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ORCIDMatthias W. Seeger
Person information
- affiliation: Amazon Development Center Germany, Berlin
- affiliation: École Polytechnique Fédérale de Lausanne, School of Computer and Communication Sciences
- affiliation: Max Planck Institute for Informatics, Saarbrücken
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2020 – today
- 2024
[i19]Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger:
Explaining Probabilistic Models with Distributional Values. CoRR abs/2402.09947 (2024)- 2023
- [c43]David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. ICML 2023: 29876-29893 - [c42]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: 11-24 - [i18]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR abs/2302.04019 (2023) - [i17]David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau:
Optimizing Hyperparameters with Conformal Quantile Regression. CoRR abs/2305.03623 (2023) - 2022
- [c41]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: 7/1-21 - [c40]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: 16/1-23 - 2021
- [c39]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. ICML 2021: 10289-10300 - [c38]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: 3463-3471 - [c37]Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger:
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI 2021: 568-577 - [i16]Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos:
BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR abs/2102.09009 (2021) - [i15]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 abs/2104.08166 (2021) - [i14]Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger:
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. CoRR abs/2106.06079 (2021) - [i13]Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau:
Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR abs/2111.03418 (2021) - 2020
- [c36]Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau:
LEEP: A New Measure to Evaluate Transferability of Learned Representations. ICML 2020: 7294-7305 - [i12]Cuong V. Nguyen, Tal Hassner, Cédric Archambeau, Matthias W. Seeger:
LEEP: A New Measure to Evaluate Transferability of Learned Representations. CoRR abs/2002.12462 (2020) - [i11]Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger:
Model-based Asynchronous Hyperparameter Optimization. CoRR abs/2003.10865 (2020) - [i10]Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger:
Cost-aware Bayesian Optimization. CoRR abs/2003.10870 (2020) - [i9]Piali Das, Valerio Perrone, Nikita Ivkin, Tanya Bansal, Zohar S. Karnin, Huibin Shen, Iaroslav Shcherbatyi, Yotam Elor, Wilton Wu, Aida Zolic, Thibaut Liénart, 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 abs/2012.08483 (2020) - [i8]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 abs/2012.08489 (2020)
2010 – 2019
- 2019
- [i7]Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton:
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR abs/1909.12552 (2019) - [i6]Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger:
Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR abs/1910.07003 (2019) - 2018
- [c35]Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau:
Scalable Hyperparameter Transfer Learning. NeurIPS 2018: 6846-6856 - [c34]Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski:
Deep State Space Models for Time Series Forecasting. NeurIPS 2018: 7796-7805 - 2017
- [j11]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): 1694-1705 (2017) - [c33]Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger:
Bayesian Optimization with Tree-structured Dependencies. ICML 2017: 1655-1664 - [i5]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 abs/1709.07638 (2017) - [i4]Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence:
Auto-Differentiating Linear Algebra. CoRR abs/1710.08717 (2017) - 2016
- [c32]Matthias W. Seeger, David Salinas, Valentin Flunkert:
Bayesian Intermittent Demand Forecasting for Large Inventories. NIPS 2016: 4646-4654 - 2015
- [c31]Young-Jun Ko, Matthias W. Seeger:
Expectation Propagation for Rectified Linear Poisson Regression. ACML 2015: 253-268 - 2014
- [c30]Iulia Gabriela Carjeu, Thomas Shorrock, Matthias W. Seeger:
Clustering IT Events around Common Root Causes. IEEE SCC 2014: 749-757 - [c29]Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger:
Scalable Collaborative Bayesian Preference Learning. AISTATS 2014: 475-483 - 2013
- [c28]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: 951-959 - 2012
- [j10]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): 3250-3265 (2012) - [c27]Young-Jun Ko, Matthias W. Seeger:
Large Scale Variational Bayesian Inference for Structured Scale Mixture Models. ICML 2012 - [c26]Matthias W. Seeger, Guillaume Bouchard:
Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models. AISTATS 2012: 1012-1018 - 2011
- [j9]Matthias W. Seeger, Hannes Nickisch:
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. SIAM J. Imaging Sci. 4(1): 166-199 (2011) - [c25]Matthias W. Seeger, Hannes Nickisch:
Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. AISTATS 2011: 652-660 - 2010
