Matthias W. Seeger

Affiliations:
  • Amazon Development Center Germany, Berlin
  • École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences
  • Max Planck Institute for Informatics, Saarbrücken


According to our database1, Matthias W. Seeger authored at least 73 papers between 1999 and 2024.

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Bibliography

2024
Fortuna: A Library for Uncertainty Quantification in Deep Learning.
J. Mach. Learn. Res., 2024

Hyperparameter Optimization in Machine Learning.
CoRR, 2024

Explaining Probabilistic Models with Distributional Values.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography.
Proceedings of the Trustworthy Machine Learning for Healthcare, 2023

Optimizing Hyperparameters with Conformal Quantile Regression.
Proceedings of the International Conference on Machine Learning, 2023

2022
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research.
Proceedings of the International Conference on Automated Machine Learning, 2022

Automatic Termination for Hyperparameter Optimization.
Proceedings of the International Conference on Automated Machine Learning, 2022

2021
Meta-Forecasting by combining Global Deep Representations with Local Adaptation.
CoRR, 2021

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution.
CoRR, 2021

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

BORE: Bayesian Optimization by Density-Ratio Estimation.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization.
CoRR, 2020

Amazon SageMaker Autopilot: a white box AutoML solution at scale.
CoRR, 2020

Cost-aware Bayesian Optimization.
CoRR, 2020

Model-based Asynchronous Hyperparameter Optimization.
CoRR, 2020

LEEP: A New Measure to Evaluate Transferability of Learned Representations.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Constrained Bayesian Optimization with Max-Value Entropy Search.
CoRR, 2019

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning.
CoRR, 2019

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.
Proc. VLDB Endow., 2017

Auto-Differentiating Linear Algebra.
CoRR, 2017

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale.
CoRR, 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. Inf. 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 Sci., 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
Gaussian Process Bandits without Regret: An Experimental Design Approach
CoRR, 2009

Model Learning with Local Gaussian Process Regression.
Adv. 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. Inf. Theory, 2008

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods.
J. Mach. Learn. Res., 2008

Bayesian Inference and Optimal Design for the Sparse Linear Model.
J. Mach. Learn. Res., 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 16th European Symposium on Artificial Neural Networks, 2008

Computed torque control with nonparametric regression models.
Proceedings of the American Control Conference, 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 Syst. Biol., 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

A Taxonomy for Semi-Supervised Learning Methods.
Proceedings of the Semi-Supervised Learning, 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.
J. Mach. Learn. Res., 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


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