Matthias Seeger
19 papers · 1999–2024 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (40) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
π±
Topic Pioneer
π
Keyword Trendsetter Combo
(10)
π¬
Deep Specialist
(13)
π
Keyword Champion
(2)
π§
Keyword Pioneer
π
Conference Pioneer
π₯
Unstoppable Β· Bronze
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Century Club Β· Bronze
ποΈ
Keyword Collector
(97)
π
Renaissance Researcher
(9)
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Trend Setter
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory
(16)
Artificial Intelligence
(14)
Bayesian & Probabilistic Β· Artificial Intelligence
(9)
Bayesian & Probabilistic Β· Machine Learning
(8)
Core Methods
(7)
Learning Paradigms
(4)
Application Areas
(3)
Core AI
(3)
Learning Types
(3)
Computer Vision
(2)
Healthcare & Medicine
(2)
Mathematics & Optimization
(2)
Clinical
(2)
Domain-Specific
(2)
Keywords
bayesian inference
(7)
variational inference
(5)
hyperparameter optimization
(5)
gaussian process
(5)
expectation propagation
(3)
bayesian optimization
(3)
convex optimization
(2)
surrogate model
(2)
gaussian process regression
(2)
conformal prediction
(2)
matrix factorization
(2)
approximate inference
(2)
gaussian process classification
(2)
magnetic resonance imaging
(2)
uncertainty quantification
(2)
bilinear model
(2)
hierarchical classification
(1)
text classification
(1)
neural coding
(1)
compressed sensing
(1)
Papers
Explaining Probabilistic Models with Distributional Values
ICML 2024
Fortuna: A Library for Uncertainty Quantification in Deep Learning
JMLR 2024
Optimizing Hyperparameters with Conformal Quantile Regression
ICML 2023
Automatic Termination for Hyperparameter Optimization
AutoML 2022
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
AutoML 2022
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
UAI 2021
LEEP: A New Measure to Evaluate Transferability of Learned Representations
ICML 2020
Bayesian Optimization with Tree-structured Dependencies
ICML 2017
Scalable Collaborative Bayesian Preference Learning
AISTATS 2014
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models
ICML 2013
Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models
AISTATS 2012
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference
AISTATS 2011
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing
NeurIPS 2009
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences
NeurIPS 2008
Local Gaussian Process Regression for Real Time Online Model Learning
NeurIPS 2008
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
NeurIPS 2007
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
NeurIPS 2006
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
JMLR 2002
Bayesian Inference and Optimal Design in the Sparse Linear Model
AISTATS 1999