← Authors

Matthias Seeger

18 papers · 2002–2024 · 6 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (7) 🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (6) πŸƒ Academic Marathon (22) πŸ—ΊοΈ Taxonomy Completionist (15) 🌟 Keyword Trendsetter Combo (3) 🐺 Lone Wolf (3) 🌱 Topic Pioneer πŸ”¬ Deep Specialist (11) πŸ† Keyword Champion πŸ’Ž Century Club (18) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (51) πŸš€ Conference Pioneer πŸ”₯ Unstoppable (5)

Conferences

ICML (5) NIPS (5) AISTATS (3) AUTOML (2) JMLR (2) UAI (1)

Top co-authors

CΓ©dric Archambeau (7) Valerio Perrone (3) Aaron Klein (3) Michele Donini (2) Mohammad Emtiyaz Khan (2) David Salinas (2) Hannes Nickisch (2) Jean Baptiste Faddoul (1) Anastasia Makarova (1) Eric Hans Lee (1)

Research topics

Robotics (1)

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