# Matthias W. Seeger

According to our database

Collaborative distances:

^{1}, Matthias W. Seeger authored at least 49 papers between 1999 and 2018.Collaborative distances:

## Timeline

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## Bibliography

2018

Deep State Space Models for Time Series Forecasting.

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Scalable Hyperparameter Transfer Learning.

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Probabilistic Demand Forecasting at Scale.

PVLDB, 2017

Bayesian Optimization with Tree-structured Dependencies.

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Bayesian Intermittent Demand Forecasting for Large Inventories.

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Expectation Propagation for Rectified Linear Poisson Regression.

Proceedings of The 7th Asian Conference on Machine Learning, 2015

2014

Scalable Collaborative Bayesian Preference Learning.

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Clustering IT Events around Common Root Causes.

Proceedings of the IEEE International Conference on Services Computing, SCC 2014, Anchorage, AK, USA, June 27, 2014

2013

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models.

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

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

IEEE Trans. Information Theory, 2012

Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models.

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models.

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models.

SIAM J. Imaging Sciences, 2011

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference.

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

2010

Real-Time Local GP Model Learning.

Proceedings of the From Motor Learning to Interaction Learning in Robots, 2010

Variational Bayesian Inference Techniques.

IEEE Signal Process. Mag., 2010

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Gaussian Covariance and Scalable Variational Inference.

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

2009

Model Learning with Local Gaussian Process Regression.

Advanced Robotics, 2009

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

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Workshop summary: Numerical mathematics in machine learning.

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Convex variational Bayesian inference for large scale generalized linear models.

Proceedings of the 26th Annual International Conference on Machine Learning, 2009

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

Proceedings of the Sampling-based Optimization in the Presence of Uncertainty, 26.04., 2009

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

Proceedings of the Sampling-based Optimization in the Presence of Uncertainty, 26.04., 2009

2008

Information Consistency of Nonparametric Gaussian Process Methods.

IEEE Trans. Information Theory, 2008

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods.

Journal of Machine Learning Research, 2008

Bayesian Inference and Optimal Design for the Sparse Linear Model.

Journal of Machine Learning Research, 2008

Bayesian Experimental Design of Magnetic Resonance Imaging Sequences.

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Local Gaussian Process Regression for Real Time Online Model Learning.

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Compressed sensing and Bayesian experimental design.

Proceedings of the Machine Learning, 2008

Learning Inverse Dynamics: a Comparison.

Proceedings of the ESANN 2008, 2008

2007

Bayesian Inference and Optimal Design in the Sparse Linear Model.

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

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

BMC Systems Biology, 2007

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior.

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Bayesian Inference for Sparse Generalized Linear Models.

Proceedings of the Machine Learning: ECML 2007, 2007

2006

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods.

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005

Fast Gaussian Process Regression using KD-Trees.

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Worst-Case Bounds for Gaussian Process Models.

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Semiparametric latent factor models.

Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005

2004

Gaussian Processes For Machine Learning.

Int. J. Neural Syst., 2004

2003

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

PhD thesis, 2003

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression.

Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003

2002

PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification.

Journal of Machine Learning Research, 2002

Fast Sparse Gaussian Process Methods: The Informative Vector Machine.

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

Covariance Kernels from Bayesian Generative Models.

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

An Improved Predictive Accuracy Bound for Averaging Classifiers.

Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

2000

Using the Nyström Method to Speed Up Kernel Machines.

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

The Effect of the Input Density Distribution on Kernel-based Classifiers.

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

1999

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

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999