17 citations · 49 across the 11 of their papers we have counts for
12 papers
Integer Subspace Differential Privacy
Prathamesh Dharangutte, Jie Gao, Ruobin Gong +1
We propose new differential privacy solutions for when external \emph{invariants} and \emph{integer} constraints are simultaneously enforced on the data product. These requirements…
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Nianqiao Ju, Jordan A. Awan, Ruobin Gong +1
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform…
Six Statistical Senses
Radu V. Craiu, Ruobin Gong, Xiao-Li Meng
This article proposes a set of categories, each one representing a particular distillation of important statistical ideas. Each category is labeled a "sense" because we think of th…
Dynamic Precise and Imprecise Probability Kinematics
Michele Caprio, Ruobin Gong
We introduce dynamic probability kinematics (DPK), a method for an agent to mechanically update subjective beliefs in the presence of partial information. We then generalize DPK to…
Subspace Differential Privacy
Jie Gao, Ruobin Gong, Fang-Yi Yu
Many data applications have certain invariant constraints due to practical needs. Data curators who employ differential privacy need to respect such constraints on the sanitized da…
Congenial Differential Privacy under Mandated Disclosure
Ruobin Gong, Xiao-Li Meng
Differentially private data releases are often required to satisfy a set of external constraints that reflect the legal, ethical, and logical mandates to which the data curator is…
Transparent Privacy is Principled Privacy
Ruobin Gong
In a technical treatment, this article establishes the necessity of transparent privacy for drawing unbiased statistical inference for a wide range of scientific questions. Transpa…
Geometric Conditions for the Discrepant Posterior Phenomenon and Connections to Simpson's Paradox
Yang Chen, Ruobin Gong, Min-ge Xie
The discrepant posterior phenomenon (DPP) is a counter-intuitive phenomenon that can frequently occur in a Bayesian analysis of multivariate parameters. It refers to the phenomenon…
A Gibbs sampler for a class of random convex polytopes
Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen +1
We present a Gibbs sampler for the Dempster-Shafer (DS) approach to statistical inference for Categorical distributions. The DS framework extends the Bayesian approach, allows in p…
Exact Inference with Approximate Computation for Differentially Private Data via Perturbations
Ruobin Gong
This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private…
Simultaneous Inference Under the Vacuous Orientation Assumption
Ruobin Gong
I propose a novel approach to simultaneous inference that alleviates the need to specify a correlational structure among marginal errors. The vacuous orientation assumption retains…
Judicious Judgment Meets Unsettling Updating: Dilation, Sure Loss, and Simpson's Paradox
Ruobin Gong, Xiao-Li Meng
Statistical learning using imprecise probabilities is gaining more attention because it presents an alternative strategy for reducing irreplicable findings by freeing the user from…