- [j8]Matthias W. Seeger, David P. Wipf:
Variational Bayesian Inference Techniques. IEEE Signal Process. Mag. 27(6): 81-91 (2010) - [c24]Matthias W. Seeger:
Gaussian Covariance and Scalable Variational Inference. ICML 2010: 967-974 - [c23]Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger:
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. ICML 2010: 1015-1022 - [p2]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Real-Time Local GP Model Learning. From Motor Learning to Interaction Learning in Robots 2010: 193-207
2000 – 2009
- 2009
- [j7]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Model Learning with Local Gaussian Process Regression. Adv. Robotics 23(15): 2015-2034 (2009) - [c22]Matthias W. Seeger, Suvrit Sra, John P. Cunningham:
Workshop summary: Numerical mathematics in machine learning. ICML 2009: 9 - [c21]Hannes Nickisch, Matthias W. Seeger:
Convex variational Bayesian inference for large scale generalized linear models. ICML 2009: 761-768 - [c20]Matthias W. Seeger:
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing. NIPS 2009: 1633-1641 - [i3]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 - [i2]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 - [i1]Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger:
Gaussian Process Bandits without Regret: An Experimental Design Approach. CoRR abs/0912.3995 (2009) - 2008
- [j6]Matthias W. Seeger:
Bayesian Inference and Optimal Design for the Sparse Linear Model. J. Mach. Learn. Res. 9: 759-813 (2008) - [j5]Matthias W. Seeger:
Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods. J. Mach. Learn. Res. 9: 1147-1178 (2008) - [j4]Matthias W. Seeger, Sham M. Kakade, Dean P. Foster:
Information Consistency of Nonparametric Gaussian Process Methods. IEEE Trans. Inf. Theory 54(5): 2376-2382 (2008) - [c19]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Computed torque control with nonparametric regression models. ACC 2008: 212-217 - [c18]Duy Nguyen-Tuong, Jan Peters, Matthias W. Seeger, Bernhard Schölkopf:
Learning Inverse Dynamics: a Comparison. ESANN 2008: 13-18 - [c17]Matthias W. Seeger, Hannes Nickisch:
Compressed sensing and Bayesian experimental design. ICML 2008: 912-919 - [c16]Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters:
Local Gaussian Process Regression for Real Time Online Model Learning. NIPS 2008: 1193-1200 - [c15]Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf:
Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. NIPS 2008: 1441-1448 - 2007
- [j3]Florian Steinke, Matthias W. Seeger, Koji Tsuda:
Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Syst. Biol. 1: 51 (2007) - [c14]Matthias W. Seeger, Sebastian Gerwinn, Matthias Bethge:
Bayesian Inference for Sparse Generalized Linear Models. ECML 2007: 298-309 - [c13]Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge:
Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. NIPS 2007: 529-536 - [c12]Matthias W. Seeger, Florian Steinke, Koji Tsuda:
Bayesian Inference and Optimal Design in the Sparse Linear Model. AISTATS 2007: 444-451 - 2006
- [c11]Matthias W. Seeger:
Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. NIPS 2006: 1233-1240 - [p1]Matthias W. Seeger:
A Taxonomy for Semi-Supervised Learning Methods. Semi-Supervised Learning 2006: 14-31 - 2005
- [c10]Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan:
Semiparametric latent factor models. AISTATS 2005: 333-340 - [c9]Sham M. Kakade, Matthias W. Seeger, Dean P. Foster:
Worst-Case Bounds for Gaussian Process Models. NIPS 2005: 619-626 - [c8]Yirong Shen, Andrew Y. Ng, Matthias W. Seeger:
Fast Gaussian Process Regression using KD-Trees. NIPS 2005: 1225-1232 - 2004
- [j2]Matthias W. Seeger:
Gaussian Processes For Machine Learning. Int. J. Neural Syst. 14(2): 69-106 (2004) - 2003
- [b1]Matthias W. Seeger:
Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations. University of Edinburgh, UK, 2003 - [c7]Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence:
Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS 2003: 254-261 - 2002
- [j1]Matthias W. Seeger:
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. J. Mach. Learn. Res. 3: 233-269 (2002) - [c6]Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich:
Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS 2002: 609-616 - 2001
- [c5]John Langford, Matthias W. Seeger, Nimrod Megiddo:
An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML 2001: 290-297 - [c4]Matthias W. Seeger:
Covariance Kernels from Bayesian Generative Models. NIPS 2001: 905-912 - 2000
- [c3]Christopher K. I. Williams, Matthias W. Seeger:
The Effect of the Input Density Distribution on Kernel-based Classifiers. ICML 2000: 1159-1166 - [c2]Christopher K. I. Williams, Matthias W. Seeger:
Using the Nyström Method to Speed Up Kernel Machines. NIPS 2000: 682-688
1990 – 1999
- 1999
- [c1]Matthias W. Seeger:
Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. NIPS 1999: 603-609
Coauthor Index
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last updated on 2024-07-06 23:41 CEST by the dblp team
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