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ORCIDJordan Awan
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2020 – today
- 2026
[i22]Yu-Wei Chen, Raghu Pasupathy, Jordan Awan:
Near-Optimal Private Tests for Simple and MLR Hypotheses. CoRR abs/2601.21959 (2026)- [i21]Young Hyun Cho, Jordan Awan:
Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy. CoRR abs/2603.07522 (2026) - [i20]Behrooz Moosavi Ramezanzadeh
, Jordan Awan:
Public Good Provision under Locally Private Signals. CoRR abs/2606.24013 (2026) - 2025
- [j10]Yuki Ohnishi, Jordan Awan:
Locally Private Causal Inference for Randomized Experiments. J. Mach. Learn. Res. 26: 14:1-14:40 (2025) - [j9]Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers:
Best Linear Unbiased Estimate from Privatized Contingency Tables. J. Mach. Learn. Res. 26: 174:1-174:41 (2025) - [j8]Zhanyu Wang, Guang Cheng, Jordan Awan:
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. J. Mach. Learn. Res. 26: 257:1-257:57 (2025) - [c7]Yu-Wei Chen, Raghu Pasupathy, Jordan Awan:
Optimal Survey Design for Private Mean Estimation. ICML 2025 - [i19]Yu-Wei Chen, Raghu Pasupathy, Jordan Awan:
Optimal Survey Design for Private Mean Estimation. CoRR abs/2501.18121 (2025) - [i18]Zhanyu Wang, Arin Chang, Jordan Awan:
Optimal Debiased Inference on Privatized Data via Indirect Estimation and Parametric Bootstrap. CoRR abs/2507.10746 (2025) - 2024
- [j7]Jordan Awan, Olivier Bernardi:
Tutte polynomials for regular oriented matroids. Discret. Math. 347(1): 113687 (2024) - [j6]Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan:
Differentially Private Topological Data Analysis. J. Mach. Learn. Res. 25: 189:1-189:42 (2024) - [j5]Jordan Awan, Aishwarya Ramasethu:
Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance. J. Mach. Learn. Res. 25: 351:1-351:32 (2024) - [i17]Jordan Awan, Andres Felipe Barrientos, Nianqiao Ju:
Statistical Inference for Privatized Data with Unknown Sample Size. CoRR abs/2406.06231 (2024) - [i16]Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers:
Best Linear Unbiased Estimate from Privatized Histograms. CoRR abs/2409.04387 (2024) - [i15]Yuki Ohnishi, Jordan Awan:
Differentially Private Covariate Balancing Causal Inference. CoRR abs/2410.14789 (2024) - [i14]Young Hyun Cho, Jordan Awan:
Formal Privacy Guarantees with Invariant Statistics. CoRR abs/2410.17468 (2024) - 2023
- [j4]Jordan Awan, Vinayak Rao:
Privacy-Aware Rejection Sampling. J. Mach. Learn. Res. 24: 74:1-74:32 (2023) - [i13]Jordan Awan, Zhanyu Wang:
Simulation-based, Finite-sample Inference for Privatized Data. CoRR abs/2303.05328 (2023) - [i12]Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan:
Differentially Private Topological Data Analysis. CoRR abs/2305.03609 (2023) - [i11]Jordan Awan, Aishwarya Ramasethu:
Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance. CoRR abs/2308.08343 (2023) - 2022
- [j3]Jordan Awan, Claire Frechette, Yumi Li, Elizabeth W. McMahon:
Demicaps in AG(4, 3) and Maximal Cap Partitions. Graphs Comb. 38(6): 193 (2022) - [c6]Jordan Awan, Jinshuo Dong:
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy. NeurIPS 2022 - [c5]Nianqiao Ju, Jordan Awan, Ruobin Gong, Vinayak Rao:
Data Augmentation MCMC for Bayesian Inference from Privatized Data. NeurIPS 2022 - [i10]Jordan Awan, Jinshuo Dong:
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy. CoRR abs/2206.04572 (2022) - [i9]Jordan Awan, Yue Wang:
Differentially Private Kolmogorov-Smirnov-Type Tests. CoRR abs/2208.06236 (2022) - [i8]Zhanyu Wang, Guang Cheng, Jordan Awan:
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. CoRR abs/2210.06140 (2022) - 2021
- [i7]Jordan Awan, Vinayak Rao:
Privacy-Aware Rejection Sampling. CoRR abs/2108.00965 (2021) - [i6]Jordan Awan, Salil P. Vadhan:
Canonical Noise Distributions and Private Hypothesis Tests. CoRR abs/2108.04303 (2021) - 2020
- [j2]Jordan Awan, Olivier Bernardi:
Tutte polynomials for directed graphs. J. Comb. Theory, Ser. B 140: 192-247 (2020) - [j1]Jordan Awan, Aleksandra B. Slavkovic:
Differentially Private Inference for Binomial Data. J. Priv. Confidentiality 10(1) (2020) - [i5]Jordan Awan, Zhanrui Cai:
One Step to Efficient Synthetic Data. CoRR abs/2006.02397 (2020)
2010 – 2019
- 2019
- [c4]Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic:
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. ICML 2019: 374-384 - [c3]Matthew Reimherr, Jordan Awan:
Elliptical Perturbations for Differential Privacy. NeurIPS 2019: 10185-10196 - [c2]Matthew Reimherr, Jordan Awan:
KNG: The K-Norm Gradient Mechanism. NeurIPS 2019: 10208-10219 - [i4]Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic:
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. CoRR abs/1901.10864 (2019) - [i3]Jordan Awan, Aleksandra B. Slavkovic:
Differentially Private Inference for Binomial Data. CoRR abs/1904.00459 (2019) - [i2]Matthew Reimherr, Jordan Awan:
Elliptical Perturbations for Differential Privacy. CoRR abs/1905.09420 (2019) - [i1]Matthew Reimherr, Jordan Awan:
KNG: The K-Norm Gradient Mechanism. CoRR abs/1905.09436 (2019) - 2018
- [c1]Jordan Awan, Aleksandra B. Slavkovic:
Differentially Private Uniformly Most Powerful Tests for Binomial Data. NeurIPS 2018: 4212-4222
Coauthor Index
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last updated on 2026-08-04 23:08 CEST by the dblp team
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