Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers. - Matthias W. Seeger (NIPS, 1999)

The Effect of the Input Density Distribution on Kernel-based Classifiers. - Christopher K. I. Williams; Matthias W. Seeger (ICML, 2000)

Using the Nyström Method to Speed Up Kernel Machines. - Christopher K. I. Williams; Matthias W. Seeger (NIPS, 2000)

An Improved Predictive Accuracy Bound for Averaging Classifiers. - John Langford 001; Matthias W. Seeger; Nimrod Megiddo (ICML, 2001)

Covariance Kernels from Bayesian Generative Models. - Matthias W. Seeger (NIPS, 2001)

Fast Sparse Gaussian Process Methods: The Informative Vector Machine. - Neil D. Lawrence; Matthias W. Seeger; Ralf Herbrich (NIPS, 2002)

PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification. - Matthias W. Seeger (J. Mach. Learn. Res., 2002)

Fast Forward Selection to Speed Up Sparse Gaussian Process Regression. - Matthias W. Seeger; Christopher K. I. Williams; Neil D. Lawrence (AISTATS, 2003)

Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations. - Matthias W. Seeger (2003)

Gaussian Processes For Machine Learning. - Matthias W. Seeger (Int. J. Neural Syst., 2004)

Semiparametric latent factor models. - Yee Whye Teh; Matthias W. Seeger; Michael I. Jordan (AISTATS, 2005)

Worst-Case Bounds for Gaussian Process Models. - Sham M. Kakade; Matthias W. Seeger; Dean P. Foster (NIPS, 2005)

Fast Gaussian Process Regression using KD-Trees. - Yirong Shen; Andrew Y. Ng; Matthias W. Seeger (NIPS, 2005)

A Taxonomy for Semi-Supervised Learning Methods. - Matthias W. Seeger (Semi-Supervised Learning, 2006)

Cross-Validation Optimization for Large Scale Hierarchical Classification Kernel Methods. - Matthias W. Seeger (NIPS, 2006)

Bayesian Inference for Sparse Generalized Linear Models. - Matthias W. Seeger; Sebastian Gerwinn; Matthias Bethge (ECML, 2007)

Bayesian Inference for Spiking Neuron Models with a Sparsity Prior. - Sebastian Gerwinn; Jakob H. Macke; Matthias W. Seeger; Matthias Bethge (NIPS, 2007)

Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. - Florian Steinke; Matthias W. Seeger; Koji Tsuda (BMC Syst. Biol., 2007)

Bayesian Inference and Optimal Design in the Sparse Linear Model. - Matthias W. Seeger; Florian Steinke; Koji Tsuda (AISTATS, 2007)

Computed torque control with nonparametric regression models. - Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters 001 (ACC, 2008)

Learning Inverse Dynamics: a Comparison. - Duy Nguyen-Tuong; Jan Peters 001; Matthias W. Seeger; Bernhard Schölkopf (ESANN, 2008)

Compressed sensing and Bayesian experimental design. - Matthias W. Seeger; Hannes Nickisch (ICML, 2008)

Local Gaussian Process Regression for Real Time Online Model Learning. - Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters 001 (NIPS, 2008)

Bayesian Experimental Design of Magnetic Resonance Imaging Sequences. - Matthias W. Seeger; Hannes Nickisch; Rolf Pohmann; Bernhard Schölkopf (NIPS, 2008)

Bayesian Inference and Optimal Design for the Sparse Linear Model. - Matthias W. Seeger (J. Mach. Learn. Res., 2008)

Cross-Validation Optimization for Large Scale Structured Classification Kernel Methods. - Matthias W. Seeger (J. Mach. Learn. Res., 2008)

Information Consistency of Nonparametric Gaussian Process Methods. - Matthias W. Seeger; Sham M. Kakade; Dean P. Foster (IEEE Trans. Inf. Theory, 2008)

09181 Working Group on Hybridization between R&S, DoE and Optimization. - Chun-Hung Chen; Liu Hong 001; Paul B. Kantor; David P. Morton; Juta Pichitlamken; Matthias W. Seeger (Sampling-based Optimization in the Presence of Uncertainty, 2009)

