| Making deep neural networks robust to label noise: A loss correction approach G Patrini, A Rozza, A Krishna Menon, R Nock, L Qu Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1791 | 2017 |
| Autorec: Autoencoders meet collaborative filtering S Sedhain, AK Menon, S Sanner, L Xie Proceedings of the 24th international conference on World Wide Web, 111-112, 2015 | 1523 | 2015 |
| Long-tail learning via logit adjustment AK Menon, S Jayasumana, AS Rawat, H Jain, A Veit, S Kumar arXiv preprint arXiv:2007.07314, 2020 | 831 | 2020 |
| Link prediction via matrix factorization AK Menon, C Elkan Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 723 | 2011 |
| Anomaly Detection Using One-Class Neural Networks R Chalapathy arXiv preprint arXiv:1802.06360, 2018 | 582 | 2018 |
| The cost of fairness in binary classification AK Menon, RC Williamson Conference on Fairness, accountability and transparency, 107-118, 2018 | 534 | 2018 |
| Does label smoothing mitigate label noise? M Lukasik, S Bhojanapalli, A Menon, S Kumar International Conference on Machine Learning, 6448-6458, 2020 | 414 | 2020 |
| Learning with symmetric label noise: The importance of being unhinged B Van Rooyen, A Menon, RC Williamson Advances in neural information processing systems 28, 2015 | 379 | 2015 |
| Learning from corrupted binary labels via class-probability estimation A Menon, B Van Rooyen, CS Ong, B Williamson International conference on machine learning, 125-134, 2015 | 284 | 2015 |
| Self-supervised learning for large-scale item recommendations T Yao, X Yi, DZ Cheng, F Yu, T Chen, A Menon, L Hong, EH Chi, S Tjoa, ... Proceedings of the 30th ACM international conference on information …, 2021 | 262 | 2021 |
| Robust, deep and inductive anomaly detection R Chalapathy, AK Menon, S Chawla Machine Learning and Knowledge Discovery in Databases: European Conference …, 2017 | 217 | 2017 |
| A machine learning framework for programming by example A Menon, O Tamuz, S Gulwani, B Lampson, A Kalai International Conference on Machine Learning, 187-195, 2013 | 204 | 2013 |
| Response prediction using collaborative filtering with hierarchies and side-information AK Menon, KP Chitrapura, S Garg, D Agarwal, N Kota Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 196 | 2011 |
| Can gradient clipping mitigate label noise? AK Menon, AS Rawat, SJ Reddi, S Kumar International Conference on Learning Representations, 2020 | 182 | 2020 |
| Fairness risk measures R Williamson, A Menon International conference on machine learning, 6786-6797, 2019 | 163 | 2019 |
| On the statistical consistency of algorithms for binary classification under class imbalance A Menon, H Narasimhan, S Agarwal, S Chawla International Conference on Machine Learning, 603-611, 2013 | 152 | 2013 |
| Federated learning with only positive labels F Yu, AS Rawat, A Menon, S Kumar International Conference on Machine Learning, 10946-10956, 2020 | 120 | 2020 |
| Beam search algorithms for multilabel learning A Kumar, S Vembu, AK Menon, C Elkan Machine learning 92, 65-89, 2013 | 118 | 2013 |
| Complementary-label learning for arbitrary losses and models T Ishida, G Niu, A Menon, M Sugiyama International conference on machine learning, 2971-2980, 2019 | 114 | 2019 |
| A statistical perspective on distillation AK Menon, AS Rawat, S Reddi, S Kim, S Kumar International Conference on Machine Learning, 7632-7642, 2021 | 99 | 2021 |
Sanjiv KumarGoogle Fellow, VP, Google ResearchVerified email at google.com
