Jordan Awan

38 papers A* 7Journal 31
YearRankTypeTitle / Venue / Authors
2026 J jnl
Beyond Data Splitting: Full-Data Conformal Prediction by Differential Privacy.
CoRR
Young Hyun Cho, Jordan Awan
2026 J jnl
Near-Optimal Private Tests for Simple and MLR Hypotheses.
CoRR
Yu-Wei Chen, Raghu Pasupathy, Jordan Awan
2025 J jnl
Best Linear Unbiased Estimate from Privatized Contingency Tables.
J. Mach. Learn. Res.
Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers
2025 J jnl
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies.
J. Mach. Learn. Res.
Zhanyu Wang, Guang Cheng, Jordan Awan
2025 J jnl
Locally Private Causal Inference for Randomized Experiments.
J. Mach. Learn. Res.
Yuki Ohnishi, Jordan Awan
2025 J jnl
Optimal Debiased Inference on Privatized Data via Indirect Estimation and Parametric Bootstrap.
CoRR
Zhanyu Wang, Arin Chang, Jordan Awan
2025 A* conf
Optimal Survey Design for Private Mean Estimation.
ICML
Yu-Wei Chen, Raghu Pasupathy, Jordan Awan
2025 J jnl
Optimal Survey Design for Private Mean Estimation.
CoRR
Yu-Wei Chen, Raghu Pasupathy, Jordan Awan
2024 J jnl
Best Linear Unbiased Estimate from Privatized Histograms.
CoRR
Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers
2024 J jnl
Differentially Private Covariate Balancing Causal Inference.
CoRR
Yuki Ohnishi, Jordan Awan
2024 J jnl
Differentially Private Topological Data Analysis.
J. Mach. Learn. Res.
Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan
2024 J jnl
Formal Privacy Guarantees with Invariant Statistics.
CoRR
Young Hyun Cho, Jordan Awan
2024 J jnl
Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance.
J. Mach. Learn. Res.
Jordan Awan, Aishwarya Ramasethu
2024 J jnl
Statistical Inference for Privatized Data with Unknown Sample Size.
CoRR
Jordan Awan, Andres Felipe Barrientos, Nianqiao Ju
2024 J jnl
Tutte polynomials for regular oriented matroids.
Discret. Math.
Jordan Awan, Olivier Bernardi
2023 J jnl
Differentially Private Topological Data Analysis.
CoRR
Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan
2023 J jnl
Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance.
CoRR
Jordan Awan, Aishwarya Ramasethu
2023 J jnl
Privacy-Aware Rejection Sampling.
J. Mach. Learn. Res.
Jordan Awan, Vinayak Rao
2023 J jnl
Simulation-based, Finite-sample Inference for Privatized Data.
CoRR
Jordan Awan, Zhanyu Wang
2022 A* conf
Data Augmentation MCMC for Bayesian Inference from Privatized Data.
NeurIPS
Nianqiao Ju, Jordan Awan, Ruobin Gong, Vinayak Rao
2022 J jnl
Demicaps in AG(4, 3) and Maximal Cap Partitions.
Graphs Comb.
Jordan Awan, Claire Frechette, Yumi Li, Elizabeth W. McMahon
2022 J jnl
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies.
CoRR
Zhanyu Wang, Guang Cheng, Jordan Awan
2022 J jnl
Differentially Private Kolmogorov-Smirnov-Type Tests.
CoRR
Jordan Awan, Yue Wang
2022 A* conf
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy.
NeurIPS
Jordan Awan, Jinshuo Dong
2022 J jnl
Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy.
CoRR
Jordan Awan, Jinshuo Dong
2021 J jnl
Canonical Noise Distributions and Private Hypothesis Tests.
CoRR
Jordan Awan, Salil P. Vadhan
2021 J jnl
Privacy-Aware Rejection Sampling.
CoRR
Jordan Awan, Vinayak Rao
2020 J jnl
Differentially Private Inference for Binomial Data.
J. Priv. Confidentiality
Jordan Awan, Aleksandra B. Slavkovic
2020 J jnl
One Step to Efficient Synthetic Data.
CoRR
Jordan Awan, Zhanrui Cai
2020 J jnl
Tutte polynomials for directed graphs.
J. Comb. Theory, Ser. B
Jordan Awan, Olivier Bernardi
2019 A* conf
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA.
ICML
Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic
2019 J jnl
Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA.
CoRR
Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic
2019 J jnl
Differentially Private Inference for Binomial Data.
CoRR
Jordan Awan, Aleksandra B. Slavkovic
2019 A* conf
Elliptical Perturbations for Differential Privacy.
NeurIPS
Matthew Reimherr, Jordan Awan
2019 J jnl
Elliptical Perturbations for Differential Privacy.
CoRR
Matthew Reimherr, Jordan Awan
2019 A* conf
KNG: The K-Norm Gradient Mechanism.
NeurIPS
Matthew Reimherr, Jordan Awan
2019 J jnl
KNG: The K-Norm Gradient Mechanism.
CoRR
Matthew Reimherr, Jordan Awan
2018 A* conf
Differentially Private Uniformly Most Powerful Tests for Binomial Data.
NeurIPS
Jordan Awan, Aleksandra B. Slavkovic