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Aditya Krishna Menon

Aditya Krishna Menon

D-Index & Metrics

Computer Science

D-Index
35
Citations
7350
World Ranking
11509
National Ranking
4730

Overview

Aditya Krishna Menon is affiliated with Google in the United States. Their research primarily falls within the domain of Computer Science, with a particular concentration on Artificial Intelligence. This focus includes subfields such as Computer Vision and Pattern Recognition, Information Systems, Computational Theory and Mathematics, and Biomedical Engineering.

The scientist's frequent publication venues include arXiv (Cornell University), where they have contributed extensively, Journal of Orthopaedic Case Reports, ACS Biomaterials Science & Engineering, Physicochemical Problems of Mineral Processing, and Chemistry of Materials.

Key topics covered in their work reflect diverse aspects of machine learning and related technologies:

  • Machine Learning and Data Classification
  • Domain Adaptation and Few-Shot Learning
  • Topic Modeling
  • Natural Language Processing Techniques
  • Machine Learning and Algorithms
  • Adversarial Robustness in Machine Learning
  • Text and Document Classification Technologies

Recent papers authored or co-authored by Aditya Krishna Menon provide insight into specific areas of their research focus. These include:

  • "Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers" (2020), published in ACS Biomaterials Science & Engineering
  • "Long-tail learning via logit adjustment" (2020), published on arXiv (Cornell University)
  • "Federated Learning with Only Positive Labels" (2020), published on arXiv (Cornell University)
  • "Self-supervised Learning for Large-scale Item Recommendations" (2020), published on arXiv (Cornell University)
  • "Does label smoothing mitigate label noise?" (2020), published on arXiv (Cornell University)

Their collaborative work is reflected in frequent co-authorship with several researchers, including:

  • Ankit Singh Rawat
  • Sanjiv Kumar
  • Wittawat Jitkrittum
  • Harikrishna Narasimhan

Best Publications

  • Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach

    Giorgio Patrini;Giorgio Patrini;Alessandro Rozza;Aditya Krishna Menon;Aditya Krishna Menon;Richard Nock;Richard Nock;Richard Nock

  • AutoRec: Autoencoders Meet Collaborative Filtering

    Suvash Sedhain;Aditya Krishna Menon;Scott Sanner;Lexing Xie

  • Link prediction via matrix factorization

    Aditya Krishna Menon;Charles Elkan

  • Long-tail learning via logit adjustment

    Aditya Krishna Menon;Sadeep Jayasumana;Ankit Singh Rawat;Himanshu Jain

  • Anomaly Detection using One-Class Neural Networks.

    Raghavendra Chalapathy;Aditya Krishna Menon;Sanjay Chawla

  • The cost of fairness in binary classification

    Aditya Krishna Menon;Robert C Williamson

  • Learning with symmetric label noise: the importance of being unhinged

    Brendan van Rooyen;Aditya Krishna Menon;Robert C. Williamson

  • Large-Scale Support Vector Machines: Algorithms and Theory

    Aditya Menon

  • Self-supervised Learning for Large-scale Item Recommendations

    Tiansheng Yao;Xinyang Yi;Derek Zhiyuan Cheng;Felix Yu

  • Learning from Corrupted Binary Labels via Class-Probability Estimation

    Aditya Menon;Brendan Van Rooyen;Cheng Soon Ong;Bob Williamson

  • Response prediction using collaborative filtering with hierarchies and side-information

    Aditya Krishna Menon;Krishna-Prasad Chitrapura;Sachin Garg;Deepak Agarwal

  • Robust, Deep and Inductive Anomaly Detection

    Raghavendra Chalapathy;Aditya Krishna Menon;Sanjay Chawla

  • A Machine Learning Framework for Programming by Example

    Aditya Menon;Omer Tamuz;Sumit Gulwani;Butler Lampson

  • Beam search algorithms for multilabel learning

    Abhishek Kumar;Shankar Vembu;Aditya Krishna Menon;Charles Elkan

  • Fast Algorithms for Approximating the Singular Value Decomposition

    Aditya Krishna Menon;Charles Elkan

  • Can gradient clipping mitigate label noise

    Aditya Krishna Menon;Ankit Singh Rawat;Sashank J. Reddi;Sanjiv Kumar

  • A Log-Linear Model with Latent Features for Dyadic Prediction

    Aditya Krishna Menon;Charles Elkan

  • On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance

    Aditya Menon;Harikrishna Narasimhan;Shivani Agarwal;Sanjay Chawla

  • Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

    Giorgio Patrini;Giorgio Patrini;Alessandro Rozza;Aditya Menon;Aditya Menon;Richard Nock;Richard Nock;Richard Nock

  • A colorful approach to text processing by example

    Kuat Yessenov;Shubham Tulsiani;Aditya Menon;Robert C. Miller

  • On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data

    Nan Lu;Gang Niu;Aditya Krishna Menon;Masashi Sugiyama

  • Low-Rank Linear Cold-Start Recommendation from Social Data.

    Suvash Sedhain;Aditya Krishna Menon;Scott Sanner;Lexing Xie

  • Federated Learning with Only Positive Labels

    Felix Xinnan Yu;Ankit Singh Rawat;Aditya Menon;Sanjiv Kumar

  • Complementary-Label Learning for Arbitrary Losses and Models

    Takashi Ishida;Gang Niu;Aditya Krishna Menon;Masashi Sugiyama

Frequent Co-Authors

Sanjiv Kumar
Sanjiv Kumar Google (United States)
Richard Nock
Richard Nock Australian National University
Robert C. Williamson
Robert C. Williamson University of Tübingen
Lexing Xie
Lexing Xie Australian National University
Felix X. Yu
Felix X. Yu Google (United States)
Sashank J. Reddi
Sashank J. Reddi Google (United States)
Charles Elkan
Charles Elkan University of California, San Diego
Sanjay Chawla
Sanjay Chawla Qatar Computing Research Institute
Xiaoqian Jiang
Xiaoqian Jiang The University of Texas Health Science Center at Houston
Lucila Ohno-Machado
Lucila Ohno-Machado University of California, San Diego

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