Matthias W. Seeger

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 29

Top Venues

CoRR

ICML

AutoML

NeurIPS

University of Edinburgh, UK (PHD)

Education History

University of Edinburgh, UK

PHD

Publications

Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
Explaining Probabilistic Models with Distributional Values. CoRR (2024)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR (2023)
Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H (2023)
David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
Optimizing Hyperparameters with Conformal Quantile Regression. ICML (2023)
Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR (2023)
David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML (2022)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Automatic Termination for Hyperparameter Optimization. AutoML (2022)
Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR (2021)
Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML (2021)
Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR (2021)
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. CoRR (2021)
Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR (2021)
Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI (2021)
Valerio Perrone, Huibin Shen, Aida Zolic, Iaroslav Shcherbatyi, Amr Ahmed, Tanya Bansal, Michele Donini, Fela Winkelmolen, Rodolphe Jenatton, Jean Baptiste Faddoul, Barbara Pogorzelska, Miroslav Miladinovic, Krishnaram Kenthapadi, Matthias W. Seeger, Cédric Archambeau
Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR (2020)
Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger
Cost-aware Bayesian Optimization. CoRR (2020)
Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger
Model-based Asynchronous Hyperparameter Optimization. CoRR (2020)
Cuong Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau
LEEP: A New Measure to Evaluate Transferability of Learned Representations. ICML (2020)
Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Lienart, Alex Tang, Amr Ahmed, Jean Baptiste Faddoul, Rodolphe Jenatton, Fela Winkelmolen, Philip Gautier, Leo Dirac, Andre Perunicic, Miroslav Miladinovic, Giovanni Zappella, Cédric Archambeau, Matthias W. Seeger, Bhaskar Dutt, Laurence Rouesnel
Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR (2020)
Cuong V. Nguyen, Tal Hassner, Cédric Archambeau, Matthias W. Seeger
LEEP: A New Measure to Evaluate Transferability of Learned Representations. CoRR (2020)
Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR (2019)
Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR (2019)
Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau
Scalable Hyperparameter Transfer Learning. NeurIPS (2018)
Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
Deep State Space Models for Time Series Forecasting. NeurIPS (2018)
Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR (2017)
Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang
Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. 10 (12) (2017)
Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger
Bayesian Optimization with Tree-structured Dependencies. ICML (2017)
Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence
Auto-Differentiating Linear Algebra. CoRR (2017)
Matthias W. Seeger, David Salinas, Valentin Flunkert
Bayesian Intermittent Demand Forecasting for Large Inventories. NIPS (2016)
Young-Jun Ko, Matthias W. Seeger
Expectation Propagation for Rectified Linear Poisson Regression. ACML (2015)
Iulia Gabriela Carjeu, Thomas Shorrock, Matthias W. Seeger
Clustering IT Events around Common Root Causes. IEEE SCC (2014)
Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger
Scalable Collaborative Bayesian Preference Learning. AISTATS (2014)
Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger
Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML (3) (2013)
Young-Jun Ko, Matthias W. Seeger
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models. ICML (2012)
Matthias W. Seeger, Guillaume Bouchard
Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models. AISTATS (2012)
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. IEEE Trans. Inf. Theory 58 (5) (2012)
Matthias W. Seeger, Hannes Nickisch
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. SIAM J. Imaging Sci. 4 (1) (2011)
Matthias W. Seeger, Hannes Nickisch
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. AISTATS (2011)
Matthias W. Seeger
Gaussian Covariance and Scalable Variational Inference. ICML (2010)
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. ICML (2010)
Matthias W. Seeger, David P. Wipf
Variational Bayesian Inference Techniques. IEEE Signal Process. Mag. 27 (6) (2010)
Hannes Nickisch, Matthias W. Seeger
Convex variational Bayesian inference for large scale generalized linear models. ICML (2009)
Matthias W. Seeger, Hannes Nickisch
Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. Sampling-based Optimization in the Presence of Uncertainty (2009)
Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
Model Learning with Local Gaussian Process Regression. Adv. Robotics 23 (15) (2009)
Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger
Gaussian Process Bandits without Regret: An Experimental Design Approach CoRR (2009)
Chun-Hung Chen, Liu Hong, Paul B. Kantor, David P. Morton, Juta Pichitlamken, Matthias W. Seeger
09181 Working Group on Hybridization between R&S, DoE and Optimization. Sampling-based Optimization in the Presence of Uncertainty (2009)
Matthias W. Seeger
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing. NIPS (2009)
Matthias W. Seeger, Suvrit Sra, John P. Cunningham
Workshop summary: Numerical mathematics in machine learning. ICML (2009)
Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
Computed torque control with nonparametric regression models. ACC (2008)
Matthias W. Seeger
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods. J. Mach. Learn. Res. 9 (2008)
Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
Local Gaussian Process Regression for Real Time Online Model Learning. NIPS (2008)
Matthias W. Seeger, Hannes Nickisch
Compressed sensing and Bayesian experimental design. ICML (2008)
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. NIPS (2008)
Duy Nguyen-Tuong, Jan Peters, Matthias W. Seeger, Bernhard Schölkopf
Learning Inverse Dynamics: a Comparison. ESANN (2008)
Matthias W. Seeger
Bayesian Inference and Optimal Design for the Sparse Linear Model. J. Mach. Learn. Res. 9 (2008)
Matthias W. Seeger, Sham M. Kakade, Dean P. Foster
Information Consistency of Nonparametric Gaussian Process Methods. IEEE Trans. Inf. Theory 54 (5) (2008)
Matthias W. Seeger, Florian Steinke, Koji Tsuda
Bayesian Inference and Optimal Design in the Sparse Linear Model. AISTATS (2007)
Florian Steinke, Matthias W. Seeger, Koji Tsuda
Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Syst. Biol. 1 (2007)
Matthias W. Seeger, Sebastian Gerwinn, Matthias Bethge
Bayesian Inference for Sparse Generalized Linear Models. ECML (2007)
Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. NIPS (2007)
Matthias W. Seeger
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. NIPS (2006)
Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan
Semiparametric latent factor models. AISTATS (2005)
Sham M. Kakade, Matthias W. Seeger, Dean P. Foster
Worst-Case Bounds for Gaussian Process Models. NIPS (2005)
Yirong Shen, Andrew Y. Ng, Matthias W. Seeger
Fast Gaussian Process Regression using KD-Trees. NIPS (2005)
Matthias W. Seeger
Gaussian Processes For Machine Learning. Int. J. Neural Syst. 14 (2) (2004)
Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS (2003)
Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich
Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS (2002)
Matthias W. Seeger
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. J. Mach. Learn. Res. 3 (2002)
John Langford, Matthias W. Seeger, Nimrod Megiddo
An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML (2001)
Matthias W. Seeger
Covariance Kernels from Bayesian Generative Models. NIPS (2001)
Christopher K. I. Williams, Matthias W. Seeger
Using the Nyström Method to Speed Up Kernel Machines. NIPS (2000)
Christopher K. I. Williams, Matthias W. Seeger
The Effect of the Input Density Distribution on Kernel-based Classifiers. ICML (2000)
Matthias W. Seeger
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. NIPS (1999)

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Publication Activity (10 Years)

Top Topics

Top Venues

Matthias W. Seeger