World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
8611
World Ranking
4408
National Ranking
2057

Research.com Recognitions

  • 2005 - Fellow of Alfred P. Sloan Foundation

Overview

Rocco A. Servedio is affiliated with Columbia University in the United States. Their research spans multiple areas within computer science, with a focus on theoretical and applied aspects of algorithms and computational models.

Their recent publication record includes works on trace reconstruction and complexity models. Notable papers include:

  • Polynomial-Time Trace Reconstruction in the Low Deletion Rate Regime, 2021, published by Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Polynomial-Time Trace Reconstruction in the Low Deletion Rate Regime, 2021, published on arXiv (Cornell University)
  • Polynomial-time Trace Reconstruction in the Smoothed Complexity Model, 2022, published in ACM Transactions on Algorithms
  • On the Approximation Power of Two-Layer Networks of Random ReLUs, 2021, published on arXiv (Cornell University)
  • Untitled paper, 2020, published in Theory of Computing

Frequent co-authors collaborating with Servedio include:

  • Anindya De
  • Shivam Nadimpalli
  • Chin Ho Lee
  • Xi Chen
  • Sandip Sinha

Servedio's publications are predominantly found in venues such as:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Theory of Computing
  • ACM Transactions on Algorithms
  • Journal of the ACM

Their primary field of study is computer science with an emphasis on the following subfields:

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Networks and Communications

Research topics covered in Servedio's work include:

  • Machine Learning and Algorithms
  • Complexity and Algorithms in Graphs
  • Algorithms and Data Compression
  • DNA and Biological Computing
  • Optimization and Search Problems
  • Environmental DNA in Biodiversity Studies
  • Molecular Biology Techniques and Applications

In 2005, Servedio was recognized as a Fellow of the Alfred P. Sloan Foundation.

Best Publications

  • Random classification noise defeats all convex potential boosters

    Philip M. Long;Rocco A. Servedio

  • Agnostically Learning Halfspaces

    Adam Tauman Kalai;Adam R. Klivans;Yishay Mansour;Rocco A. Servedio

  • Learning intersections and thresholds of halfspaces

    A. R. Klivans;R. O'Donnell;Rocco A. Servedio

  • Learning DNF in time 2 õ ( n 1/3 )

    Adam R. Klivans;Rocco A. Servedio

  • Smooth boosting and learning with malicious noise

    Rocco A. Servedio

  • On the Capacity of Secure Network Coding

    Jon Feldman;Tal Malkin;Rocco A. Servedio;Cliff Stein

  • Learning functions of k relevant variables

    Elchanan Mossel;Ryan O'Donnell;Rocco A. Servedio

  • Learning juntas

    Elchanan Mossel;Ryan O'Donnell;Rocco P. Servedio

  • Every decision tree has an influential variable

    R. O'Donnell;M. Saks;O. Schramm;R.A. Servedio

  • Equivalences and Separations Between Quantum and Classical Learnability

    Rocco A. Servedio;Steven J. Gortler

  • Learning Monotone Decision Trees in Polynomial Time

    Ryan O'Donnell;Rocco A. Servedio

  • Agnostically learning halfspaces

    A.T. Kalai;A.R. Klivans;Yishay Mansour;R.A. Servedio

  • LP Decoding Corrects a Constant Fraction of Errors

    J. Feldman;T. Malkin;R.A. Servedio;C. Stein

  • Learning DNF in time

    Adam R. Klivans;Rocco Servedio

  • Learning Halfspaces with Malicious Noise

    Adam R. Klivans;Philip M. Long;Rocco A. Servedio

  • Testing for Concise Representations

    I. Diakonikolas;H.K. Lee;K. Matule;K. Onak

  • Quantum Algorithms for Learning and Testing Juntas

    Alp Atıcı;Rocco A. Servedio

  • Testing Halfspaces

    Kevin Matulef;Ryan O'Donnell;Ronitt Rubinfeld;Rocco A. Servedio

  • Learning Mixtures of Product Distributions over Discrete Domains

    Jon Feldman;Ryan O'Donnell;Rocco A. Servedio

  • Learning Geometric Concepts via Gaussian Surface Area

    A.R. Klivans;R. O'Donnell;R.A. Servedio

  • Efficient density estimation via piecewise polynomial approximation

    Siu-On Chan;Ilias Diakonikolas;Rocco A. Servedio;Xiaorui Sun

  • Bounded Independence Fools Halfspaces

    Ilias Diakonikolas;Parikshit Gopalan;Ragesh Jaiswal;Rocco A. Servedio

Frequent Co-Authors

Ilias Diakonikolas
Ilias Diakonikolas University of Wisconsin–Madison
Ryan O'Donnell
Ryan O'Donnell Carnegie Mellon University
Xi Chen
Xi Chen Columbia University
Philip M. Long
Philip M. Long Google (United States)
Adam R. Klivans
Adam R. Klivans The University of Texas at Austin
Jon Feldman
Jon Feldman Google (United States)
Dana Ron
Dana Ron Tel Aviv University
Vitaly Feldman
Vitaly Feldman Apple (United States)

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