We present a first attempt in connecting two areas of statistical learning that have not shared much common ground: weakly supervised learning and privacy aware learning. In the former, we aim to learn models of labeled data, when full information of the labels is not available; the latter concerns the design of algorithms with privacy guarantees for the protection of the data, while trading of...

Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not hold for such data, two important problems are to accurately estimate group-level effect sizes, and to obtain powerful statistical tests against group-level null hypotheses. A common approach is to summarize subject-lev...

This paper introduces new algorithms and data st.ruct,ures for quick rounting for machine learning dat.asets. We focus on t,he counting task of constructing contingent:. t.ables, but our approach is also applicahlc t.o counting the number of records in a dataset that match conjunctive queries. Subject to certain assumptionsl t h c rosts of thesr operations ca,n he shown to be independent of the...

The minimum message length principle is an information theoretic criterion that links data compression with statistical inference. This paper studies the strict minimum message length (SMML) estimator for d-dimensional exponential families with continuous sufficient statistics, for all d. The partition of an SMML estimator is shown to consist of convex polytopes (i.e. convex polygons when d = 2...

One of the most utilized adaptation techniques is the feature Maximum Likelihood Linear Regression (fMLLR). In comparison with other adaptation methods the number of free parameters to be estimated significantly decreases. Thus, the method is well suited for situations with small amount of adaptation data. However, fMLLR still fails in situations with extremely small data sets. Such situations ...

Given a large dataset and an estimation task, it is common to pre-process the data by reducing them to a set of sufficient statistics. This step is often regarded as straightforward and advantageous (in that it simplifies statistical analysis). I show that –on the contrary– reducing data to sufficient statistics can change a computationally tractable estimation problem into an intractable one. ...