Statistical and computational tradeoffs in interactive clustering
The ever-increasing size and complexity of modern data sets pose many challenges for statistical inference, while classical statistical analysis has lagged behind. Among these challenges are fundamental questions related to the interplay and tradeoffs between statistical and computational requirements, which emerge due to the presence of complex underlying combinatorial structures in modern high-dimensional problems. In this talk, I will introduce a semi-supervised formulation of the clustering problem that allow active querying during data segmentation. Clever implementation of interactive querying framework can improve the accuracy of clustering and help in inferring labels of large amount of data by issuing only a small number of queries. For this problem, I will characterize the statistical and computational boundaries on the minimal number of pairwise queries needed to recover (possibly overlapping) latent clusters, under both adversarial and statistical modeling assumptions.
Wasim Huleihel received the B.Sc. and M.Sc. degrees in 2012 and 2013 from Ben-Gurion University, respectively, and received his PhD dgree in 2017 from the Technion, all in electrical engineering. He spent two years as a postdoctoral fellow in the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT), and is currently a postdoctoral fellow in the Department of Electrical Engineering - Systems at Tel-Aviv university. His research interests include high dimensional statistics, theoretical machine learning, information theory, and statistical signal processing.
Wasim is the recipient of the MIT - Technion Postdoctoral Fellowship, Viterbi fellowship, the Vatat postdoctoral scholarship, Vatat fellowship foe excellent PhD students, the Advanced Communication Center (ACC) Feder Family Award for outstanding research work in the field of communication technologies (first prize), coauthor of the Best Student Paper Award in the 31rd Annual Conference on Learning Theory (COLT'18), B.Sc. and M.Sc. graduations with honor, M.Sc. Rector's List Award, and Technion excellent tutor awards.