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We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We first investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes o ...
We study generalization properties of distributed algorithms in the setting of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We investigate distributed stochastic gradient methods (SGM), with mini-batches and multi-passes over th ...
We study how language on social media is linked to diseases such as atherosclerotic heart disease (AHD), diabetes and various types of cancer. Our proposed model leverages state-of-the-art sentence embeddings, followed by a regression model and clustering, ...
This study aims towards an improved estimation of annual heat demand of the building stock for an entire region. This requires the holistic representation of aspects influencing the heat demand of buildings, namely their geometry, fabric, users and surroun ...
A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order ...
Machine learning tools such as neural networks and Gaussian process regression are increasingly being implemented in the development of atomistic potentials. Here, we develop a formalism to leverage such non-linear interpolation tools in describing propert ...
Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...
Despite recent developments, neighborhood-scale performance assessment at the early-design phase is seldom carried out in practice, notably due to high computational complexity, time requirement, and perceived need for expert knowledge, ultimately limiting ...
Building simulation requires a large number of uncertain inputs and parameters. These include quantities that may be known with reasonable confidence, like the thermal properties of materials and building dimensions, but also inputs whose correct values ca ...
The availability of massive volumes of data and recent advances in data collection and processing platforms have motivated the development of distributed machine learning algorithms. In numerous real-world applications large datasets are inevitably noisy a ...
