Lawrence K. Saul
17 papers · 2003–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Renaissance Researcher (7) π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (15) π£ Hot Topic Early Bird
π
Academic Marathon
(22)
πΊοΈ
Taxonomy Completionist
(15)
π§
Keyword Pioneer
π
Keyword Trendsetter Combo
(5)
π±
Topic Pioneer
π
Keyword Champion
ποΈ
Keyword Collector
(80)
π
Trend Setter
π
Century Club
(17)
π₯
Unstoppable
(5)
π
Conference Pioneer
Conferences
NIPS (9)
JMLR (3)
AISTATS (2)
ICML (1)
NSDI (1)
UAI (1)
Top co-authors
Keywords
dimensionality reduction
(4)
variational inference
(3)
convex optimization
(2)
semidefinite programming
(2)
gaussian approximation
(2)
bayesian inference
(2)
distance metric learning
(2)
latent variable model
(2)
manifold learning
(2)
matrix factorization
(1)
large-scale optimization
(1)
probabilistic modeling
(1)
metric learning
(1)
regularization
(1)
graph laplacian
(1)
online learning
(1)
margin-based learning
(1)
supervised learning
(1)
automatic speech recognition
(1)
uncertainty quantification
(1)
Papers
Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
JMLR 2025
Batch, match, and patch: low-rank approximations for score-based variational inference
AISTATS 2025
Variational Inference in Location-Scale Families: Exact Recovery of the Mean and Correlation Matrix
AISTATS 2025
EigenVI: score-based variational inference with orthogonal function expansions
NIPS 2024
Batch and match: black-box variational inference with a score-based divergence
ICML 2024
The Shrinkage-Delinkage Trade-off: an Analysis of Factorized Gaussian Approximations for Variational Inference
UAI 2023
Variational Inference with Gaussian Score Matching
NIPS 2023
An online passive-aggressive algorithm for difference-of-squares classification
NIPS 2021
eDoctor: Automatically Diagnosing Abnormal Battery Drain Issues on Smartphones
NSDI 2013
Latent Coincidence Analysis: A Hidden Variable Model for Distance Metric Learning
NIPS 2012
Maximum Covariance Unfolding : Manifold Learning for Bimodal Data
NIPS 2011
Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development
NIPS 2010
Kernel Methods for Deep Learning
NIPS 2009
Distance Metric Learning for Large Margin Nearest Neighbor Classification
JMLR 2009
Graph Laplacian Regularization for Large-Scale Semidefinite Programming
NIPS 2006
Large Margin Hidden Markov Models for Automatic Speech Recognition
NIPS 2006
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds
JMLR 2003