More from the Same Authors

• 2021 Spotlight: Slice Sampling Reparameterization Gradients »
  David Zoltowski · Diana Cai · Ryan Adams
• 2021 Spotlight: Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate »
  Xingyuan Sun · Tianju Xue · Szymon Rusinkiewicz · Ryan Adams
• 2021 : ProBF: Probabilistic Safety Certificates with Barrier Functions »
  Sulin Liu · Athindran Ramesh Kumar · Jaime Fisac · Ryan Adams · Peter J. Ramadge
• 2021 : Reading the Road: Leveraging Meta-Learning to Learn Other Driver Behavior »
  Anat Kleiman · Ryan Adams
• 2022 : A code superoptimizer through neural Monte-Carlo tree search »
  Wenda Zhou · Olga Solodova · Ryan Adams
• 2022 : A code superoptimizer through neural Monte-Carlo tree search »
  Wenda Zhou · Olga Solodova · Ryan Adams
• 2022 Poster: Multi-fidelity Monte Carlo: a pseudo-marginal approach »
  Diana Cai · Ryan Adams
• 2021 : Randomized Automatic Differentiation - Ryan Adams - Princeton University »
  Ryan Adams
• 2021 Poster: Slice Sampling Reparameterization Gradients »
  David Zoltowski · Diana Cai · Ryan Adams
• 2021 Poster: Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate »
  Xingyuan Sun · Tianju Xue · Szymon Rusinkiewicz · Ryan Adams
• 2021 Poster: Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
  Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan Adams · Sergey Levine
• 2020 Workshop: Machine Learning for Engineering Modeling, Simulation and Design »
  Alex Beatson · Priya Donti · Amira Abdel-Rahman · Stephan Hoyer · Rose Yu · J. Zico Kolter · Ryan Adams
• 2020 Poster: On Warm-Starting Neural Network Training »
  Jordan Ash · Ryan Adams
• 2020 Poster: Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters »
  Sulin Liu · Xingyuan Sun · Peter J. Ramadge · Ryan Adams
• 2020 Poster: Learning Composable Energy Surrogates for PDE Order Reduction »
  Alex Beatson · Jordan Ash · Geoffrey Roeder · Tianju Xue · Ryan Adams
• 2020 Oral: Learning Composable Energy Surrogates for PDE Order Reduction »
  Alex Beatson · Jordan Ash · Geoffrey Roeder · Tianju Xue · Ryan Adams
• 2019 Poster: SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers »
  Igor Fedorov · Ryan Adams · Matthew Mattina · Paul Whatmough
• 2019 Poster: Discrete Object Generation with Reversible Inductive Construction »
  Ari Seff · Wenda Zhou · Farhan Damani · Abigail Doyle · Ryan Adams
• 2018 : Discussion Panel: Ryan Adams, Nicolas Heess, Leslie Kaelbling, Shie Mannor, Emo Todorov (moderator: Roy Fox) »
  Ryan Adams · Nicolas Heess · Leslie Kaelbling · Shie Mannor · Emo Todorov · Roy Fox
• 2018 : Inference and Control of Learning Behavior in Rodents (Ryan Adams) »
  Ryan Adams
• 2018 Poster: A Bayesian Nonparametric View on Count-Min Sketch »
  Diana Cai · Michael Mitzenmacher · Ryan Adams
• 2017 Poster: PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
  Jonathan Huggins · Ryan Adams · Tamara Broderick
• 2017 Spotlight: PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
  Jonathan Huggins · Ryan Adams · Tamara Broderick
• 2017 Poster: Reducing Reparameterization Gradient Variance »
  Andrew Miller · Nick Foti · Alexander D'Amour · Ryan Adams
• 2016 : Panel Discussion »
  Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal
• 2016 : A Tribute to David MacKay »
  Ryan Adams
• 2016 Workshop: Bayesian Optimization: Black-box Optimization and Beyond »
  Roberto Calandra · Bobak Shahriari · Javier Gonzalez · Frank Hutter · Ryan Adams
• 2016 : Leveraging Structure in Bayesian Optimization »
  Ryan Adams
• 2016 Poster: Bayesian latent structure discovery from multi-neuron recordings »
  Scott Linderman · Ryan Adams · Jonathan Pillow
• 2016 Poster: Blind Attacks on Machine Learners »
