Browsing Computer Science and Automation (CSA) by Advisor "Bhatnagar, Shalabh"
Now showing items 21-29 of 29
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Scalable Sprase Bayesian Nonparametric and Matrix Tri-factorization Models for Text Mining Applications
(2018-05-23)Hierarchical Bayesian Models and Matrix factorization methods provide an unsupervised way to learn latent components of data from the grouped or sequence data. For example, in document data, latent component corn-responds ... -
Simulation Based Algorithms For Markov Decision Process And Stochastic Optimization
(2010-08-06)In Chapter 2, we propose several two-timescale simulation-based actor-critic algorithms for solution of infinite horizon Markov Decision Processes (MDPs) with finite state-space under the average cost criterion. On the ... -
Simulation based methods for optimization
In many engineering problems, one is often interested in optimizing a parameterized performance objective. If the objective function is analytically known and its derivatives easily computable, then a number of methods ... -
Single and Multi-Agent Finite Horizon Reinforcement Learning Algorithms for Smart Grids
In this thesis, we study sequential decision-making under uncertainty in the context of smart grids using reinforcement learning. The underlying mathematical model for reinforcement learning algorithms are Markov Decision ... -
Stochastic Approximation Algorithms with Set-valued Dynamics : Theory and Applications
(2018-07-05)Stochastic approximation algorithms encompass a class of iterative schemes that converge to a sought value through a series of successive approximations. Such algorithms converge even when the observations are erroneous. ... -
Stochastic Approximation with Markov Noise: Analysis and applications in reinforcement learning
Stochastic approximation algorithms are sequential non-parametric methods for finding a zero or minimum of a function in the situation where only the noisy observations of the function values are available. Two time-scale ... -
Stochastic approximation with set-valued maps and Markov noise: Theoretical foundations and applications
Stochastic approximation algorithms produce estimates of a desired solution using noisy real world data. Introduced by Robbins and Monro, in 1951, stochastic approximation techniques have been instrumental in the asymptotic ... -
Stochastic Newton Methods With Enhanced Hessian Estimation
(2018-05-22)Optimization problems involving uncertainties are common in a variety of engineering disciplines such as transportation systems, manufacturing, communication networks, healthcare and finance. The large number of input ... -
Stochastic Optimization And Its Application In Reinforcement Learning
Numerous engineering fields, such as transportation systems, manufacturing, communication networks, healthcare, and finance, frequently encounter problems requiring optimization in the presence of uncertainty. Simulation-based ...