Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all trades between each counterparty under both market and credit risk. We present a ...
Abstract: Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs ...
Abstract: Policy gradient algorithm is often used to deal with the continuous control problems. But as a model-free algorithm, it suffers from the low data efficiency and long learning phase. In this ...
Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States Chemical Engineering Department, Texas A&M University at Qatar, Education City, ...
In this talk I will present a theoretical framework that links a set of widely used methods from signal processing to statistical inference procedures. This result will then be used as a conceptual ...
This project models my speedcubing solve times using a Gaussian HMM and a residual neural forecaster. The goal is to understand how my performance evolves across Flow, Baseline, and Tilt states, and ...
On September 26th and 27th, 2023, the following special lectures will be delivered as part of the international activities of the Multiscale Analysis, Modelling and Simulation Unit. Professor Ofer ...
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