Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate

no code implementations • 15 Dec 2024 • Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, Henry Lam

Despite being an essential tool across engineering and finance, Monte Carlo simulation can be computationally intensive, especially in large-scale, path-dependent problems that hinder straightforward parallelization.

Efficient Neural Network Prediction

Learning without gradient descent encoded by the dynamics of a neurobiological model

no code implementations • 16 Mar 2021 • Vivek Kurien George, Vikash Morar, Weiwei Yang, Jonathan Larson, Bryan Tower, Shweti Mahajan, Arkin Gupta, Christopher White, Gabriel A. Silva

The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function.

BIG-bench Machine Learning