no code implementations • 5 Feb 2021 • Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Menon, Sanjiv Kumar
By analyzing the relationship between churn and prediction confidences, we pursue an approach with two components for churn reduction.
1 code implementation • ICLR 2021 • Jingzhao Zhang, Aditya Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra
The label shift problem refers to the supervised learning setting where the train and test label distributions do not match.
1 code implementation • 25 Jul 2020 • Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Ting Chen, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger
Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.
no code implementations • NeurIPS 2020 • Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar
In this paper, we present, to our knowledge, the first theoretical guarantees for learning a classifier in hyperbolic rather than Euclidean space.
2 code implementations • CVPR 2017 • Giorgio Patrini, Alessandro Rozza, Aditya Menon, Richard Nock, Lizhen Qu
We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise.
Ranked #2 on Image Classification on Clothing1M (using clean data) (using extra training data)