{"paper":{"title":"Stochastic Optimization and Data Science","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Stochastic optimization problems arise when maximizing log-likelihood or minimizing population risk in statistical estimation and learning.","cross_cats":[],"primary_cat":"math.OC","authors_text":"Alexander Gasnikov, Arutyun Avetisyan, Darina Dvinskikh, Denis Turdakov, Nazarii Tupitsa, Vladimir Temlyakov","submitted_at":"2026-05-16T08:36:29Z","abstract_excerpt":"This paper aims to motivate stochastic optimization problems from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood or minimize the population risk. We briefly describe the two main approaches: offline (Monte Carlo / Sample Average Approximation) and online (Stochastic Approximation) approaches -- to solve the expectation minimization problems."},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Stochastic optimization problems can be motivated from a statistical perspective and a statistical learning perspective, where the goal is to maximize the log-likelihood or minimize the population risk, using offline (Monte Carlo / Sample Average Approximation) and online (Stochastic Approximation) approaches.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the two described approaches (offline Monte Carlo/SAA and online SA) are the primary or sufficient ways to solve the expectation minimization problems arising in statistical settings, without needing additional context or 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