Prefetching. UAI Elaine Angelino, Eddie Kohler, Amos Waterland, Margo I. Seltzer, Ryan P. Adams
2014	A	`conf`	Avoiding pathologies in very deep networks. AISTATS David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
2014	J	`jnl`	Avoiding pathologies in very deep networks. CoRR David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani
2014	A	`conf`	Bayesian Optimization with Unknown Constraints. UAI Michael A. Gelbart, Jasper Snoek, Ryan P. Adams
2014	J	`jnl`	Bayesian Optimization with Unknown Constraints. CoRR Michael A. Gelbart, Jasper Snoek, Ryan P. Adams
2014	A*	`conf`	Discovering Latent Network Structure in Point Process Data. ICML Scott W. Linderman, Ryan P. Adams
2014	J	`jnl`	Discovering Latent Network Structure in Point Process Data. CoRR Scott W. Linderman, Ryan P. Adams
2014	A*	`conf`	Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. ICML Andrew C. Miller, Luke Bornn, Ryan P. Adams, Kirk Goldsberry
2014	A	`conf`	Firefly Monte Carlo: Exact MCMC with Subsets of Data. UAI Dougal Maclaurin, Ryan P. Adams
2014	J	`jnl`	Firefly Monte Carlo: Exact MCMC with Subsets of Data. CoRR Dougal Maclaurin, Ryan P. Adams
2014	J	`jnl`	Graph-Sparse LDA: A Topic Model with Structured Sparsity. CoRR Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams
2014	A*	`conf`	Input Warping for Bayesian Optimization of Non-Stationary Functions. ICML Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
2014	J	`jnl`	Input Warping for Bayesian Optimization of Non-stationary Functions. CoRR Jasper Snoek, Kevin Swersky, Richard S. Zemel, Ryan P. Adams
2014	A*	`conf`	Learning Ordered Representations with Nested Dropout. ICML Oren Rippel, Michael A. Gelbart, Ryan P. Adams
2014	J	`jnl`	Learning Ordered Representations with Nested Dropout. CoRR Oren Rippel, Michael A. Gelbart, Ryan P. Adams
2014	A*	`conf`	Learning the Parameters of Determinantal Point Process Kernels. ICML Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Benjamin Taskar
2014	J	`jnl`	Learning the Parameters of Determinantal Point Process Kernels. CoRR Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Ben Taskar
2014	J	`jnl`	Parallel MCMC with generalized elliptical slice sampling. J. Mach. Learn. Res. Robert Nishihara, Iain Murray, Ryan P. Adams
2013	—	`ch.`	Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition. Machine Learning for Computer Vision Jeroen C. Chua, Inmar E. Givoni, Ryan P. Adams, Brendan J. Frey
2013	A*	`conf`	Bootstrap Learning via Modular Concept Discovery. IJCAI Eyal Dechter, Jonathan Malmaud, Ryan P. Adams, Joshua B. Tenenbaum
2013	J	`jnl`	ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures CoRR Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka
2013	Misc	`conf`	Computational caches. SYSTOR Amos Waterland, Elaine Angelino, Ekin D. Cubuk, Efthimios Kaxiras, Ryan P. Adams, Jonathan Appavoo, Margo I. Seltzer
2013	—	`conf`	Learning outcome-discriminative dynamics in multivariate physiological cohort time series. EMBC Shamim Nemati, Li-Wei H. Lehman, Ryan P. Adams
2013	—	`conf`	Tracking progression of patient state of health in critical care using inferred shared dynamics in physiological time series. EMBC Li-Wei H. Lehman, Shamim Nemati, Ryan P. Adams, George B. Moody, Atul Malhotra, Roger G. Mark
2012	—	`conf`	Cardinality Restricted Boltzmann Machines. NIPS Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard S. Zemel, Ryan P. Adams
2012	—	`conf`	Discovering shared cardiovascular dynamics within a patient cohort. EMBC Shamim Nemati, Li-Wei H. Lehman, Ryan P. Adams, Atul Malhotra
2012	—	`conf`	Discovering shared dynamics in physiological signals: Application to patient monitoring in ICU. EMBC Li-Wei H. Lehman, Shamim Nemati, Ryan P. Adams, Roger G. Mark
2012	J	`jnl`	Nonparametric guidance of autoencoder representations using label information. J. Mach. Learn. Res. Jasper Snoek, Ryan P. Adams, Hugo Larochelle
2012	—	`conf`	Practical Bayesian Optimization of Machine Learning Algorithms. NIPS Jasper Snoek, Hugo Larochelle, Ryan P. Adams
2012	—	`conf`	Priors for Diversity in Generative Latent Variable Models. NIPS James Y. Zou, Ryan P. Adams
2012	—	`conf`	Probabilistic n-Choose-k Models for Classification and Ranking. NIPS Kevin Swersky, Daniel Tarlow, Ryan P. Adams, Richard S. Zemel, Brendan J. Frey