4 papers
Scalable Statistical Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla
Constructing confidence regions for stochastic gradient descent (SGD) ideally requires estimating the asymptotic covariance matrix, a severe computational bottleneck in high dimens…
On the Utility of Equal Batch Sizes for Inference in Stochastic Gradient Descent
Rahul Singh, Abhinek Shukla, Dootika Vats
Stochastic gradient descent (SGD) is an estimation tool for large data employed in machine learning and statistics. Due to the Markovian nature of the SGD process, inference is a c…
On estimating parameters of a multi-component Chirp Model with equal chirp rates
Abhinek Shukla, Debasis Kundu, Amit Mitra +1
Multi-component chirp signal models with equal chirp rates appear in various radar applications, e.g., synthetic aperture radar, echo signal of a rapid mobile target, etc. Many sub…
A Computationally Efficient algorithm to estimate the Parameters of a Two-Dimensional Chirp Model with the product term
Abhinek Shukla, Rhythm Grover, Debasis Kundu +1
Chirp signal models and their generalizations have been used to model many natural and man-made phenomena in signal processing and time series literature. In recent times, several…