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Optimal Learning via Moderate Deviations Theory
This paper proposes a statistically optimal approach for learning a function value using a confidence interval in a wide range of models, including general non-parametric estimation of an expected loss described as a stochastic programming problem or various SDE models.
Infinite-dimensional optimization and Bayesian nonparametric learning of stochastic differential equations
The paper has two major themes.
Context-Aware Design of Cyber-Physical Human Systems (CPHS)
The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models.
A variational approach to path estimation and parameter inference of hidden diffusion processes
We consider a hidden Markov model, where the signal process, given by a diffusion, is only indirectly observed through some noisy measurements.