Large Scale Variational Inference and Experimental Design for Sparse Generalized Linear Models. - Matthias W. Seeger; Hannes Nickisch (Sampling-based Optimization in the Presence of Uncertainty, 2009)

Convex variational Bayesian inference for large scale generalized linear models. - Hannes Nickisch; Matthias W. Seeger (ICML, 2009)

Workshop summary: Numerical mathematics in machine learning. - Matthias W. Seeger; Suvrit Sra; John P. Cunningham (ICML, 2009)

Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing. - Matthias W. Seeger (NIPS, 2009)

Model Learning with Local Gaussian Process Regression. - Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters 001 (Adv. Robotics, 2009)

Gaussian Process Bandits without Regret: An Experimental Design Approach - Niranjan Srinivas; Andreas Krause 001; Sham M. Kakade; Matthias W. Seeger (CoRR, 2009)

Gaussian Covariance and Scalable Variational Inference. - Matthias W. Seeger (ICML, 2010)

Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design. - Niranjan Srinivas; Andreas Krause 001; Sham M. Kakade; Matthias W. Seeger (ICML, 2010)

Variational Bayesian Inference Techniques. - Matthias W. Seeger; David P. Wipf (IEEE Signal Process. Mag., 2010)

Real-Time Local GP Model Learning. - Duy Nguyen-Tuong; Matthias W. Seeger; Jan Peters 001 (From Motor Learning to Interaction Learning in Robots, 2010)

Fast Convergent Algorithms for Expectation Propagation Approximate Bayesian Inference. - Matthias W. Seeger; Hannes Nickisch (AISTATS, 2011)

Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models. - Matthias W. Seeger; Hannes Nickisch (SIAM J. Imaging Sci., 2011)

Large Scale Variational Bayesian Inference for Structured Scale Mixture Models. - Young-Jun Ko; Matthias W. Seeger (ICML, 2012)

Fast Variational Bayesian Inference for Non-Conjugate Matrix Factorization Models. - Matthias W. Seeger; Guillaume Bouchard (AISTATS, 2012)

Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting. - Niranjan Srinivas; Andreas Krause 001; Sham M. Kakade; Matthias W. Seeger (IEEE Trans. Inf. Theory, 2012)

Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. - Mohammad Emtiyaz Khan; Aleksandr Y. Aravkin; Michael P. Friedlander; Matthias W. Seeger (ICML (3), 2013)

Clustering IT Events around Common Root Causes. - Iulia Gabriela Carjeu; Thomas Shorrock; Matthias W. Seeger (IEEE SCC, 2014)

Scalable Collaborative Bayesian Preference Learning. - Mohammad Emtiyaz Khan; Young-Jun Ko; Matthias W. Seeger (AISTATS, 2014)

Expectation Propagation for Rectified Linear Poisson Regression. - Young-Jun Ko; Matthias W. Seeger (ACML, 2015)

Bayesian Intermittent Demand Forecasting for Large Inventories. - Matthias W. Seeger; David Salinas; Valentin Flunkert (NIPS, 2016)

Bayesian Optimization with Tree-structured Dependencies. - Rodolphe Jenatton; Cédric Archambeau; Javier González 002; Matthias W. Seeger (ICML, 2017)

Approximate Bayesian Inference in Linear State Space Models for Intermittent Demand Forecasting at Scale. - Matthias W. Seeger; Syama Sundar Rangapuram; Yuyang Wang 001; David Salinas; Jan Gasthaus; Tim Januschowski; Valentin Flunkert (CoRR, 2017)

Auto-Differentiating Linear Algebra. - Matthias W. Seeger; Asmus Hetzel; Zhenwen Dai; Neil D. Lawrence (CoRR, 2017)

Probabilistic Demand Forecasting at Scale. - Joos-Hendrik Boese; Valentin Flunkert; Jan Gasthaus; Tim Januschowski; Dustin Lange; David Salinas; Sebastian Schelter; Matthias W. Seeger; Bernie Wang 001 (Proc. VLDB Endow., 2017)

Scalable Hyperparameter Transfer Learning. - Valerio Perrone; Rodolphe Jenatton; Matthias W. Seeger; Cédric Archambeau (NeurIPS, 2018)

