On Semi-supervised Estimation of Distributions

We study the problem of estimating the joint probability mass function (pmf) over two random variables. In particular, the estimation is based on the observation of 𝑚 samples containing both variables and 𝑛 samples missing one fixed variable. We adopt the minimax framework with [notation] loss functions, and we show that the composition of uni-variate minimax estimators achieves minimax risk with the optimal first-order constant for 𝑝 ≥ 2, in the regime 𝑚 = 𝑜(𝑛).

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Erol, Hasan Sabri Melihcan

Advisor dc:contributor.advisor

• Zheng, Lizhong

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151386

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151386