Menon, Aditya

14 publications

ICML 2022 In Defense of Dual-Encoders for Neural Ranking Aditya Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank Reddi, Sanjiv Kumar

AISTATS 2021 RankDistil: Knowledge Distillation for Ranking Sashank Reddi, Rama Kumar Pasumarthi, Aditya Menon, Ankit Singh Rawat, Felix Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar

ICML 2020 Does Label Smoothing Mitigate Label Noise? Michal Lukasik, Srinadh Bhojanapalli, Aditya Menon, Sanjiv Kumar

ICML 2020 Federated Learning with Only Positive Labels Felix Yu, Ankit Singh Rawat, Aditya Menon, Sanjiv Kumar

ICML 2020 Supervised Learning: No Loss No Cry Richard Nock, Aditya Menon

ICML 2019 Complementary-Label Learning for Arbitrary Losses and Models Takashi Ishida, Gang Niu, Aditya Menon, Masashi Sugiyama

ICML 2019 Fairness Risk Measures Robert Williamson, Aditya Menon

ICML 2019 Monge Blunts Bayes: Hardness Results for Adversarial Training Zac Cranko, Aditya Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder

NeurIPS 2016 A Scaled Bregman Theorem with Applications Richard Nock, Aditya Menon, Cheng Soon Ong

ICML 2016 Linking Losses for Density Ratio and Class-Probability Estimation Aditya Menon, Cheng Soon Ong

ICML 2015 Learning from Corrupted Binary Labels via Class-Probability Estimation Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, Bob Williamson

NeurIPS 2015 Learning with Symmetric Label Noise: The Importance of Being Unhinged Brendan van Rooyen, Aditya Menon, Robert C. Williamson

ICML 2013 A Machine Learning Framework for Programming by Example Aditya Menon, Omer Tamuz, Sumit Gulwani, Butler Lampson, Adam Kalai

ICML 2013 On the Statistical Consistency of Algorithms for Binary Classification Under Class Imbalance Aditya Menon, Harikrishna Narasimhan, Shivani Agarwal, Sanjay Chawla