Space-Efficient Estimation of Robust Statistics and Distribution Testing

: The generic problem of estimation and inference given a sequence of i.i.d. samples has been extensively studied in the statistics, property testing, and learning communities. A natural quantity of interest is the sample complexity of the particular learning or estimation problem being considered. While sample complexity is an important component of the computational efficiency of the task, it is also natural to consider the space complexity: do we need to store all the samples as they are drawn, or is it sufficient to use memory that is significantly sublinear in the sample complexity? Surprisingly, this aspect of the complexity of estimation has received significantly less attention in all but a few specific cases. While space-bounded, sequential computation is the purview of the field of data-stream computation, almost all of the literature on the algorithmic theory of data-streams considers only "empirical problems", where the goal is to compute a function of the data pr...

• Type: Conference
• Year: 2010
• Where: ICS
• Authors: Steve Chien, Katrina Ligett, Andrew McGregor
• Keywords: ICS 2010, Theoritical Computer Science