Jonathan Huggins
13 papers · 2009–2021 · 4 venues · across top CS/AI venues
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Achievements
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π Interdisciplinary Bridge Β· Bronze π Conference Polyglot Β· Bronze π Academic Marathon Β· Half π£ Hot Topic Early Bird πΊοΈ Taxonomy Completionist (23)
π±
Topic Pioneer
π¬
Deep Specialist
(10)
π
Trend Setter
π
Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
Venues
Top co-authors
Research topics
Machine Learning
(12)
Optimization & Theory
(12)
Artificial Intelligence
(5)
Bayesian & Probabilistic Β· Artificial Intelligence
(5)
Bayesian & Probabilistic Β· Machine Learning
(4)
Mathematics & Optimization
(3)
Core Methods
(3)
Deep Learning
(2)
Learning Types
(2)
Mathematics
(2)
Models
(2)
Healthcare & Medicine
(1)
Optimization
(1)
Research
(1)
Keywords
bayesian inference
(6)
markov chain monte carlo
(6)
posterior approximation
(3)
laplace approximation
(2)
variational inference
(2)
importance sampling
(2)
black-box variational inference
(2)
logistic regression
(2)
posterior inference
(2)
generalized linear model
(2)
uncertainty quantification
(2)
low-rank approximation
(1)
kernel approximation
(1)
stochastic optimization
(1)
high-dimensional inference
(1)
high-dimensional regression
(1)
stochastic gradient descent
(1)
diffusion process
(1)
feature selection
(1)
gaussian process
(1)
Papers
Challenges and Opportunities in High Dimensional Variational Inference
NeurIPS 2021
Robust, Accurate Stochastic Optimization for Variational Inference
NeurIPS 2020
Validated Variational Inference via Practical Posterior Error Bounds
AISTATS 2020
Data-dependent compression of random features for large-scale kernel approximation
AISTATS 2019
LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations
ICML 2019
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions
ICML 2019
Random Feature Stein Discrepancies
NeurIPS 2018
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference
NeurIPS 2017
Quantifying the accuracy of approximate diffusions and Markov chains
AISTATS 2017
Coresets for Scalable Bayesian Logistic Regression
NeurIPS 2016
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
ICML 2015
Risk and Regret of Hierarchical Bayesian Learners
ICML 2015
A simple feature-copying approach for long-distance dependencies
CoNLL 2009