96 citations · 298 across the 54 of their papers we have counts for
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Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
Smoothed functional (SF) schemes for gradient estimation are known to be efficient in stochastic optimization algorithms, specially when the objective is to improve the performance…
Generative Maximum Entropy Learning for Multiclass Classification
Ambedkar Dukkipati, Gaurav Pandey, Debarghya Ghoshdastidar +2
Maximum entropy approach to classification is very well studied in applied statistics and machine learning and almost all the methods that exists in literature are discriminative i…
On Power-law Kernels, corresponding Reproducing Kernel Hilbert Space and Applications
Debarghya Ghoshdastidar, Ambedkar Dukkipati
The role of kernels is central to machine learning. Motivated by the importance of power-law distributions in statistical modeling, in this paper, we propose the notion of power-la…
q-Gaussian based Smoothed Functional Algorithm for Stochastic Optimization
Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
The q-Gaussian distribution results from maximizing certain generalizations of Shannon entropy under some constraints. The importance of q-Gaussian distributions stems from the fac…