11 citations · 11 across the 3 of their papers we have counts for
4 papers
Isotropic Gaussian Processes Improve Vanilla Bayesian Optimization in High Dimensions
Wei-Ting Tang, Madhav Muthyala, Joel A. Paulson
High-dimensional Bayesian optimization (BO) often fits Gaussian process (GP) surrogates from far fewer observations than input dimensions. Modern Vanilla BO can perform well in thi…
Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design
Madhav R. Muthyala, Farshud Sorourifar, Tianhong Tan +2
Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high…
SyMANTIC: An Efficient Symbolic Regression Method for Interpretable and Parsimonious Model Discovery in Science and Beyond
Madhav R. Muthyala, Farshud Sorourifar, You Peng +1
Symbolic regression (SR) is an emerging branch of machine learning focused on discovering simple and interpretable mathematical expressions from data. Although a wide-variety of SR…
TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
Madhav Muthyala, Farshud Sorourifar, Joel A. Paulson
Symbolic regression (SR) is a powerful machine learning approach that searches for both the structure and parameters of algebraic models, offering interpretable and compact represe…