
New York, NY
Email: adityakmenon followed by google.com
Biography
I'm a Staff Research Scientist at Google.
I work on machine learning and its applications.
I completed my honours in Computer Science from the University of Sydney in 2006 under Sanjay Chawla. I completed my PhD in Computer Science from UC San Diego in 2013 under Charles Elkan. From 2013 - 2018, I held positions at NICTA, CSIRO Data61, and the Australian National University, working with Bob Williamson and other inimitable colleagues.
Here is a copy of my CV.
I completed my honours in Computer Science from the University of Sydney in 2006 under Sanjay Chawla. I completed my PhD in Computer Science from UC San Diego in 2013 under Charles Elkan. From 2013 - 2018, I held positions at NICTA, CSIRO Data61, and the Australian National University, working with Bob Williamson and other inimitable colleagues.
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:
- Weakly-supervised learning (e.g. learning from label noise, positive and unlabelled learning)
- Classification with real-world constraints (e.g. class imbalance, fairness)
- Relations amongst foundational problems (e.g. class-probability estimation, bipartite ranking)
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. (Liner notes are not peer-, or even coauthor-reviewed.)
Inspired by Bernard Chazelle's wonderful idea of "liner notes" for his papers, I've included some of my own for a few papers. (Liner notes are not peer-, or even coauthor-reviewed.)
- Long-tail learning via logit adjustment.
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar.
In International Conference on Learning Representations (ICLR), 2021.
[pdf]
- Can gradient clipping mitigate label noise?
Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, and Sanjiv Kumar.
In International Conference on Learning Representations (ICLR), Addis Ababa 2020.
[pdf]
- Fairness risk measures.
Robert C. Williamson and Aditya Krishna Menon.
In International Conference on Machine Learning (ICML), Long Beach, 2019.
[pdf]
- 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]