Ryan P. Adams
38 papers · 2008–2022 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer πΊοΈ Taxonomy Completionist (21) π Renaissance Researcher (6) π Interdisciplinary Bridge π£ Hot Topic Early Bird
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Interdisciplinary Bridge
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Academic Marathon
(14)
πΊοΈ
Taxonomy Completionist
(21)
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Keyword Trendsetter Combo
(3)
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Conference Loyalist
(30)
π
The Namer
π¬
Deep Specialist
(17)
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Keyword Champion
π±
Topic Pioneer
π₯
Unstoppable
(11)
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Conference Pioneer
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Century Club
(38)
β‘
Prolific Year
(5)
ποΈ
Keyword Collector
(86)
π
Trend Setter
Conferences
NIPS (30)
JMLR (3)
ICLR (2)
AISTATS (1)
ICML (1)
UAI (1)
Top co-authors
Keywords
bayesian inference
(11)
markov chain monte carlo
(8)
gaussian process
(8)
neural network
(5)
bayesian optimization
(4)
latent variable model
(4)
bayesian nonparametrics
(4)
variational inference
(4)
probabilistic modeling
(3)
slice sampling
(3)
convolutional neural network
(3)
generative model
(3)
generalized linear model
(3)
mixture model
(3)
message passing
(2)
hierarchical model
(2)
nonparametric bayesian
(2)
constrained optimization
(2)
topic modeling
(2)
feature extraction
(2)
Papers
Multi-fidelity Monte Carlo: a pseudo-marginal approach
NIPS 2022
Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability
NIPS 2021
Slice Sampling Reparameterization Gradients
NIPS 2021
Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate
NIPS 2021
Active multi-fidelity Bayesian online changepoint detection
UAI 2021
Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters
NIPS 2020
Learning Composable Energy Surrogates for PDE Order Reduction
NIPS 2020
SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
ICLR 2020
On Warm-Starting Neural Network Training
NIPS 2020
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
NIPS 2019
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
ICLR 2019
Discrete Object Generation with Reversible Inductive Construction
NIPS 2019
A Bayesian Nonparametric View on Count-Min Sketch
NIPS 2018
Reducing Reparameterization Gradient Variance
NIPS 2017
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
NIPS 2017
Variational Boosting: Iteratively Refining Posterior Approximations
ICML 2017
Bayesian latent structure discovery from multi-neuron recordings
NIPS 2016
Composing graphical models with neural networks for structured representations and fast inference
NIPS 2016
A General Framework for Constrained Bayesian Optimization using Information-based Search
JMLR 2016
Convolutional Networks on Graphs for Learning Molecular Fingerprints
NIPS 2015
Spectral Representations for Convolutional Neural Networks
NIPS 2015
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation
NIPS 2015
A Gaussian Process Model of Quasar Spectral Energy Distributions
NIPS 2015
A framework for studying synaptic plasticity with neural spike train data
NIPS 2014
Parallel MCMC with Generalized Elliptical Slice Sampling
JMLR 2014
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
NIPS 2013
Contrastive Learning Using Spectral Methods
NIPS 2013
Multi-Task Bayesian Optimization
NIPS 2013
Message Passing Inference with Chemical Reaction Networks
NIPS 2013
Probabilistic n-Choose-k Models for Classification and Ranking
NIPS 2012
Practical Bayesian Optimization of Machine Learning Algorithms
NIPS 2012
Nonparametric Guidance of Autoencoder Representations using Label Information
JMLR 2012
Cardinality Restricted Boltzmann Machines
NIPS 2012
Priors for Diversity in Generative Latent Variable Models
NIPS 2012
Learning the Structure of Deep Sparse Graphical Models
AISTATS 2010
Tree-Structured Stick Breaking for Hierarchical Data
NIPS 2010
Slice sampling covariance hyperparameters of latent Gaussian models
NIPS 2010
The Gaussian Process Density Sampler
NIPS 2008