World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
5745
World Ranking
9836
National Ranking
4140

Overview

Vitaly Feldman is a researcher affiliated with Apple in the United States. Their academic focus is primarily within the field of Computer Science, with a significant concentration in Artificial Intelligence. Additional subfields of study include Statistics and Probability, Management Science and Operations Research, Computational Mechanics, and Computational Theory and Mathematics.

Their research spans several key topics, notably Privacy-Preserving Technologies in Data, Cryptography and Data Security, and Stochastic Gradient Optimization Techniques. Other areas of interest within their work include Internet Traffic Analysis and Secure E-voting, Adversarial Robustness in Machine Learning, Sparse and Compressive Sensing Techniques, and Complexity and Algorithms in Graphs.

Vitaly Feldman has contributed to multiple publications in various prestigious venues. Frequent publication sites include:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Mathematics of Operations Research

Some of their recent papers include:

  • What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation (2020), published in arXiv (Cornell University)
  • Individual Privacy Accounting via a Renyi Filter (2020), published in arXiv (Cornell University)

Other notable recent works published in collaboration or in closely related fields include:

  • Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation (2020), published in arXiv (Cornell University)
  • Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses (2020), published in arXiv (Cornell University)
  • Information-Theoretic Single-Server PIR in the Shuffle Model (2024), published in Leibniz-Zentrum für Informatik (Schloss Dagstuhl)

Vitaly Feldman frequently collaborates with several co-authors, including:

  • Kunal Talwar
  • Audra McMillan
  • Hilal Asi
  • Tomer Koren
  • Junye Chen

Best Publications

  • The reusable holdout: Preserving validity in adaptive data analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Preserving Statistical Validity in Adaptive Data Analysis

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Cognitive computing building block: A versatile and efficient digital neuron model for neurosynaptic cores

    Andrew S. Cassidy;Paul Merolla;John V. Arthur;Steve K. Esser

  • Statistical Algorithms and a Lower Bound for Detecting Planted Cliques

    Vitaly Feldman;Elena Grigorescu;Lev Reyzin;Santosh S. Vempala

  • Does learning require memorization? a short tale about a long tail

    Vitaly Feldman

  • New Results for Learning Noisy Parities and Halfspaces

    V. Feldman;P. Gopalan;S. Khot;A.K. Ponnuswami

  • Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • Generalization in adaptive data analysis and holdout reuse

    Cynthia Dwork;Vitaly Feldman;Moritz Hardt;Toniann Pitassi

  • Amplification by shuffling: from local to central differential privacy via anonymity

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

    Vitaly Feldman;Chiyuan Zhang

  • Statistical algorithms and a lower bound for detecting planted cliques

    Vitaly Feldman;Elena Grigorescu;Lev Reyzin;Santosh Vempala

  • Agnostic Learning of Monomials by Halfspaces Is Hard

    Vitaly Feldman;Venkatesan Guruswami;Prasad Raghavendra;Yi Wu

  • Privacy Amplification by Iteration

    Vitaly Feldman;Ilya Mironov;Kunal Talwar;Abhradeep Thakurta

  • On using extended statistical queries to avoid membership queries

    Nader H. Bshouty;Vitaly Feldman

  • ON AGNOSTIC LEARNING OF PARITIES, MONOMIALS, AND HALFSPACES

    Vitaly Feldman;Parikshit Gopalan;Subhash Khot;Ashok Kumar Ponnuswami

  • Private Stochastic Convex Optimization with Optimal Rates

    Raef Bassily;Vitaly Feldman;Kunal Talwar;Abhradeep Guha Thakurta

  • High probability generalization bounds for uniformly stable algorithms with nearly optimal rate

    Vitaly Feldman;Jan Vondrak

  • Private stochastic convex optimization: optimal rates in linear time

    Vitaly Feldman;Tomer Koren;Kunal Talwar

  • Evolvability from learning algorithms

    Vitaly Feldman

  • On the Complexity of Random Satisfiability Problems with Planted Solutions

    Vitaly Feldman;Will Perkins;Santosh Vempala

  • Encode, Shuffle, Analyze Privacy Revisited: Formalizations and Empirical Evaluation.

    Úlfar Erlingsson;Vitaly Feldman;Ilya Mironov;Ananth Raghunathan

  • Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses

    Raef Bassily;Vitaly Feldman;Cristóbal Guzmán;Kunal Talwar

Frequent Co-Authors

Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Jan Vondrák
Jan Vondrák Stanford University
Kunal Talwar
Kunal Talwar Apple (United States)
Moritz Hardt
Moritz Hardt Max Planck Institute for Intelligent Systems
Cynthia Dwork
Cynthia Dwork Harvard University
Toniann Pitassi
Toniann Pitassi Columbia University
Rocco A. Servedio
Rocco A. Servedio Columbia University
Abhradeep Thakurta
Abhradeep Thakurta Google (United States)
Aaron Roth
Aaron Roth University of Pennsylvania
Omer Reingold
Omer Reingold Stanford University

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