Subset Selection in Machine Learning: From Theory to Applications

A growing number of machine learning problems involve finding subsets of data points. Examples range from selecting subset of labeled or unlabeled data points, to subsets of features or model parameters, to selecting subsets of pixels, keypoints, sentences etc. in image segmentation, correspondence and summarization problems. The workshop would encompass a wide variety of topics ranging from theoretical aspects of subset selection e.g. coresets, submodularity, determinantal point processes, to several practical applications, {\em e.g.}, time and energy efficient learning, learning under resource constraints, active learning, human assisted learning, feature selection, model compression, feature induction, {\em etc.}

We believe that this workshop is very timely since, a) subset selection is naturally emerging and has often been considered in isolation in many of the above applications, and b) by connecting researchers working on both the theoretical and application domains above, we can foster a much needed discussion on reusing a several technical innovations across these subareas and applications. Furthermore, we would also like to connect researchers working on the theoretical foundations of subset selection (in areas such as coresets and submodularity) with researchers working in applications (such as feature selection, active learning, data efficient learning, model compression, and human assisted machine learning).