Ryan Adams
22 papers · 2010–2020 · 3 conferences · across top CS/AI conferences
Achievements
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π£ Hot Topic Early Bird π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (14) π§ Keyword Pioneer π Conference Polyglot (3)
π£
Hot Topic Early Bird
π
Interdisciplinary Bridge
π
Conference Polyglot
(3)
π¬
Deep Specialist
(12)
ποΈ
Keyword Collector
(98)
π
Conference Pioneer
π
Century Club
(22)
π₯
Unstoppable
(9)
π
Trend Setter
β‘
Prolific Year
(6)
Conferences
ICML (14)
AISTATS (7)
UAI (1)
Top co-authors
Research topics
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
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