Matthias W. Seeger

发表

Bayesian Experimental Design of Magnetic Resonance Imaging Sequences

Bernhard Schölkopf, Matthias W. Seeger, Hannes Nickisch, 2008, NIPS.

Workshop summary: Numerical mathematics in machine learning

Suvrit Sra, John P. Cunningham, Matthias W. Seeger, 2009, ICML '09.

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2011, SIAM J. Imaging Sci..

Bayesian Inference for Sparse Generalized Linear Models

Matthias Bethge, Matthias W. Seeger, Sebastian Gerwinn, 2007, ECML.

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2010, AISTATS.

Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations

Matthias W. Seeger, M. Seeger, 2003 .

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models

Mohammad Emtiyaz Khan, Michael P. Friedlander, Matthias W. Seeger, 2013, ICML.

Real-Time Local GP Model Learning

Jan Peters, Duy Nguyen-Tuong, Matthias W. Seeger, 2010, From Motor Learning to Interaction Learning in Robots.

Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2008, Sampling-based Optimization in the Presence of Uncertainty.

Probabilistic Demand Forecasting at Scale

Joos-Hendrik Böse, Matthias W. Seeger, Tim Januschowski, 2017, Proc. VLDB Endow..

Clustering IT Events around Common Root Causes

Matthias W. Seeger, Iulia Gabriela Carjeu, Thomas Shorrock, 2014, 2014 IEEE International Conference on Services Computing.

Expectation Propagation for Rectified Linear Poisson Regression

Matthias W. Seeger, Young-Jun Ko, M. Seeger, 2015, ACML.

Gaussian Covariance and Scalable Variational Inference

Matthias W. Seeger, M. Seeger, 2010, ICML.

Bayesian Optimization with Tree-structured Dependencies

Matthias W. Seeger, Javier González, Cédric Archambeau, 2017, ICML.

Local Gaussian Process Regression for Real Time Online Model Learning

Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters, 2008, NIPS.

Gaussian Process Bandits without Regret: An Experimental Design Approach pdf

Andreas Krause, Sham M. Kakade, Matthias W. Seeger, 2009, ArXiv.

Scalable Hyperparameter Transfer Learning

Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton, 2018, NeurIPS.

Gaussian Processes For Machine Learning

Matthias W. Seeger, M. Seeger, Matthias W. Seeger, 2004, Int. J. Neural Syst..

Fast Gaussian Process Regression using KD-Trees

Andrew Y. Ng, Matthias W. Seeger, Yirong Shen, 2005, NIPS.

Variational Bayesian Inference Techniques

David P. Wipf, Matthias W. Seeger, M. Seeger, 2010, IEEE Signal Processing Magazine.

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models

Matthias W. Seeger, Young-Jun Ko, M. Seeger, 2012, ICML.

Information Consistency of Nonparametric Gaussian Process Methods

Sham M. Kakade, Dean P. Foster, Matthias W. Seeger, 2008, IEEE Transactions on Information Theory.

Auto-Differentiating Linear Algebra pdf

Neil D. Lawrence, Zhenwen Dai, Matthias W. Seeger, 2017, ArXiv.

Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing

Matthias W. Seeger, 2009, NIPS.

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting

Andreas Krause, Sham M. Kakade, Matthias W. Seeger, 2009, IEEE Transactions on Information Theory.

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression

Neil D. Lawrence, Matthias W. Seeger, Christopher K. I. Williams, 2003, AISTATS.

Covariance Kernels from Bayesian Generative Models

Matthias W. Seeger, M. Seeger, 2001, NIPS.

Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models

Guillaume Bouchard, Matthias W. Seeger, Guillaume Bouchard, 2012, AISTATS.

Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models

Florian Steinke, Matthias W. Seeger, Koji Tsuda, 2006, BMC Systems Biology.

Using the Nyström Method to Speed Up Kernel Machines

Matthias W. Seeger, Christopher K. I. Williams, M. Seeger, 2000, NIPS.

Scalable Collaborative Bayesian Preference Learning

Mohammad Emtiyaz Khan, Matthias W. Seeger, Young-Jun Ko, 2014, AISTATS.

Deep State Space Models for Time Series Forecasting

Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, 2018, NeurIPS.

An Improved Predictive Accuracy Bound for Averaging Classifiers

John Langford, Nimrod Megiddo, Matthias W. Seeger, 2001, ICML.

Bayesian Intermittent Demand Forecasting for Large Inventories

Matthias W. Seeger, Valentin Flunkert, David Salinas, 2016, NIPS.

09181 Working Group on Hybridization between R&S, DoE and Optimization

Chun-Hung Chen, Paul B. Kantor, David P. Morton, 2009, Sampling-based Optimization in the Presence of Uncertainty.

PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification

Matthias W. Seeger, M. Seeger, 2003, J. Mach. Learn. Res..

Compressed sensing and Bayesian experimental design

Matthias W. Seeger, Hannes Nickisch, H. Nickisch, 2008, ICML '08.

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods

Matthias W. Seeger, 2006, NIPS.

Fast Sparse Gaussian Process Methods: The Informative Vector Machine

Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich, 2002, NIPS.

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior

Matthias Bethge, Matthias W. Seeger, Jakob H. Macke, 2007, NIPS.

Semiparametric latent factor models

Yee Whye Teh, Michael I. Jordan, Matthias W. Seeger, 2005, AISTATS.

Learning Inverse Dynamics: a Comparison

Bernhard Schölkopf, Jan Peters, Duy Nguyen-Tuong, 2008, ESANN.

Convex variational Bayesian inference for large scale generalized linear models

Matthias W. Seeger, Hannes Nickisch, 2009, ICML '09.

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods

Matthias W. Seeger, M. Seeger, 2008, J. Mach. Learn. Res..

Worst-Case Bounds for Gaussian Process Models

Sham M. Kakade, Dean P. Foster, Matthias W. Seeger, 2005, NIPS.

Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers

Matthias W. Seeger, M. Seeger, 1999, NIPS.

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale pdf

Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, 2017, ArXiv.

Model Learning with Local Gaussian Process Regression

Jan Peters, Duy Nguyen-Tuong, Matthias W. Seeger, 2009, Adv. Robotics.

Bayesian Inference and Optimal Design in the Sparse Linear Model

Florian Steinke, Matthias W. Seeger, Koji Tsuda, 2007, AISTATS.