Adams, Ryan P.

64 publications

ICLRW 2025 Crystal Generative Modeling with Explicit Autoregressive Conditional Likelihoods and Nontrivial Space Group Stabilizers Rees Chang, Alex Guerra, Nick Richardson, Ni Zhan, Sulin Liu, Angela Pak, Ryan Marr, Alex M. Ganose, Ryan P Adams, Elif Ertekin

ICLR 2025 Designing Mechanical Meta-Materials by Learning Equivariant Flows Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P Adams

ICML 2025 Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P Adams

ICML 2025 Efficiently Vectorized MCMC on Modern Accelerators Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P Adams

ICLR 2025 Graph Neural Networks Gone Hogwild Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P Adams

ICLR 2025 Real-Time Design of Architectural Structures with Differentiable Mechanics and Neural Networks Rafael Pastrana, Eder Medina, Isabel M. de Oliveira, Sigrid Adriaenssens, Ryan P Adams

NeurIPS 2025 Space Group Equivariant Crystal Diffusion Rees Chang, Angela Pak, Alex Guerra, Ni Zhan, Nick Richardson, Elif Ertekin, Ryan P Adams

ICLR 2024 Fiber Monte Carlo Nick Richardson, Deniz Oktay, Yaniv Ovadia, James C Bowden, Ryan P Adams

ICML 2024 Generative Marginalization Models Sulin Liu, Peter Ramadge, Ryan P Adams

ICMLW 2023 Generative Marginalization Models Sulin Liu, Peter Ramadge, Ryan P Adams

ICMLW 2023 JAX FDM: A Differentiable Solver for Inverse Form-Finding Rafael Pastrana, Deniz Oktay, Ryan P Adams, Sigrid Adriaenssens

ICLR 2023 Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P Adams

NeurIPS 2022 Multi-Fidelity Monte Carlo: A Pseudo-Marginal Approach Diana Cai, Ryan P. Adams

ICLR 2022 Vitruvion: A Generative Model of Parametric CAD Sketches Ari Seff, Wenda Zhou, Nick Richardson, Ryan P Adams

UAI 2021 Active Multi-Fidelity Bayesian Online Changepoint Detection Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams

NeurIPS 2021 Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate Xingyuan Sun, Tianju Xue, Szymon Rusinkiewicz, Ryan P. Adams

ICLR 2021 Randomized Automatic Differentiation Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P Adams

NeurIPS 2021 Slice Sampling Reparameterization Gradients David Zoltowski, Diana Cai, Ryan P. Adams

NeurIPS 2021 Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine

ICLRW 2020 Amortized Finite Element Analysis for Fast PDE-Constrained Optimization Tianju Xue, Alex Beatson, Sigrid Adriaenssens, Ryan P. Adams

NeurIPS 2020 Learning Composable Energy Surrogates for PDE Order Reduction Alex Beatson, Jordan Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams

NeurIPS 2020 On Warm-Starting Neural Network Training Jordan Ash, Ryan P. Adams

ICLR 2020 SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

NeurIPS 2020 Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters Sulin Liu, Xingyuan Sun, Peter J. Ramadge, Ryan P. Adams

NeurIPS 2019 Discrete Object Generation with Reversible Inductive Construction Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams

ICML 2019 Efficient Optimization of Loops and Limits with Randomized Telescoping Sums Alex Beatson, Ryan P Adams

ICLR 2019 Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

NeurIPS 2019 SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul Whatmough

NeurIPS 2018 A Bayesian Nonparametric View on Count-Min Sketch Diana Cai, Michael Mitzenmacher, Ryan P. Adams

AISTATS 2018 Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams

AISTATS 2017 Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski

NeurIPS 2017 PASS-GLM: Polynomial Approximate Sufficient Statistics for Scalable Bayesian GLM Inference Jonathan Huggins, Ryan P. Adams, Tamara Broderick

NeurIPS 2017 Reducing Reparameterization Gradient Variance Andrew Miller, Nicholas Foti, Alexander D'Amour, Ryan P. Adams

ICML 2017 Variational Boosting: Iteratively Refining Posterior Approximations Andrew C. Miller, Nicholas J. Foti, Ryan P. Adams

JMLR 2016 A General Framework for Constrained Bayesian Optimization Using Information-Based Search José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani

