6 papers · 1 filter
A note on the minimax risk of sparse linear regression
Yilin Guo, Shubhangi Ghosh, Haolei Weng +1
Sparse linear regression is one of the classical and extensively studied problems in high-dimensional statistics and compressed sensing. Despite the substantial body of literature…
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
Infinitely divisible privacy and beyond I: resolution of the conjecture
Aaradhya Pandey, Arian Maleki, Sanjeev Kulkarni
Differential privacy is increasingly formalized through the lens of hypothesis testing via the robust and interpretable -DP framework, where privacy guarantees are encoded by a…
Minimax Analysis of Estimation Problems in Coherent Imaging
Hao Xing, Soham Jana, Arian Maleki
Unlike conventional imaging modalities, such as magnetic resonance imaging, which are often well described by a linear regression framework, coherent imaging systems follow a signi…
Is speckle noise more challenging to mitigate than additive noise?
Reihaneh Malekian, Hao Xing, Arian Maleki
We study the problem of estimating a function in the presence of both speckle and additive noises, commonly referred to as the de-speckling problem. Although additive noise has bee…
Signal-to-noise ratio aware minimax analysis of sparse linear regression
Shubhangi Ghosh, Yilin Guo, Haolei Weng +1
We consider parameter estimation under sparse linear regression -- an extensively studied problem in high-dimensional statistics and compressed sensing. While the minimax framework…