← Authors

Ryan Adams

22 papers · 2010–2020 · 3 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (10) 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (6) πŸ—ΊοΈ Taxonomy Completionist (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (3) πŸ”¬ Deep Specialist (12) πŸ—ƒοΈ Keyword Collector (98) πŸš€ Conference Pioneer πŸ’Ž Century Club (22) πŸ”₯ Unstoppable (9) πŸ“ˆ Trend Setter ⚑ Prolific Year (6)

Conferences

ICML (14) AISTATS (7) UAI (1)

Top co-authors

Oren Rippel (3) Andrew Miller (3) Matthew Hoffman (3) Matthew Johnson (3) David Duvenaud (3) Jasper Snoek (3) Dougal Maclaurin (2) Mr Prabhat (2) JosΓ© Miguel HernΓ‘ndez-Lobato (2) Michael Gelbart (2)

Research topics

Statistics (1)

Keywords

bayesian inference (6) hyperparameter optimization (4) bayesian optimization (4) gaussian process (4) variational inference (3) representation learning (3) uncertainty quantification (2) neural network architecture (2) bayesian nonparametric (2) time series (2) point process (2) neural network (2) multivariate gaussian (1) scalable learning (1) feature learning (1) matrix factorization (1) structured prediction (1) multi-task learning (1) dimensionality reduction (1) neural network optimization (1)

Papers

Amortized Finite Element Analysis for Fast PDE-Constrained Optimization ICML 2020
The 35th Uncertainty in Artificial Intelligence Conference: Preface UAI 2019
Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models AISTATS 2018
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems AISTATS 2017
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM ICML 2016
Early Stopping as Nonparametric Variational Inference AISTATS 2016
Predictive Entropy Search for Multi-objective Bayesian Optimization ICML 2016
Gradient-based Hyperparameter Optimization through Reversible Learning ICML 2015
Predictive Entropy Search for Bayesian Optimization with Unknown Constraints ICML 2015
Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks ICML 2015
Scalable Bayesian Optimization Using Deep Neural Networks ICML 2015
Celeste: Variational inference for a generative model of astronomical images ICML 2015
Input Warping for Bayesian Optimization of Non-Stationary Functions ICML 2014
Avoiding pathologies in very deep networks AISTATS 2014
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball ICML 2014
Learning the Parameters of Determinantal Point Process Kernels ICML 2014
Discovering Latent Network Structure in Point Process Data ICML 2014
Learning Ordered Representations with Nested Dropout ICML 2014
Gaussian Process Kernels for Pattern Discovery and Extrapolation ICML 2013
Randomized Optimum Models for Structured Prediction AISTATS 2012
On Nonparametric Guidance for Learning Autoencoder Representations AISTATS 2012
Elliptical slice sampling AISTATS 2010