Statistical Estimation from Dependent and Adversarial Data

This thesis studies learning and estimation from data that is not independent, but rather falls into one of the following categories: (1) Data with strong correlations, such as social network correlations and data over a spatial domain or a temporal domain; and (2) Adversarial time series data, where the algorithm can possibly influence future data points in an adversarial manner. I will define mathematical models and learning problems that aim to capture these scenarios and describe polynomial-time algorithms to solve them. For (1), I will define the learning problem as a problem of learning Ising models and present algorithms to learning Ising models under different contexts. For (2), I will use the formulation of adversarial streaming algorithms by Ben-Eliezer and Yogev [2020] and present a tight analysis.

Name thesis:degree_name

Doctoral

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

• Dagan, Yuval

Advisor dc:contributor.advisor

• Daskalakis, Constantinos

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

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

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/150231

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/150231