15 papers
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…
Maximum Likelihood Reconstruction for Multi-Look Digital Holography with Markov-Modeled Speckle Correlation
Xi Chen, Arian Maleki, Shirin Jalali
Multi-look acquisition is a widely used strategy for reducing speckle noise in coherent imaging systems such as digital holography. By acquiring multiple measurements, speckle can…
Monte Carlo Maximum Likelihood Reconstruction for Digital Holography with Speckle
Xi Chen, Arian Maleki, Shirin Jalali
In coherent imaging, speckle is statistically modeled as multiplicative noise, posing a fundamental challenge for image reconstruction. While maximum likelihood estimation (MLE) pr…
Imperfect Influence, Preserved Rankings: A Theory of TRAK for Data Attribution
Han Tong, Shubhangi Ghosh, Haolin Zou +1
Data attribution, tracing a model's prediction back to specific training data, is an important tool for interpreting sophisticated AI models. The widely used TRAK algorithm address…
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…