2 citations · 3 across the 18 of their papers we have counts for
20 papers
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…
Uniform Sampling of Proper Graph Colorings via Soft Coloring and Partial Rejection Sampling
Sarat Moka, Ava Vahedi
We present a new algorithm for the exact uniform sampling of proper \(k\)-colorings of a graph on \(n\) vertices with maximum degree~\(Δ\). The algorithm is based on partial reject…
Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications
Anant Mathur, Benoit Liquet, Samuel Muller +1
High-dimensional biomedical studies require models that are simultaneously accurate, sparse, and interpretable, yet exact best subset selection for generalized linear models is com…
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…
Efficient Rare-Event Simulation for Random Geometric Graphs via Importance Sampling
Sarat Moka, Christian Hirsch, Volker Schmidt +1
Random geometric graphs defined on Euclidean subspaces, also called Gilbert graphs, are widely used to model spatially embedded networks across various domains. In such graphs, nod…
Rating Players of Counter-Strike: Global Offensive Based on Plus/Minus value
Hongyu Xu, Sarat Moka
We propose a player rating mechanism for Counter-Strike: Global Offensive (CS ), a popular e-sport, by analyzing players' Plus/Minus values. The Plus/Minus value represents the ave…
Continuous Optimization for Offline Change Point Detection and Estimation
Hans Reimann, Sarat Moka, Georgy Sofronov
This work explores use of novel advances in best subset selection for regression modelling via continuous optimization for offline change point detection and estimation in univaria…
Group COMBSS: Group Selection via Continuous Optimization
Anant Mathur, Sarat Moka, Benoit Liquet +1
We present a new optimization method for the group selection problem in linear regression. In this problem, predictors are assumed to have a natural group structure and the goal is…
Best Subset Solution Path for Linear Dimension Reduction Models using Continuous Optimization
Benoit Liquet, Sarat Moka, Samuel Muller
The selection of best variables is a challenging problem in supervised and unsupervised learning, especially in high dimensional contexts where the number of variables is usually m…
Spatial Autoregressive Model on a Dirichlet Distribution
Teo Nguyen, Sarat Moka, Kerrie Mengersen +1
Compositional data find broad application across diverse fields due to their efficacy in representing proportions or percentages of various components within a whole. Spatial depen…
Generalized Linear Models via the Lasso: To Scale or Not to Scale?
Anant Mathur, Sarat Moka, Zdravko Botev
The Lasso regression is a popular regularization method for feature selection in statistics. Prior to computing the Lasso estimator in both linear and generalized linear models, it…
Column Subset Selection and Nyström Approximation via Continuous Optimization
Anant Mathur, Sarat Moka, Zdravko Botev
We propose a continuous optimization algorithm for the Column Subset Selection Problem (CSSP) and Nyström approximation. The CSSP and Nyström method construct low-rank approximatio…
COMBSS: Best Subset Selection via Continuous Optimization
Sarat Moka, Benoit Liquet, Houying Zhu +1
The problem of best subset selection in linear regression is considered with the aim to find a fixed size subset of features that best fits the response. This is particularly chall…
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…
Rare Events in Random Geometric Graphs
Christian Hirsch, Sarat B. Moka, Thomas Taimre +1
This work introduces and compares approaches for estimating rare-event probabilities related to the number of edges in the random geometric graph on a Poisson point process. In the…
Unbiased Estimation of the Reciprocal Mean for Non-negative Random Variables
Sarat Moka, Dirk P. Kroese, Sandeep Juneja
Many simulation problems require the estimation of a ratio of two expectations. In recent years Monte Carlo estimators have been proposed that can estimate such ratios without bias…
Perfect Sampling for Gibbs Point Processes Using Partial Rejection Sampling
Sarat B. Moka, Dirk P. Kroese
We present a perfect sampling algorithm for Gibbs point processes, based on the partial rejection sampling of Guo et al. (2017). Our particular focus is on pairwise interaction pro…
Rejection and Importance Sampling based Perfect Simulation for Gibbs hard-sphere models
S. B. Moka, S. Juneja, M. R. H. Mandjes
Coupling from the past (CFTP) methods have been used to generate perfect samples from finite Gibbs hard-sphere models, an important class of spatial point processes, which is a set…
Diffusion Parameters of Flows in Stable Multi-class Queueing Networks
Sarat Babu Moka, Yoni Nazarathy, Werner Scheinhardt
We consider open multi-class queueing networks with general arrival processes, general processing time sequences and Bernoulli routing. The network is assumed to be operating under…
Regenerative Simulation for Queueing Networks with Exponential or Heavier Tail Arrival Distributions
Sarat Babu Moka, Sandeep Juneja
Multiclass open queueing networks find wide applications in communication, computer and fabrication networks. Often one is interested in steady-state performance measures associate…