Ryan Adams
22 papers · 2010–2020 · 3 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (38) 🏃 Academic Marathon · Half 🌍 Conference Polyglot · Bronze 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
🔬
Deep Specialist
(13)
🧭
Keyword Pioneer
🚀
Conference Pioneer
📈
Trend Setter
⚡
Prolific Year · Productive
🔥
Unstoppable · Bronze
🗃️
Keyword Collector
(98)
💎
Century Club · Bronze
Venues
Top co-authors
Research topics
Machine Learning
(21)
Optimization & Theory
(14)
Deep Learning
(7)
Mathematics & Optimization
(7)
Artificial Intelligence
(6)
Bayesian & Probabilistic · Artificial Intelligence
(5)
Bayesian & Probabilistic · Machine Learning
(5)
Core Methods
(5)
Models
(4)
Optimization
(4)
Data Science & Analytics
(3)
Architectures
(3)
Learning Types · Deep Learning
(3)
Mathematics
(3)
Learning Types · Machine Learning
(2)
Keywords
bayesian inference
(6)
gaussian process
(4)
hyperparameter optimization
(4)
bayesian optimization
(4)
variational inference
(3)
representation learning
(3)
bayesian nonparametrics
(2)
neural network architecture
(2)
neural network
(2)
point process
(2)
time series
(2)
uncertainty quantification
(2)
scalable learning
(1)
matrix factorization
(1)
feature learning
(1)
structured prediction
(1)
multi-task learning
(1)
dimensionality reduction
(1)
black-box optimization
(1)
gradient-based 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
Early Stopping as Nonparametric Variational Inference
AISTATS 2016
Predictive Entropy Search for Multi-objective Bayesian Optimization
ICML 2016
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
ICML 2016
Celeste: Variational inference for a generative model of astronomical images
ICML 2015
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
Avoiding pathologies in very deep networks
AISTATS 2014
Discovering Latent Network Structure in Point Process Data
ICML 2014
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
ICML 2014
Input Warping for Bayesian Optimization of Non-Stationary Functions
ICML 2014
Learning Ordered Representations with Nested Dropout
ICML 2014
Learning the Parameters of Determinantal Point Process Kernels
ICML 2014
Gaussian Process Kernels for Pattern Discovery and Extrapolation
ICML 2013
On Nonparametric Guidance for Learning Autoencoder Representations
AISTATS 2012
Randomized Optimum Models for Structured Prediction
AISTATS 2012
Elliptical slice sampling
AISTATS 2010