We propose a new reinforcement learning algorithm for partially observable Markov
decision processes (POMDP) based on spectral decomposition methods. While spectral …

State aggregation is a popular model reduction method rooted in optimal control. It reduces
the complexity of engineering systems by map** the system's states into a small number …

In many natural settings, the analysis goal is not to characterize a single data set in isolation,
but rather to understand the difference between one set of observations and another. For …

The Hidden Markov Model (HMM) is one of the mainstays of statistical modeling of discrete
time series, with applications including speech recognition, computational biology, computer …

In this article we use the theory of quantum channels and open quantum systems to provide
an efficient unitary characterization of a class of stochastic generators known as quantum …

Due to recent empirical successes, the options framework for hierarchical reinforcement
learning is gaining increasing popularity. Rather than learning from rewards, we consider …

We observe that the technique of Markov contraction can be used to establish measure
concentration for a broad class of noncontracting chains. In particular, geometric ergodicity …

We develop a scalable, computationally efficient method for the task of energy
disaggregation for home appliance monitoring. In this problem the goal is to estimate the …

Spectral learning methods have recently been proposed as alternatives to slow, non-convex
optimization algorithms like EM for a variety of probabilistic models in which hidden …

This paper is concerned with the computational and statistical complexity of learning the
Hidden Markov model (HMM). Although HMMs are some of the most widely used tools in …