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Ramakrishnan Srikant

Ramakrishnan Srikant

D-Index & Metrics

Computer Science

D-Index
54
Citations
88386
World Ranking
4414
National Ranking
2059

Overview

Ramakrishnan Srikant is affiliated with Google in the United States. Their recent research contributions focus primarily on reinforcement learning and related optimization techniques.

Their notable recent publications include:

  • Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization, 2024, arXiv (Cornell University)
  • Reinforcement Learning with Segment Feedback, 2025, arXiv (Cornell University)

Srikant frequently collaborates with a range of researchers, including Yihan Du, Anna Winnicki, Gal Dalal, and Shie Mannor. Each of these colleagues has coauthored multiple works alongside Srikant, reflecting a pattern of recurring collaboration.

Their publications have appeared mainly in venues such as arXiv (Cornell University), where both recent papers were published.

Best Publications

  • Fast algorithms for mining association rules

    Rakesh Agrawal;Ramakrishnan Srikant

  • Fast Algorithms for Mining Association Rules in Large Databases

    Rakesh Agrawal;Ramakrishnan Srikant

  • Mining sequential patterns

    R. Agrawal;R. Srikant

  • Privacy-preserving data mining

    Rakesh Agrawal;Ramakrishnan Srikant

  • Mining Sequential Patterns: Generalizations and Performance Improvements

    Ramakrishnan Srikant;Ramakrishnan Srikant;Rakesh Agrawal

  • Fast discovery of association rules

    Rakesh Agrawal;Heikki Mannila;Ramakrishnan Srikant;Hannu Toivonen

  • Mining quantitative association rules in large relational tables

    Ramakrishnan Srikant;Rakesh Agrawal

  • Mining Generalized Association Rules

    Ramakrishnan Srikant;Rakesh Agrawal

  • Advances in Knowledge Discovery and Data Mining

    Unknown

  • Order preserving encryption for numeric data

    Rakesh Agrawal;Jerry Kiernan;Ramakrishnan Srikant;Yirong Xu

  • Mining association rules with item constraints

    Ramakrishnan Srikant;Quoc Vu;Rakesh Agrawal

  • Scaling mining algorithms to large databases

    Paul Bradley;Johannes Gehrke;Raghu Ramakrishnan;Ramakrishnan Srikant

  • Privacy preserving mining of association rules

    Alexandre Evfimievski;Ramakrishnan Srikant;Rakesh Agrawal;Johannes Gehrke

  • Limiting privacy breaches in privacy preserving data mining

    Alexandre Evfimievski;Johannes Gehrke;Ramakrishnan Srikant

  • Hippocratic databases

    Rakesh Agrawal;Jerry Kiernan;Ramakrishnan Srikant;Yirong Xu

  • Scaling up all pairs similarity search

    Roberto J. Bayardo;Yiming Ma;Ramakrishnan Srikant

  • Information sharing across private databases

    Rakesh Agrawal;Alexandre Evfimievski;Ramakrishnan Srikant

  • Mining generalized association rules

    Ramakrishnan Srikant;Rakesh Agrawal

  • Range queries in OLAP data cubes

    Ching-Tien Ho;Rakesh Agrawal;Nimrod Megiddo;Ramakrishnan Srikant

  • Chapter 14 – Hippocratic Databases

    Rakesh Agrawal;Jerry Kiernan;Ramakrishnan Srikant;Yirong Xu

  • The Quest Data mining System

    Rakesh Agrawal;Manish Mehta;John Shafer;Ramakrishnan Srikant

Frequent Co-Authors

Rakesh Agrawal
Rakesh Agrawal Purdue University West Lafayette
Kyuseok Shim
Kyuseok Shim Seoul National University
Ramanathan V. Guha
Ramanathan V. Guha Google (United States)
Nimrod Megiddo
Nimrod Megiddo IBM (United States)
Johannes Gehrke
Johannes Gehrke Microsoft (United States)
Christos Faloutsos
Christos Faloutsos Carnegie Mellon University
Foster Provost
Foster Provost New York University
Zhen Liu
Zhen Liu Nokia (Finland)
Sunita Sarawagi
Sunita Sarawagi Indian Institute of Technology Bombay
Tomasz Imielinski
Tomasz Imielinski Rutgers, The State University of New Jersey

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