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ORCIDPragya Sur
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- affiliation: Harvard University, Cambridge, MA, USA
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2020 – today
- 2024
[i11]John C. Duchi, Suyash Gupta, Kuanhao Jiang, Pragya Sur:
Predictive Inference in Multi-environment Scenarios. CoRR abs/2403.16336 (2024)- [i10]Kevin Luo, Yufan Li, Pragya Sur:
ROTI-GCV: Generalized Cross-Validation for right-ROTationally Invariant Data. CoRR abs/2406.11666 (2024) - [i9]Yanke Song, Sohom Bhattacharya, Pragya Sur:
Generalization error of min-norm interpolators in transfer learning. CoRR abs/2406.13944 (2024) - 2023
- [i8]Yufan Li, Pragya Sur:
Spectrum-Aware Adjustment: A New Debiasing Framework with Applications to Principal Components Regression. CoRR abs/2309.07810 (2023) - 2022
- [c2]Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS 2022 - [i7]Tengyuan Liang, Subhabrata Sen, Pragya Sur:
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix Models. CoRR abs/2204.04476 (2022) - [i6]Cathy Shyr, Pragya Sur, Giovanni Parmigiani, Prasad Patil:
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling. CoRR abs/2207.04588 (2022) - [i5]Lijia Zhou
, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR abs/2210.12082 (2022) - 2020
- [c1]Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, Pragya Sur:
Abstracting Fairness: Oracles, Metrics, and Interpretability. FORC 2020: 8:1-8:16 - [i4]Tengyuan Liang, Pragya Sur:
A Precise High-Dimensional Asymptotic Theory for Boosting and Min-L1-Norm Interpolated Classifiers. CoRR abs/2002.01586 (2020) - [i3]Cynthia Dwork, Christina Ilvento, Guy N. Rothblum, Pragya Sur:
Abstracting Fairness: Oracles, Metrics, and Interpretability. CoRR abs/2004.01840 (2020) - [i2]Zhun Deng, Frances Ding, Cynthia Dwork, Rachel Hong, Giovanni Parmigiani, Prasad Patil, Pragya Sur:
Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representations. CoRR abs/2006.11478 (2020)
2010 – 2019
- 2017
- [i1]Pragya Sur, Yuxin Chen, Emmanuel J. Candès:
The Likelihood Ratio Test in High-Dimensional Logistic Regression Is Asymptotically a Rescaled Chi-Square. CoRR abs/1706.01191 (2017)
Coauthor Index
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last updated on 2024-10-02 21:39 CEST by the dblp team
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