Differentially Private Uniformly Most Powerful Tests for Binomial Data. - Jordan Awan; Aleksandra B. Slavkovic (NeurIPS, 2018)

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA. - Jordan Awan; Ana Kenney; Matthew Reimherr; Aleksandra B. Slavkovic (ICML, 2019)

Elliptical Perturbations for Differential Privacy. - Matthew Reimherr; Jordan Awan (NeurIPS, 2019)

KNG: The K-Norm Gradient Mechanism. - Matthew Reimherr; Jordan Awan (NeurIPS, 2019)

One Step to Efficient Synthetic Data. - Jordan Awan; Zhanrui Cai (CoRR, 2020)

Tutte polynomials for directed graphs. - Jordan Awan; Olivier Bernardi (J. Comb. Theory, Ser. B, 2020)

Differentially Private Inference for Binomial Data. - Jordan Awan; Aleksandra B. Slavkovic (J. Priv. Confidentiality, 2020)

Canonical Noise Distributions and Private Hypothesis Tests. - Jordan Awan; Salil P. Vadhan (CoRR, 2021)

Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy. - Jordan Awan; Jinshuo Dong (NeurIPS, 2022)

Data Augmentation MCMC for Bayesian Inference from Privatized Data. - Nianqiao Ju; Jordan Awan; Ruobin Gong; Vinayak Rao (NeurIPS, 2022)

Differentially Private Kolmogorov-Smirnov-Type Tests. - Jordan Awan; Yue Wang (CoRR, 2022)

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies. - Zhanyu Wang; Guang Cheng 003; Jordan Awan (CoRR, 2022)

Demicaps in AG(4, 3) and Maximal Cap Partitions. - Jordan Awan; Claire Frechette; Yumi Li; Elizabeth W. McMahon (Graphs Comb., 2022)

Simulation-based, Finite-sample Inference for Privatized Data. - Jordan Awan; Zhanyu Wang (CoRR, 2023)

Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance. - Jordan Awan; Aishwarya Ramasethu (CoRR, 2023)

Privacy-Aware Rejection Sampling. - Jordan Awan; Vinayak Rao (J. Mach. Learn. Res., 2023)

Statistical Inference for Privatized Data with Unknown Sample Size. - Jordan Awan; Andres Felipe Barrientos; Nianqiao Ju (CoRR, 2024)

Best Linear Unbiased Estimate from Privatized Histograms. - Jordan Awan; Adam Edwards; Paul Bartholomew; Andrew Sillers (CoRR, 2024)

Differentially Private Covariate Balancing Causal Inference. - Yuki Ohnishi; Jordan Awan (CoRR, 2024)

Formal Privacy Guarantees with Invariant Statistics. - Young Hyun Cho; Jordan Awan (CoRR, 2024)

Differentially Private Topological Data Analysis. - Taegyu Kang; Sehwan Kim; Jinwon Sohn; Jordan Awan (J. Mach. Learn. Res., 2024)

Tutte polynomials for regular oriented matroids. - Jordan Awan; Olivier Bernardi (Discret. Math., 2024)

Optimal Debiased Inference on Privatized Data via Indirect Estimation and Parametric Bootstrap. - Zhanyu Wang; Arin Chang; Jordan Awan (CoRR, 2025)