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Maya R. Gupta
- > Home > Persons > Maya R. Gupta
Publications
- 2021
- [c74]Heinrich Jiang, Maya R. Gupta:
Bootstrapping for Batch Active Sampling. KDD 2021: 3086-3096 - 2020
- [c71]Dara Bahri, Heinrich Jiang, Maya R. Gupta:
Deep k-NN for Noisy Labels. ICML 2020: 540-550 - [i18]Dara Bahri, Heinrich Jiang, Maya R. Gupta:
Deep k-NN for Noisy Labels. CoRR abs/2004.12289 (2020) - 2019
- [j23]Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Lutong Wang, Taman Narayan, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. J. Mach. Learn. Res. 20: 172:1-172:59 (2019) - [c67]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu:
Shape Constraints for Set Functions. ICML 2019: 1388-1396 - [c66]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML 2019: 1397-1405 - [i17]Heinrich Jiang, Maya R. Gupta:
Minimum-Margin Active Learning. CoRR abs/1906.00025 (2019) - 2018
- [c61]Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. NeurIPS 2018: 5546-5557 - [i13]Heinrich Jiang, Been Kim, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. CoRR abs/1805.11783 (2018) - [i12]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu:
Interpretable Set Functions. CoRR abs/1806.00050 (2018) - [i9]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR abs/1807.00028 (2018) - [i8]Andrew Cotter, Heinrich Jiang, Serena Lutong Wang, Taman Narayan, Maya R. Gupta, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. CoRR abs/1809.04198 (2018)
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