Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting von Srinivas, N., Krause, A., Kakade, S. M., Seeger, M.

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Optimization of k-space trajectories for compressed sensing by Bayesian experimental design von Seeger, Matthias, Nickisch, Hannes, Pohmann, Rolf, Schölkopf, Bernhard

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Information Consistency of Nonparametric Gaussian Process Methods von Seeger, M.W., Kakade, S.M., Foster, D.P.

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Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models von Seeger, Matthias W., Nickisch, Hannes

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Sparse linear models: Variational approximate inference and Bayesian experimental design von Seeger, Matthias W

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Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models von Steinke, Florian, Seeger, Matthias, Tsuda, Koji

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PAC-Bayesian generalisation error bounds for Gaussian process classification von Seeger, M

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Probabilistic demand forecasting at scale von Böse, Joos-Hendrik, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim, Lange, Dustin, Salinas, David, Schelter, Sebastian, Seeger, Matthias, Wang, Yuyang

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Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography von Franceschi, Luca, Zor, Cemre, Zafar, Muhammad Bilal, Detommaso, Gianluca, Archambeau, Cedric, Madl, Tamas, Donini, Michele, Seeger, Matthias

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Explaining Probabilistic Models with Distributional Values von Franceschi, Luca, Donini, Michele, Archambeau, Cédric, Seeger, Matthias

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Optimizing Hyperparameters with Conformal Quantile Regression von Salinas, David, Golebiowski, Jacek, Klein, Aaron, Seeger, Matthias, Archambeau, Cedric

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Hyperparameter Optimization in Machine Learning von Franceschi, Luca, Donini, Michele, Perrone, Valerio, Klein, Aaron, Archambeau, Cédric, Seeger, Matthias, Pontil, Massimiliano, Frasconi, Paolo

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A Nonmyopic Approach to Cost-Constrained Bayesian Optimization von Lee, Eric Hans, Eriksson, David, Perrone, Valerio, Seeger, Matthias

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Fortuna: A Library for Uncertainty Quantification in Deep Learning von Detommaso, Gianluca, Gasparin, Alberto, Donini, Michele, Seeger, Matthias, Wilson, Andrew Gordon, Archambeau, Cedric

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Explaining Probabilistic Models with Distributional Values von Franceschi, Luca, Donini, Michele, Archambeau, Cédric, Seeger, Matthias

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Cost-aware Bayesian Optimization von Lee, Eric Hans, Perrone, Valerio, Archambeau, Cedric, Seeger, Matthias

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LEEP: A New Measure to Evaluate Transferability of Learned Representations von Nguyen, Cuong V, Hassner, Tal, Seeger, Matthias, Archambeau, Cedric

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Meta-Forecasting by combining Global Deep Representations with Local Adaptation von Grazzi, Riccardo, Flunkert, Valentin, Salinas, David, Januschowski, Tim, Seeger, Matthias, Archambeau, Cedric

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Constrained Bayesian Optimization with Max-Value Entropy Search von Perrone, Valerio, Shcherbatyi, Iaroslav, Jenatton, Rodolphe, Archambeau, Cedric, Seeger, Matthias

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Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning von Perrone, Valerio, Shen, Huibin, Seeger, Matthias, Archambeau, Cedric, Jenatton, Rodolphe

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