  Alex Beatson · Zhaoran Wang · Han Liu
• 2016 Poster: Composing graphical models with neural networks for structured representations and fast inference »
  Matthew Johnson · David Duvenaud · Alex Wiltschko · Ryan Adams · Sandeep R Datta
• 2015 Workshop: Bayesian Optimization: Scalability and Flexibility »
  Bobak Shahriari · Ryan Adams · Nando de Freitas · Amar Shah · Roberto Calandra
• 2015 Workshop: Statistical Methods for Understanding Neural Systems »
  Alyson Fletcher · Jakob H Macke · Ryan Adams · Jascha Sohl-Dickstein
• 2015 Poster: Convolutional Networks on Graphs for Learning Molecular Fingerprints »
  David Duvenaud · Dougal Maclaurin · Jorge Iparraguirre · Rafael Bombarell · Timothy Hirzel · Alan Aspuru-Guzik · Ryan Adams
• 2015 Poster: A Gaussian Process Model of Quasar Spectral Energy Distributions »
  Andrew Miller · Albert Wu · Jeffrey Regier · Jon McAuliffe · Dustin Lang · Mr. Prabhat · David Schlegel · Ryan Adams
• 2015 Poster: Spectral Representations for Convolutional Neural Networks »
  Oren Rippel · Jasper Snoek · Ryan Adams
• 2015 Poster: Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation »
  Scott Linderman · Matthew Johnson · Ryan Adams
• 2014 Workshop: Bayesian Optimization in Academia and Industry »
  Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek
• 2014 Poster: A framework for studying synaptic plasticity with neural spike train data »
  Scott Linderman · Christopher H Stock · Ryan Adams
• 2013 Workshop: Bayesian Optimization in Theory and Practice »
  Matthew Hoffman · Jasper Snoek · Nando de Freitas · Michael A Osborne · Ryan Adams · Sebastien Bubeck · Philipp Hennig · Remi Munos · Andreas Krause
• 2013 Poster: Multi-Task Bayesian Optimization »
  Kevin Swersky · Jasper Snoek · Ryan Adams
• 2013 Poster: Message Passing Inference with Chemical Reaction Networks »
  Nils E Napp · Ryan Adams
• 2013 Oral: Message Passing Inference with Chemical Reaction Networks »
  Nils E Napp · Ryan Adams
• 2013 Poster: A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data »
  Jasper Snoek · Richard Zemel · Ryan Adams
• 2013 Poster: Contrastive Learning Using Spectral Methods »
  James Y Zou · Daniel Hsu · David Parkes · Ryan Adams
• 2012 Poster: Bayesian n-Choose-k Models for Classification and Ranking »
  Kevin Swersky · Danny Tarlow · Richard Zemel · Ryan Adams · Brendan J Frey
• 2012 Poster: Priors for Diversity in Generative Latent Variable Models »
  James Y Zou · Ryan Adams
• 2012 Poster: Cardinality Restricted Boltzmann Machines »
  Kevin Swersky · Danny Tarlow · Ilya Sutskever · Richard Zemel · Russ Salakhutdinov · Ryan Adams
• 2012 Poster: Practical Bayesian Optimization of Machine Learning Algorithms »
  Jasper Snoek · Hugo Larochelle · Ryan Adams
• 2011 Workshop: Bayesian Nonparametric Methods: Hope or Hype? »
  Emily Fox · Ryan Adams
• 2010 Workshop: Transfer Learning Via Rich Generative Models. »
  Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths
• 2010 Workshop: Monte Carlo Methods for Bayesian Inference in Modern Day Applications »
  Ryan Adams · Mark A Girolami · Iain Murray
• 2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
  Ryan Adams · Zoubin Ghahramani · Michael Jordan
• 2010 Oral: Slice sampling covariance hyperparameters of latent Gaussian models »
  Iain Murray · Ryan Adams
• 2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
  Ryan Adams · Zoubin Ghahramani · Michael Jordan
• 2010 Poster: Slice sampling covariance hyperparameters of latent Gaussian models »
  Iain Murray · Ryan Adams
• 2008 Poster: The Gaussian Process Density Sampler »
  Ryan Adams · Iain Murray · David MacKay
• 2008 Spotlight: The Gaussian Process Density Sampler »
  Ryan Adams · Iain Murray · David MacKay