Preference SQL is a declarative extension of standard SQL by strict partial order preferences, behaving like soft constraints under the BMO query model. Preference queries can be formulated intuitively following an inductive constructor-based approach. Both qualitative methods like e.g. Pareto / skyline and quantative methods like numerical ranking,… (More)
Our demo application demonstrates a personalized location-based web application using Preference SQL that allows single users as well as groups of users to find accommodations in Istanbul that satisfy both hard constraints and user preferences. The application assists in defining spatial, numerical , and categorical base preferences and composes complex… (More)
Location-Based Social Networks (LBSN) are a vast source for personalized geo-social user data with more and more users adding spatial information to their posts and tweets. The potential of spatially rich user models lies in the aggregation of different networks with each network adding a piece to the digital footprint of a user. These user models in turn… (More)
Among the goals of statistical genetics is to find sparse associations of genetic data with binary phenotypes, such as heritable diseases. Often, the data are obfuscated by confounders such as age, ancestry, or population structure. A widely appreciated modeling paradigm which corrects for such confounding relies on linear mixed models. These are linear… (More)
Complex application domains like outdoor activity platforms demand a powerful search interface that can adapt to personal user preferences and to changing contexts like weather conditions. Today most platforms offer a search technology known as Faceted Search, also named Parametric Search, where a user iteratively adapts his/her search parameters by a… (More)
We develop a variational inference (VI) scheme for the recently proposed Bayesian kernel support vector machine (SVM) and a stochastic version (SVI) for the linear Bayesian SVM. We compute the SVM's posterior, paving the way to apply attractive Bayesian techniques, as we exemplify in our experiments by means of automated model selection.
Dynamic probabilistic models are standard in various time-series applications, including weather forecasting, stock market analysis, and robotics. Typically such models consist of a diffusion model that governs the state of the system and a model of measuring this state. As an example consider the simple non-mixture time series model µ t = µ t−1 + v t , v t… (More)
Previous work on inference for dynamic mixture models has so far been directed to models that follow a simple Brownian motion diffusion over time and pursued a batch inference approach. We generalize the underlying dynamics model to follow a Gaussian process, introducing a novel class of dynamic priors for mixture models. Further, we propose a stochastic… (More)