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stat.ML2026
Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference
Hanli Xu, Fengxiang He, Sarat Moka
Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies distributional ``local-mass be…
stat.ML2025
A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection
Sarat Moka, Matias Quiroz, Vali Asimit +1
Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse port…
stat.ML2021
Variance Reduction for Matrix Computations with Applications to Gaussian Processes
Anant Mathur, Sarat Moka, Zdravko Botev
In addition to recent developments in computing speed and memory, methodological advances have contributed to significant gains in the performance of stochastic simulation. In this…