Deep State Space Models for Time Series Forecasting. - Syama Sundar Rangapuram; Matthias W. Seeger; Jan Gasthaus; Lorenzo Stella; Yuyang Wang 001; Tim Januschowski (NeurIPS, 2018)

Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning. - Valerio Perrone; Huibin Shen; Matthias W. Seeger; Cédric Archambeau; Rodolphe Jenatton (CoRR, 2019)

Constrained Bayesian Optimization with Max-Value Entropy Search. - Valerio Perrone; Iaroslav Shcherbatyi; Rodolphe Jenatton; Cédric Archambeau; Matthias W. Seeger (CoRR, 2019)

LEEP: A New Measure to Evaluate Transferability of Learned Representations. - Cuong V. Nguyen; Tal Hassner; Matthias W. Seeger; Cédric Archambeau (ICML, 2020)

Model-based Asynchronous Hyperparameter Optimization. - Louis C. Tiao; Aaron Klein; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

Cost-aware Bayesian Optimization. - Eric Hans Lee; Valerio Perrone; Cédric Archambeau; Matthias W. Seeger (CoRR, 2020)

Amazon SageMaker Autopilot: a white box AutoML solution at scale. - 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; et al. (CoRR, 2020)

Amazon SageMaker Automatic Model Tuning: Scalable Black-box Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (CoRR, 2020)

BORE: Bayesian Optimization by Density-Ratio Estimation. - Louis C. Tiao; Aaron Klein; Matthias W. Seeger; Edwin V. Bonilla; Cédric Archambeau; Fabio Ramos 001 (ICML, 2021)

Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization. - Valerio Perrone; Huibin Shen; Aida Zolic; Iaroslav Shcherbatyi; Amr Ahmed 004; Tanya Bansal; Michele Donini; Fela Winkelmolen; Rodolphe Jenatton; Jean Baptiste Faddoul; Barbara Pogorzelska; Miroslav Miladinovic; et al. (KDD, 2021)

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. - Eric Hans Lee; David Eriksson; Valerio Perrone; Matthias W. Seeger (UAI, 2021)

Overfitting in Bayesian Optimization: an empirical study and early-stopping solution. - Anastasia Makarova; Huibin Shen; Valerio Perrone; Aaron Klein; Jean Baptiste Faddoul; Andreas Krause 001; Matthias W. Seeger; Cédric Archambeau (CoRR, 2021)

Meta-Forecasting by combining Global Deep Representations with Local Adaptation. - Riccardo Grazzi; Valentin Flunkert; David Salinas; Tim Januschowski; Matthias W. Seeger; Cédric Archambeau (CoRR, 2021)

Automatic Termination for Hyperparameter Optimization. - Anastasia Makarova; Huibin Shen; Valerio Perrone; Aaron Klein; Jean Baptiste Faddoul; Andreas Krause 001; Matthias W. Seeger; Cédric Archambeau (AutoML, 2022)

Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research. - David Salinas; Matthias W. Seeger; Aaron Klein; Valerio Perrone; Martin Wistuba; Cédric Archambeau (AutoML, 2022)

Optimizing Hyperparameters with Conformal Quantile Regression. - David Salinas; Jacek Golebiowski; Aaron Klein; Matthias W. Seeger; Cédric Archambeau (ICML, 2023)

Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. - Luca Franceschi 001; Cemre Zor; Muhammad Bilal Zafar; Gianluca Detommaso; Cédric Archambeau; Tamas Madl; Michele Donini; Matthias W. Seeger (TML4H, 2023)

Explaining Probabilistic Models with Distributional Values. - Luca Franceschi 001; Michele Donini; Cédric Archambeau; Matthias W. Seeger (ICML, 2024)

Hyperparameter Optimization in Machine Learning. - Luca Franceschi 001; Michele Donini; Valerio Perrone; Aaron Klein; Cédric Archambeau; Matthias W. Seeger; Massimiliano Pontil; Paolo Frasconi (CoRR, 2024)

Fortuna: A Library for Uncertainty Quantification in Deep Learning. - Gianluca Detommaso; Alberto Gasparin; Michele Donini; Matthias W. Seeger; Andrew Gordon Wilson; Cédric Archambeau (J. Mach. Learn. Res., 2024)