{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/150231"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/150231","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Statistical Estimation from Dependent and Adversarial Data","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Dagan, Yuval"],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Daskalakis, Constantinos"],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-02","date_published":"2023-02","updated_at":"2026-07-22T22:21:20Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/150231","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Daskalakis, Constantinos"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Statistical Estimation from Dependent and Adversarial Data"]}]}],"canonical_facts":{"dc:contributor.advisor":["Daskalakis, Constantinos"],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["Dagan, Yuval"],"dc:date.accessioned":["2023-03-31T14:41:15Z"],"dc:date.available":["2023-03-31T14:41:15Z"],"dc:date.issued":["2023-02"],"dc:description.abstract":["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."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/150231"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Statistical Estimation from Dependent and Adversarial Data"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:21:20Z"}