18 citations · 40 across the 14 of their papers we have counts for
5 papers · 1 filter
Amortized Bayesian Local Interpolation NetworK: Fast covariance parameter estimation for Gaussian Processes
Brandon R. Feng, Reetam Majumder, Brian J. Reich +1
Gaussian processes (GPs) are a ubiquitous tool for geostatistical modeling with high levels of flexibility and interpretability, and the ability to make predictions at unseen spati…
Density correction for multivariate spatial fields of global climate model output using deep learning
Reetam Majumder, Shiqi Fang, A. Sankarasubramanian +2
Global Climate Models (GCMs) are numerical models that simulate complex physical processes within the Earth's climate system and are essential for understanding and predicting clim…
Optimal Stock Portfolio Selection with a Multivariate Hidden Markov Model
Reetam Majumder, Qing Ji, Nagaraj K. Neerchal
The underlying market trends that drive stock price fluctuations are often referred to in terms of bull and bear markets. Optimal stock portfolio selection methods need to take int…
Stochastic Gradient MCMC for Massive Geostatistical Data
Mohamed A. Abba, Brian J. Reich, Reetam Majumder +1
Gaussian processes (GPs) are commonly used for prediction and inference for spatial data analyses. However, since estimation and prediction tasks have cubic time and quadratic memo…
Deep Learning of Multivariate Extremes via a Geometric Representation
Callum J. R. Murphy-Barltrop, Reetam Majumder, Jordan Richards
The study of geometric extremes, where extremal dependence properties are inferred from the deterministic limiting shapes of scaled sample clouds, provides an exciting approach to…