Article-Journal

Learning PAC-Bayes Priors for Probabilistic Neural Networks
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Seasonal Arctic sea ice forecasting with probabilistic deep learning
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Tighter Risk Certificates for Neural Networks
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High-Dimensional Bayesian Optimization with Manifold Gaussian Processes
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Probabilistic Active Meta-Learning
Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics …
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Estimating Barycenters of Measures in High Dimensions
Barycentric averaging is a principled way of summarizing populations of measures. Existing algorithms for estimating barycenters …
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Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in …
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Aligning Time Series on Incomparable Spaces
A Foliated View of Transfer Learning
Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to …
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A PAC-Bayesian perspective on structured prediction with implicit loss embeddings
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Consolidated Dataset and Metrics for High-Dynamic-Range Image Quality
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