A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

Regime-driven models are popular for addressing temporal patterns in both financial market
performance and underlying stylized factors, wherein a regime describes periods with …

Sharp global convergence guarantees for iterative nonconvex optimization with random data
Page 1 The Annals of Statistics 2023, Vol. 51, No. 1, 179–210 https://doi.org/10.1214/22-AOS2246 …

Since the advantage of hidden Markov model in dealing with time series data and for the
sake of identifying driving style, three driving style (aggressive, moderate and mild) are …

We study the model‐based undiscounted reinforcement learning for partially observable
Markov decision processes (POMDPs). The oracle we consider is the optimal policy of the …

Motivation The evolution of bacterial drug resistance and other features in biology, the
progression of cancer and other diseases and a wide range of broader questions can often …

We study a multi-armed bandit problem where the rewards exhibit regime switching.
Specifically, the distributions of the random rewards generated from all arms are modulated …

We propose an algorithm to impute and forecast a time series by transforming the observed
time series into a matrix, utilizing matrix estimation to recover missing values and de-noise …

We propose a hidden Markov model approach for processing seismocardiograms. The
seismocardiogram morphology is learned using the expectation-maximization algorithm …

We consider a general class of regression models with normally distributed covariates, and
the associated nonconvex problem of fitting these models from data. We develop a general …