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Pranjal Awasthi

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Semi-supervised Active Regression

Jun 12, 2021
Fnu Devvrit, Nived Rajaraman, Pranjal Awasthi

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Neural Active Learning with Performance Guarantees

Jun 06, 2021
Pranjal Awasthi, Christoph Dann, Claudio Gentile, Ayush Sekhari, Zhilei Wang

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Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective

May 20, 2021
Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel

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A Finer Calibration Analysis for Adversarial Robustness

May 06, 2021
Pranjal Awasthi, Anqi Mao, Mehryar Mohri, Yutao Zhong

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Calibration and Consistency of Adversarial Surrogate Losses

May 04, 2021
Pranjal Awasthi, Natalie Frank, Anqi Mao, Mehryar Mohri, Yutao Zhong

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A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness

Mar 03, 2021
Jacob Abernethy, Pranjal Awasthi, Satyen Kale

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Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Feb 16, 2021
Pranjal Awasthi, Alex Beutel, Matthaeus Kleindessner, Jamie Morgenstern, Xuezhi Wang

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Adversarial Robustness Across Representation Spaces

Dec 01, 2020
Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan

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