Temporal Segmentation: Perspectives from Statistics, Machine Learning, and Signal Processing

Data with temporal (or sequential) structure arise in several applications, such as speaker diarization, human action segmentation, network intrusion detection, DNA copy number analysis, and neuron activity modelling, to name a few. A particularly recurrent temporal structure in real applications is the so-called change-point model, where the data may be temporally partitioned into a sequence of segments delimited by change-points, such that a single model holds within each segment whereas different models hold accross segments. Change-point problems may be tackled from two points of view, corresponding to the practical problem at hand: retrospective (or "a posteriori"), aka multiple change-point estimation, where the whole signal is taken at once and the goal is to estimate the change-point locations, and online (or sequential), aka quickest detection, where data are observed sequentially and the goal is to quickly detect change-points. The purpose of this workshop is to bring together experts from the statistics, machine learning, signal processing communities, to address a broad range of applications from robotics to neuroscience, to discuss and cross-fertilize ideas, and to define the current challenges in temporal segmentation.