NeurIPS 2016 Bayesian Latent Structure Discovery from Multi-Neuron Recordings Scott Linderman, Ryan P. Adams, Jonathan W Pillow

NeurIPS 2016 Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference Matthew J Johnson, David K. Duvenaud, Alex Wiltschko, Ryan P. Adams, Sandeep R Datta

AISTATS 2016 Early Stopping as Nonparametric Variational Inference David Duvenaud, Dougal Maclaurin, Ryan P. Adams

FnTML 2016 Patterns of Scalable Bayesian Inference Elaine Angelino, Matthew James Johnson, Ryan P. Adams

NeurIPS 2015 A Gaussian Process Model of Quasar Spectral Energy Distributions Andrew Miller, Albert Wu, Jeff Regier, Jon McAuliffe, Dustin Lang, Mr. Prabhat, David Schlegel, Ryan P. Adams

NeurIPS 2015 Convolutional Networks on Graphs for Learning Molecular Fingerprints David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams

NeurIPS 2015 Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-Gamma Augmentation Scott Linderman, Matthew J Johnson, Ryan P. Adams

AAAI 2015 Graph-Sparse LDA: A Topic Model with Structured Sparsity Finale Doshi-Velez, Byron C. Wallace, Ryan P. Adams

NeurIPS 2015 Spectral Representations for Convolutional Neural Networks Oren Rippel, Jasper Snoek, Ryan P. Adams

NeurIPS 2014 A Framework for Studying Synaptic Plasticity with Neural Spike Train Data Scott Linderman, Christopher H Stock, Ryan P. Adams

UAI 2014 Accelerating MCMC via Parallel Predictive Prefetching Elaine Angelino, Eddie Kohler, Amos Waterland, Margo I. Seltzer, Ryan P. Adams

AISTATS 2014 Avoiding Pathologies in Very Deep Networks David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

UAI 2014 Bayesian Optimization with Unknown Constraints Michael A. Gelbart, Jasper Snoek, Ryan P. Adams

UAI 2014 Firefly Monte Carlo: Exact MCMC with Subsets of Data Dougal Maclaurin, Ryan P. Adams

JMLR 2014 Parallel MCMC with Generalized Elliptical Slice Sampling Robert Nishihara, Iain Murray, Ryan P. Adams

NeurIPS 2013 A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data Jasper Snoek, Richard Zemel, Ryan P. Adams

IJCAI 2013 Bootstrap Learning via Modular Concept Discovery Eyal Dechter, Jonathan Malmaud, Ryan P. Adams, Joshua B. Tenenbaum

NeurIPS 2013 Contrastive Learning Using Spectral Methods James Y Zou, Daniel J. Hsu, David C. Parkes, Ryan P. Adams

NeurIPS 2013 Message Passing Inference with Chemical Reaction Networks Nils E Napp, Ryan P. Adams

NeurIPS 2013 Multi-Task Bayesian Optimization Kevin Swersky, Jasper Snoek, Ryan P. Adams

NeurIPS 2012 Cardinality Restricted Boltzmann Machines Kevin Swersky, Ilya Sutskever, Daniel Tarlow, Richard S. Zemel, Ruslan Salakhutdinov, Ryan P. Adams

JMLR 2012 Nonparametric Guidance of Autoencoder Representations Using Label Information Jasper Snoek, Ryan P. Adams, Hugo Larochelle

NeurIPS 2012 Practical Bayesian Optimization of Machine Learning Algorithms Jasper Snoek, Hugo Larochelle, Ryan P. Adams

NeurIPS 2012 Priors for Diversity in Generative Latent Variable Models James T. Kwok, Ryan P. Adams

NeurIPS 2012 Probabilistic N-Choose-K Models for Classification and Ranking Kevin Swersky, Brendan J. Frey, Daniel Tarlow, Richard S. Zemel, Ryan P. Adams

AISTATS 2010 Learning the Structure of Deep Sparse Graphical Models Ryan P. Adams, Hanna Wallach, Zoubin Ghahramani

NeurIPS 2010 Slice Sampling Covariance Hyperparameters of Latent Gaussian Models Iain Murray, Ryan P. Adams

NeurIPS 2010 Tree-Structured Stick Breaking for Hierarchical Data Zoubin Ghahramani, Michael I. Jordan, Ryan P. Adams

NeurIPS 2008 The Gaussian Process Density Sampler Iain Murray, David MacKay, Ryan P. Adams