Seeger, Matthias

25 publications

ICML 2024 Explaining Probabilistic Models with Distributional Values Luca Franceschi, Michele Donini, Cedric Archambeau, Matthias Seeger

MLOSS 2024 Fortuna: A Library for Uncertainty Quantification in Deep Learning Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau

ICLRW 2023 Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cedric Archambeau, Tamas Madl, Michele Donini, Matthias Seeger

ICML 2023 Optimizing Hyperparameters with Conformal Quantile Regression David Salinas, Jacek Golebiowski, Aaron Klein, Matthias Seeger, Cedric Archambeau

AutoML 2022 Automatic Termination for Hyperparameter Optimization Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau

AutoML 2022 Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research David Salinas, Matthias Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cedric Archambeau

UAI 2021 A Nonmyopic Approach to Cost-Constrained Bayesian Optimization Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

ICML 2020 LEEP: A New Measure to Evaluate Transferability of Learned Representations Cuong Nguyen, Tal Hassner, Matthias Seeger, Cedric Archambeau

ICML 2017 Bayesian Optimization with Tree-Structured Dependencies Rodolphe Jenatton, Cedric Archambeau, Javier González, Matthias Seeger

ICML 2013 Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models Mohammad Emtiyaz Khan, Aleksandr Aravkin, Michael Friedlander, Matthias Seeger

AISTATS 2012 Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models Matthias Seeger, Guillaume Bouchard

AISTATS 2011 Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference Matthias Seeger, Hannes Nickisch

NeurIPS 2009 Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing Matthias Seeger

NeurIPS 2008 Bayesian Experimental Design of Magnetic Resonance Imaging Sequences Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf, Matthias Seeger

NeurIPS 2008 Local Gaussian Process Regression for Real Time Online Model Learning Duy Nguyen-tuong, Jan R. Peters, Matthias Seeger

AISTATS 2007 Bayesian Inference and Optimal Design in the Sparse Linear Model Matthias Seeger, Florian Steinke, Koji Tsuda

NeurIPS 2007 Bayesian Inference for Spiking Neuron Models with a Sparsity Prior Sebastian Gerwinn, Matthias Bethge, Jakob H. Macke, Matthias Seeger

NeurIPS 2006 Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods Matthias Seeger

NeurIPS 2005 Fast Gaussian Process Regression Using KD-Trees Yirong Shen, Matthias Seeger, Andrew Y. Ng

AISTATS 2005 Semiparametric Latent Factor Models Yee Whye Teh, Matthias Seeger, Michael I. Jordan

NeurIPS 2002 Fast Sparse Gaussian Process Methods: The Informative Vector Machine Neil D. Lawrence, Matthias Seeger, Ralf Herbrich

JMLR 2002 PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification (Kernel Machines Section) Matthias Seeger

NeurIPS 2001 Covariance Kernels from Bayesian Generative Models Matthias Seeger

NeurIPS 2000 Using the Nyström Method to Speed up Kernel Machines Christopher K. I. Williams, Matthias Seeger

NeurIPS 1999 Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers Matthias Seeger