Matthias W. Seeger

80 papers A* 16A 7B 1C 2Journal 31Unranked 20

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Hyperparameter Optimization in Machine Learning. Found. Trends Mach. Learn. Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2024	A*	`conf`	Explaining Probabilistic Models with Distributional Values. ICML Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024	J	`jnl`	Explaining Probabilistic Models with Distributional Values. CoRR Luca Franceschi, Michele Donini, Cédric Archambeau, Matthias W. Seeger
2024	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2024	J	`jnl`	Hyperparameter Optimization in Machine Learning. CoRR Luca Franceschi, Michele Donini, Valerio Perrone, Aaron Klein, Cédric Archambeau, Matthias W. Seeger, Massimiliano Pontil, Paolo Frasconi
2023	—	`conf`	Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger
2023	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2023	A*	`conf`	Optimizing Hyperparameters with Conformal Quantile Regression. ICML David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
2023	J	`jnl`	Optimizing Hyperparameters with Conformal Quantile Regression. CoRR David Salinas, Jacek Golebiowski, Aaron Klein, Matthias W. Seeger, Cédric Archambeau
2022	—	`conf`	Automatic Termination for Hyperparameter Optimization. AutoML Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
2022	—	`conf`	Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. AutoML David Salinas, Matthias W. Seeger, Aaron Klein, Valerio Perrone, Martin Wistuba, Cédric Archambeau
2021	A	`conf`	A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
2021	J	`jnl`	A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. CoRR Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
2021	A*	`conf`	Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. KDD 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
2021	A*	`conf`	BORE: Bayesian Optimization by Density-Ratio Estimation. ICML Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
2021	J	`jnl`	BORE: Bayesian Optimization by Density-Ratio Estimation. CoRR Louis C. Tiao, Aaron Klein, Matthias W. Seeger, Edwin V. Bonilla, Cédric Archambeau, Fabio Ramos
2021	J	`jnl`	Meta-Forecasting by combining Global Deep Representations with Local Adaptation. CoRR Riccardo Grazzi, Valentin Flunkert, David Salinas, Tim Januschowski, Matthias W. Seeger, Cédric Archambeau
2021	J	`jnl`	Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. CoRR Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias W. Seeger, Cédric Archambeau
2020	J	`jnl`	Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. CoRR 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
2020	J	`jnl`	Amazon SageMaker Autopilot: a white box AutoML solution at scale. CoRR 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
2020	J	`jnl`	Cost-aware Bayesian Optimization. CoRR Eric Hans Lee, Valerio Perrone, Cédric Archambeau, Matthias W. Seeger
2020	A*	`conf`	LEEP: A New Measure to Evaluate Transferability of Learned Representations. ICML Cuong V. Nguyen, Tal Hassner, Matthias W. Seeger, Cédric Archambeau
2020	J	`jnl`	LEEP: A New Measure to Evaluate Transferability of Learned Representations. CoRR Cuong V. Nguyen, Tal Hassner, Cédric Archambeau, Matthias W. Seeger
2020	J	`jnl`	Model-based Asynchronous Hyperparameter Optimization. CoRR Louis C. Tiao, Aaron Klein, Cédric Archambeau, Matthias W. Seeger
2019	J	`jnl`	Constrained Bayesian Optimization with Max-Value Entropy Search. CoRR Valerio Perrone, Iaroslav Shcherbatyi, Rodolphe Jenatton, Cédric Archambeau, Matthias W. Seeger
2019	J	`jnl`	Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. CoRR Valerio Perrone, Huibin Shen, Matthias W. Seeger, Cédric Archambeau, Rodolphe Jenatton
2018	A*	`conf`	Deep State Space Models for Time Series Forecasting. NeurIPS Syama Sundar Rangapuram, Matthias W. Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, Tim Januschowski
2018	A*	`conf`	Scalable Hyperparameter Transfer Learning. NeurIPS Valerio Perrone, Rodolphe Jenatton, Matthias W. Seeger, Cédric Archambeau
2017	J	`jnl`	Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. CoRR Matthias W. Seeger, Syama Sundar Rangapuram, Yuyang Wang, David Salinas, Jan Gasthaus, Tim Januschowski, Valentin Flunkert
2017	J	`jnl`	Auto-Differentiating Linear Algebra. CoRR Matthias W. Seeger, Asmus Hetzel, Zhenwen Dai, Neil D. Lawrence
2017	A*	`conf`	Bayesian Optimization with Tree-structured Dependencies. ICML Rodolphe Jenatton, Cédric Archambeau, Javier González, Matthias W. Seeger
2017	J	`jnl`	Probabilistic Demand Forecasting at Scale. Proc. VLDB Endow. Joos-Hendrik Boese, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Dustin Lange, David Salinas, Sebastian Schelter, Matthias W. Seeger, Bernie Wang
2016	—	`conf`	Bayesian Intermittent Demand Forecasting for Large Inventories. NIPS Matthias W. Seeger, David Salinas, Valentin Flunkert
2015	C	`conf`	Expectation Propagation for Rectified Linear Poisson Regression. ACML Young-Jun Ko, Matthias W. Seeger
2014	—	`conf`	Clustering IT Events around Common Root Causes. IEEE SCC Iulia Gabriela Carjeu, Thomas Shorrock, Matthias W. Seeger
2014	A	`conf`	Scalable Collaborative Bayesian Preference Learning. AISTATS Mohammad Emtiyaz Khan, Young-Jun Ko, Matthias W. Seeger
2013	—	`conf`	Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML (3) Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias W. Seeger
