Matthias Seeger
18 papers · 2002–2024 · 6 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge π Renaissance Researcher (7) πΊοΈ Taxonomy Completionist (15) π£ Hot Topic Early Bird
π
Conference Polyglot
(6)
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Academic Marathon
(22)
πΊοΈ
Taxonomy Completionist
(15)
π
Keyword Trendsetter Combo
(3)
πΊ
Lone Wolf
(3)
π±
Topic Pioneer
π¬
Deep Specialist
(11)
π
Keyword Champion
π
Century Club
(18)
π
Trend Setter
ποΈ
Keyword Collector
(51)
π
Conference Pioneer
π₯
Unstoppable
(5)
Conferences
ICML (5)
NIPS (5)
AISTATS (3)
AUTOML (2)
JMLR (2)
UAI (1)
Top co-authors
Research topics
Keywords
bayesian inference
(7)
variational inference
(5)
gaussian process
(5)
hyperparameter optimization
(5)
expectation propagation
(3)
bayesian optimization
(3)
convex optimization
(2)
surrogate model
(2)
uncertainty quantification
(2)
conformal prediction
(2)
matrix factorization
(2)
approximate inference
(2)
gaussian process classification
(2)
magnetic resonance imaging
(2)
gaussian process regression
(2)
bilinear model
(2)
cross-validation
(1)
text classification
(1)
neural coding
(1)
compressed sensing
(1)
Papers
Fortuna: A Library for Uncertainty Quantification in Deep Learning
JMLR 2024
Explaining Probabilistic Models with Distributional Values
ICML 2024
Optimizing Hyperparameters with Conformal Quantile Regression
ICML 2023
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
AUTOML 2022
Automatic Termination for Hyperparameter Optimization
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
NIPS 2009
Local Gaussian Process Regression for Real Time Online Model Learning
NIPS 2008
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences
NIPS 2008
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
NIPS 2007
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods
NIPS 2006
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
JMLR 2002