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个人简介
His work in concrete computational complexity theory has yielded new lower bounds and state-of-the-art derandomization results for well-studied Boolean circuit models. A core goal that motivates and inspires much of Servedio’s work is to understand the structural properties of different types of Boolean functions using a range of analytic, algebraic, probabilistic, and combinatorial techniques. Beyond identifying and establishing such structural properties, Servedio leverages these properties both to develop efficient solutions to various algorithmic problems (such as computational learning and property testing) and to establish computational hardness (such as circuit lower bounds and pseudorandomness results). Servedio has given state-of-the-art algorithms and hardness results for learning, testing, and derandomizing well-studied Boolean function classes such as DNF formulas, monotone functions, functions with small Fourier sparsity and/or Fourier dimension, constant-depth circuits, linear separators, intersections of halfspaces, polynomial threshold functions, and juntas. His interests also include structural analysis and computational learning and testing of various classes of probability distributions, as well as other data analysis problems.
研究兴趣
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CoRR (2024)
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arxiv(2023)
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SODApp.605-637, (2023)
CoRR (2023): 33:1-33:23
SODA (2023): 4050-4082
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