2012	A	`conf`	Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models. AISTATS Matthias W. Seeger, Guillaume Bouchard
2012	J	`jnl`	Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. IEEE Trans. Inf. Theory Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger
2012	A*	`conf`	Large Scale Variational Bayesian Inference for Structured Scale Mixture Models. ICML Young-Jun Ko, Matthias W. Seeger
2011	A	`conf`	Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. AISTATS Matthias W. Seeger, Hannes Nickisch
2011	J	`jnl`	Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. SIAM J. Imaging Sci. Matthias W. Seeger, Hannes Nickisch
2010	A*	`conf`	Gaussian Covariance and Scalable Variational Inference. ICML Matthias W. Seeger
2010	A*	`conf`	Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. ICML Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger
2010	—	`ch.`	Real-Time Local GP Model Learning. From Motor Learning to Interaction Learning in Robots Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
2010	J	`jnl`	Variational Bayesian Inference Techniques. IEEE Signal Process. Mag. Matthias W. Seeger, David P. Wipf
2009	—	`conf`	09181 Working Group on Hybridization between R&S, DoE and Optimization. Sampling-based Optimization in the Presence of Uncertainty Chun-Hung Chen, Liu Hong, Paul B. Kantor, David P. Morton, Juta Pichitlamken, Matthias W. Seeger
2009	A*	`conf`	Convex variational Bayesian inference for large scale generalized linear models. ICML Hannes Nickisch, Matthias W. Seeger
2009	J	`jnl`	Gaussian Process Bandits without Regret: An Experimental Design Approach CoRR Niranjan Srinivas, Andreas Krause, Sham M. Kakade, Matthias W. Seeger
2009	—	`conf`	Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. Sampling-based Optimization in the Presence of Uncertainty Matthias W. Seeger, Hannes Nickisch
2009	J	`jnl`	Model Learning with Local Gaussian Process Regression. Adv. Robotics Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
2009	—	`conf`	Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing. NIPS Matthias W. Seeger
2009	A*	`conf`	Workshop summary: Numerical mathematics in machine learning. ICML Matthias W. Seeger, Suvrit Sra, John P. Cunningham
2008	—	`conf`	Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. NIPS Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann, Bernhard Schölkopf
2008	J	`jnl`	Bayesian Inference and Optimal Design for the Sparse Linear Model. J. Mach. Learn. Res. Matthias W. Seeger
2008	A*	`conf`	Compressed sensing and Bayesian experimental design. ICML Matthias W. Seeger, Hannes Nickisch
2008	C	`conf`	Computed torque control with nonparametric regression models. ACC Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
2008	J	`jnl`	Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods. J. Mach. Learn. Res. Matthias W. Seeger
2008	J	`jnl`	Information Consistency of Nonparametric Gaussian Process Methods. IEEE Trans. Inf. Theory Matthias W. Seeger, Sham M. Kakade, Dean P. Foster
2008	B	`conf`	Learning Inverse Dynamics: a Comparison. ESANN Duy Nguyen-Tuong, Jan Peters, Matthias W. Seeger, Bernhard Schölkopf
2008	—	`conf`	Local Gaussian Process Regression for Real Time Online Model Learning. NIPS Duy Nguyen-Tuong, Matthias W. Seeger, Jan Peters
2007	A	`conf`	Bayesian Inference and Optimal Design in the Sparse Linear Model. AISTATS Matthias W. Seeger, Florian Steinke, Koji Tsuda
2007	—	`conf`	Bayesian Inference for Sparse Generalized Linear Models. ECML Matthias W. Seeger, Sebastian Gerwinn, Matthias Bethge
2007	—	`conf`	Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. NIPS Sebastian Gerwinn, Jakob H. Macke, Matthias W. Seeger, Matthias Bethge
2007	J	`jnl`	Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Syst. Biol. Florian Steinke, Matthias W. Seeger, Koji Tsuda
2006	—	`ch.`	A Taxonomy for Semi-Supervised Learning Methods. Semi-Supervised Learning Matthias W. Seeger
2006	—	`conf`	Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. NIPS Matthias W. Seeger
2005	—	`conf`	Fast Gaussian Process Regression using KD-Trees. NIPS Yirong Shen, Andrew Y. Ng, Matthias W. Seeger
2005	A	`conf`	Semiparametric latent factor models. AISTATS Yee Whye Teh, Matthias W. Seeger, Michael I. Jordan
2005	—	`conf`	Worst-Case Bounds for Gaussian Process Models. NIPS Sham M. Kakade, Matthias W. Seeger, Dean P. Foster
2004	J	`jnl`	Gaussian Processes For Machine Learning. Int. J. Neural Syst. Matthias W. Seeger
2003	—	`—`	Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations. Matthias W. Seeger
2003	A	`conf`	Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. AISTATS Matthias W. Seeger, Christopher K. I. Williams, Neil D. Lawrence
2002	—	`conf`	Fast Sparse Gaussian Process Methods: The Informative Vector Machine. NIPS Neil D. Lawrence, Matthias W. Seeger, Ralf Herbrich
2002	J	`jnl`	PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. J. Mach. Learn. Res. Matthias W. Seeger
2001	A*	`conf`	An Improved Predictive Accuracy Bound for Averaging Classifiers. ICML John Langford, Matthias W. Seeger, Nimrod Megiddo
2001	—	`conf`	Covariance Kernels from Bayesian Generative Models. NIPS Matthias W. Seeger
2000	A*	`conf`	The Effect of the Input Density Distribution on Kernel-based Classifiers. ICML Christopher K. I. Williams, Matthias W. Seeger
2000	—	`conf`	Using the Nyström Method to Speed Up Kernel Machines. NIPS Christopher K. I. Williams, Matthias W. Seeger
1999	—	`conf`	Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. NIPS Matthias W. Seeger