PREVIOUS SEMINAR - 9th October 2020

Speaker:  Solt Kovacs (ETH Zurich)

Title: Optimistic search strategy: change point detection for large-scale data via adaptive logarithmic queries

Abstract: Change point detection is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching through all candidate split points on the grid for finding the best one requires O(T) evaluations of the gain function for an interval with T observations. If each evaluation is computationally demanding (e.g. in high-dimensional models), this can be computationally infeasible. Instead, we propose “optimistic” strategies with O(log T) evaluations exploiting specific structure of the gain function. Towards solid understanding of our strategies, we investigate in detail the classical univariate Gaussian change in mean setup. For some of our proposals we prove asymptotic minimax optimality for single and multiple change point scenarios, for the latter in combination with the computationally efficient seeded binary segmentation algorithm. In simulations we demonstrate competitive estimation performance with significantly reduced computational complexity. Our search strategies generalize far beyond the theoretically analyzed univariate setup. As a promising example, we demonstrate massive computational speedup in change point detection for high-dimensional Gaussian graphical models. This talk is based on joint work with Housen Li (University of Göttingen), Lorenz Haubner (ETH Zurich), Axel Munk (University of Göttingen) and Peter Bühlmann (ETH Zurich).