David Carlson
18 papers · 2014–2025 · 8 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (42) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
π€
Dynamic Duo
(10)
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Renaissance Researcher
(7)
π₯
Unstoppable Β· Bronze
π
Century Club Β· Bronze
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Trend Setter
ποΈ
Keyword Collector
(99)
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory
(9)
Bayesian & Probabilistic Β· Machine Learning
(7)
Artificial Intelligence
(6)
Core Methods
(6)
Learning Types
(5)
Deep Learning
(4)
Mathematics & Optimization
(4)
Application Areas
(4)
Bayesian & Probabilistic Β· Artificial Intelligence
(3)
Core AI
(3)
Computer Vision
(2)
Natural Language Processing
(2)
Models
(2)
Optimization
(2)
Keywords
bayesian inference
(4)
restricted boltzmann machine
(3)
variational inference
(3)
sigmoid belief network
(3)
topic modeling
(2)
markov chain monte carlo
(2)
posterior sampling
(2)
generative model
(2)
neural network
(2)
gaussian process
(2)
multi-task learning
(1)
ensemble learning
(1)
matrix factorization
(1)
logistic regression
(1)
adversarial learning
(1)
stochastic gradient
(1)
domain adaptation
(1)
expectation maximization
(1)
link prediction
(1)
data augmentation
(1)
Papers
ClinStructor: AI-Powered Structuring of Unstructured Clinical Texts
IJCNLP 2025
Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series
ICML 2025
Estimating Causal Effects using a Multi-task Deep Ensemble
ICML 2023
Learning to Weight Filter Groups for Robust Classification
WACV 2022
Estimating Uncertainty Intervals from Collaborating Networks
JMLR 2021
Attention-Based Network for Weak Labels in Neonatal Seizure Detection
MLHC 2020
Dynamic Embedding on Textual Networks via a Gaussian Process
AAAI 2020
On Target Shift in Adversarial Domain Adaptation
AISTATS 2019
StoryGAN: A Sequential Conditional GAN for Story Visualization
CVPR 2019
Stochastic Bouncy Particle Sampler
ICML 2017
Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
AISTATS 2016
Learning Sigmoid Belief Networks via Monte Carlo Expectation Maximization
AISTATS 2016
Parallel Majorization Minimization with Dynamically Restricted Domains for Nonconvex Optimization
AISTATS 2016
Partition Functions from Rao-Blackwellized Tempered Sampling
ICML 2016
Learning Deep Sigmoid Belief Networks with Data Augmentation
AISTATS 2015
Scalable Deep Poisson Factor Analysis for Topic Modeling
ICML 2015
Stochastic Spectral Descent for Restricted Boltzmann Machines
AISTATS 2015
Latent Gaussian Models for Topic Modeling
AISTATS 2014