Jordan Awan

According to our database, Jordan Awan authored at least 20 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Tutte polynomials for regular oriented matroids.

Jordan Awan, Olivier Bernardi

Discret. Math., January, 2024

Differentially Private Topological Data Analysis.

Taegyu Kang, Sehwan Kim, Jinwon Sohn, Jordan Awan

J. Mach. Learn. Res., 2024

Best Linear Unbiased Estimate from Privatized Histograms.

Jordan Awan, Adam Edwards, Paul Bartholomew, Andrew Sillers

CoRR, 2024

Statistical Inference for Privatized Data with Unknown Sample Size.

Jordan Awan, Andres Felipe Barrientos, Nianqiao Ju

CoRR, 2024

2023

Privacy-Aware Rejection Sampling.

Jordan Awan, Vinayak Rao

J. Mach. Learn. Res., 2023

Optimizing Noise for f-Differential Privacy via Anti-Concentration and Stochastic Dominance.

Jordan Awan, Aishwarya Ramasethu

CoRR, 2023

Simulation-based, Finite-sample Inference for Privatized Data.

Jordan Awan, Zhanyu Wang

CoRR, 2023

2022

Demicaps in AG(4, 3) and Maximal Cap Partitions.

Jordan Awan, Claire Frechette, Yumi Li, Elizabeth W. McMahon

Graphs Comb., 2022

Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies.

Zhanyu Wang, Guang Cheng, Jordan Awan

CoRR, 2022

Differentially Private Kolmogorov-Smirnov-Type Tests.

Jordan Awan, Yue Wang

CoRR, 2022

Data Augmentation MCMC for Bayesian Inference from Privatized Data.

Nianqiao Ju, Jordan Awan, Ruobin Gong, Vinayak Rao

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy.

Jordan Awan, Jinshuo Dong

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Canonical Noise Distributions and Private Hypothesis Tests.

Jordan Awan, Salil P. Vadhan

CoRR, 2021

2020

Differentially Private Inference for Binomial Data.

Jordan Awan, Aleksandra B. Slavkovic

J. Priv. Confidentiality, 2020

Tutte polynomials for directed graphs.

Jordan Awan, Olivier Bernardi

J. Comb. Theory, Ser. B, 2020

One Step to Efficient Synthetic Data.

Jordan Awan, Zhanrui Cai

CoRR, 2020

2019

KNG: The K-Norm Gradient Mechanism.

Matthew Reimherr, Jordan Awan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Elliptical Perturbations for Differential Privacy.

Matthew Reimherr, Jordan Awan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA.

Jordan Awan, Ana Kenney, Matthew Reimherr, Aleksandra B. Slavkovic

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Differentially Private Uniformly Most Powerful Tests for Binomial Data.

Jordan Awan, Aleksandra B. Slavkovic

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018