Research School of Computer Science

The Australian National University (ANU)

Canberra, Australia

Email: aditya.menon followed by anu.edu.au


I'm a Fellow at the ANU Research School of Computer Science. I work on machine learning and its applications.

I completed my honours in Computer Science from the University of Sydney in 2006 under the supervision of Sanjay Chawla. I completed my PhD in Computer Science from the University of California, San Diego in 2013 under the supervision of Charles Elkan. From May 2013 -- June 2016, I was a researcher in the machine learning group at NICTA. From June 2016 -- Dec 2017, I was a senior research scientist at CSIRO Data61.

Starting January 2018, I am a Fellow at the Australian National University working with Lexing Xie and Bob Williamson.

Here is a copy of my CV.

Research interests

I am broadly interested in the design and analysis of machine learning algorithms for (weakly) supervised learning problems occurring in practice. Specific areas of interest include:

Selected publications

Below are a few representative publications. For a full list, see here.

Inspired by Bernard Chazelle's wonderful idea of "liner notes" for his papers, I've included some of my own for a few papers. (These are not peer-, or even coauthor-reviewed.)
  • Learning from binary labels with instance-dependent corruption.
    Aditya Krishna Menon, Brendan van Rooyen, and Nagarajan Natarajan.
    arXiV Preprint.

  • Low-rank linear cold-start recommendation from social data.
    Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie, and Darius Braziunas.
    In AAAI Conference on Artificial Intelligence (AAAI), San Francisco, 2017.

  • Bipartite ranking: a risk-theoretic perspective.
    Aditya Krishna Menon and Robert C. Williamson.
    In Journal of Machine Learning Research (JMLR), Volume 17, Issue 195. 2016.
    [pdf] [liner notes]

  • Linking losses for density ratio and class-probability estimation.
    Aditya Krishna Menon and Cheng Soon Ong.
    In International Conference on Machine Learning (ICML), New York City, 2016.
    [pdf] [slides] [poster] [code] [liner notes]

  • Learning from corrupted binary labels via class-probability estimation.
    Aditya Krishna Menon, Brendan van Rooyen, Cheng Soon Ong and Robert C. Williamson.
    In International Conference on Machine Learning (ICML), Lille, 2015.
    [pdf] [slides] [poster] [code] [liner notes]