World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
11781
World Ranking
7421
National Ranking
3234

Overview

S. V. N. Vishwanathan is affiliated with Purdue University West Lafayette in the United States and focuses their research primarily in the field of Computer Science. Their work spans various subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Computer Science Applications, and Information Systems.

The scientist's research topics cover:

  • Advanced Image and Video Retrieval Techniques
  • Topic Modeling
  • Text and Document Classification Technologies
  • Domain Adaptation and Few-Shot Learning
  • Natural Language Processing Techniques
  • Advanced Bandit Algorithms Research
  • Auction Theory and Applications

S. V. N. Vishwanathan has contributed multiple papers mainly published via arXiv (Cornell University), which is the frequent venue for their research outputs. Selected recent publications include:

  • Toward Understanding Privileged Features Distillation in Learning-to-Rank, 2022, arXiv (Cornell University)
  • DS-FACTO: Doubly Separable Factorization Machines, 2020, arXiv (Cornell University)
  • Embracing Structure in Data for Billion-Scale Semantic Product Search, 2021, arXiv (Cornell University)
  • On the Value of Behavioral Representations for Dense Retrieval, 2022, arXiv (Cornell University)
  • Retrieval-augmented Encoders for Extreme Multi-label Text Classification, 2025, arXiv (Cornell University)

Throughout their career, Vishwanathan has worked with various co-authors, including:

  • Sujay Sanghavi
  • Choon Hui Teo
  • Aashiq Muhamed
  • Sriram Srinivasan
  • Choon-Hui Teo

The scientist's academic focus incorporates diverse topics mostly related to machine learning models, large-scale data retrieval and classification systems, and efficient algorithm design within artificial intelligence domains.

Best Publications

  • Protein function prediction via graph kernels

    Karsten M. Borgwardt;Cheng Soon Ong;Stefan Schönauer;S. V. N. Vishwanathan

  • Deep Graph Kernels

    Pinar Yanardag;S.V.N. Vishwanathan

  • Graph Kernels

    S. V. N. Vishwanathan;Nicol N. Schraudolph;Risi Kondor;Karsten M. Borgwardt

  • Efficient Graphlet Kernels for Large Graph Comparison

    Nino Sherashidze;S. V. N. Vishwanathan;Tobias H. Petri;Kurt Mehlhorn

  • Predicting Structured Data

    GH Bakir;T Hofmann;B Schölkopf;Smola Aj, Taskar, B

  • Accelerated training of conditional random fields with stochastic gradient methods

    S. V. N. Vishwanathan;Nicol N. Schraudolph;Mark W. Schmidt;Kevin P. Murphy

  • Kernel methods for missing variables

    Alexander J. Smola;S. V. N. Vishwanathan;Thomas Hofmann

  • Fast Kernels for String and Tree Matching

    Alex J. Smola;S.v.n. Vishwanathan

  • Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties

    Feng Yan;Shreyas Sundaram;S. V. N. Vishwanathan;Yuan Qi

  • Hash Kernels for Structured Data

    Qinfeng Shi;James Petterson;Gideon Dror;John Langford

  • SSVM: a simple SVM algorithm

    S.V.M. Vishwanathan;M. Narasimha Murty

  • Multiple Kernel Learning and the SMO Algorithm

    Zhaonan Sun;Nawanol Ampornpunt;Manik Varma;S.v.n. Vishwanathan

  • A scalable modular convex solver for regularized risk minimization

    Choon Hui Teo;Alex Smola;S. V.N. Vishwanathan;Quoc Viet Le

  • NOMAD: non-locking, stochastic multi-machine algorithm for asynchronous and decentralized matrix completion

    Hyokun Yun;Hsiang-Fu Yu;Cho-Jui Hsieh;S. V. N. Vishwanathan

  • Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes

    S. V. Vishwanathan;Alexander J. Smola;René Vidal

  • Predicting Structured Data (Neural Information Processing)

    Gükhan H. Bakir;Thomas Hofmann;Bernhard Schölkopf;Alexander J. Smola

  • Bundle Methods for Machine Learning

    Quoc V. Le;Alex J. Smola;S.v.n. Vishwanathan

  • Fast Computation of Graph Kernels

    Karsten M. Borgwardt;Nicol N. Schraudolph;S.v.n. Vishwanathan

  • Large Scale Max-Margin Multi-Label Classification with Priors

    Bharath Hariharan;Lihi Zelnik-manor;Manik Varma;S.v.n. Vishwanathan

  • Fast Iterative Kernel Principal Component Analysis

    Simon Günter;Nicol N. Schraudolph;S. V. N. Vishwanathan

  • Energy-Based Models

    Gökhan BakIr;Thomas Hofmann;Bernhard Schölkopf;Alexander J. Smola

Frequent Co-Authors

Alexander J. Smola
Alexander J. Smola Amazon (United States)
Nicol N. Schraudolph
Nicol N. Schraudolph Dalle Molle Institute for Artificial Intelligence Research
Thomas Hofmann
Thomas Hofmann ETH Zurich
Bernhard Schölkopf
Bernhard Schölkopf Max Planck Institute for Intelligent Systems
Ben Taskar
Ben Taskar University of Washington
Karsten M. Borgwardt
Karsten M. Borgwardt Max Planck Institute of Biochemistry
Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)
Manfred K. Warmuth
Manfred K. Warmuth Google (United States)
Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Jan P. Allebach
Jan P. Allebach Purdue University West Lafayette

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