1 citations · 1 across the 9 of their papers we have counts for
11 papers
Epidemiological Causal Graph Identification: Challenges, Identifiability and Algorithms
Sambit Mishra, Yingying Wang, Christine K. Johnson +1
Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptio…
On the Identifiability of Mixed Ordinal and Exponential Family Causal DAGs under Linear Parametric Models
Sambit Mishra, Urbashi Mitra
The problem of identifiability in linear parametric models (LPMs) whose nodes follow either an ordered logit model or a regular one-parameter exponential family is evaluated. The r…
Causal Discovery in Equal Variance Linear Gaussian DAGs via SURE-Tuned Ridge Regression
Sambit Mishra, Urbashi Mitra
Recovering the directed acyclic graph (DAG) of a structural equation model (SEM) from observational data is a central problem in causal discovery. The iterative gradient descent an…
Atomic Hybrid Sparse/Diffuse Channel Estimation and Cramér-Rao Bounds Analysis
Lei Lyu, Maxime Ferreira Da Costa, Urbashi Mitra
In this paper, an atomic hybrid sparse/diffuse (aHSD) channel model in the frequency domain is proposed. Based on a structural analysis of the resolvable paths and diffuse scatteri…
Hybrid Atomic Norm Sparse/Diffuse Channel Estimation
Lei Lyu, Urbashi Mitra
In this paper, the hybrid sparse/diffuse (HSD) channel model in frequency domain is proposed. Based on the structural analysis on the resolvable paths and diffuse scattering statis…
Causal Link Discovery with Unequal Edge Error Tolerance
Joni Shaska, Urbashi Mitra
This paper proposes a novel framework for causal discovery with asymmetric error control, called Neyman-Pearson causal discovery. Despite the importance of applications where diffe…
Bayesian causal discovery: Posterior concentration and optimal detection
Valentinian Lungu, Joni Shaska, Ioannis Kontoyiannis +1
We consider the problem of Bayesian causal discovery for the standard model of linear structural equations with equivariant Gaussian noise. A uniform prior is placed on the space o…
Delay-Angle Information Spoofing for Channel State Information-Free Location-Privacy Enhancement
Jianxiu Li, Urbashi Mitra
In this paper, a delay-angle information spoofing (DAIS) strategy is proposed to enhance the location privacy at the physical layer. More precisely, the location-relevant delays an…
Optimized Parameter Design for Channel State Information-Free Location Spoofing
Jianxiu Li, Urbashi Mitra
In this paper, an augmented analysis of a delay-angle information spoofing (DAIS) is provided for location-privacy preservation, where the location-relevant delays and angles are a…
Channel State Information-Free Location-Privacy Enhancement: Delay-Angle Information Spoofing
Jianxiu Li, Urbashi Mitra
In this paper, a delay-angle information spoofing (DAIS) strategy is proposed for location-privacy enhancement. By shifting the location-relevant delays and angles without the aid…
Guaranteed Private Communication with Secret Block Structure
Maxime Ferreira Da Costa, Jianxiu Li, Urbashi Mitra
A novel private communication framework is proposed where privacy is induced by transmitting over a channel instances of linear inverse problems that are identifiable to the legiti…