Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 15 of 15 for “"Learning to Optimize"”.
-
Modifications of Q-learning to Optimize Dynamic Treatment Regimes
… has become a popular choice for investigators as it facilitates the construction and analysis of dynamic treatment regimes (DTRs). There exists a comprehensive body of literature on various statistical methods to analyze data collected from such trials and estimate the optimal DTR for an …
-
Bridging Machine Learning and Operations Research: From Decision-Focused Learning to Learning to Optimize
Optimization is central to modern decision-making, with critical applications in energy systems, supply chains, transportation, and healthcare. In these domains, operators must repeatedly solve large-scale optimization problems under tight time constraints and pervasive uncertainty. For example, …
-
Data-driven dynamic optimization with auxiliary covariates
… problems the operations research community seeks to solve. In this thesis, we develop and analyze algorithms that incorporate ideas from machine learning to optimize uncertain objectives directly from data. In the first chapter, we consider problems in which the decision affects the observed …
-
Creation of a Cognitive Radar with Machine Learning: Simulation and Implementation
… Decision Process (MDP), and then apply Deep-Q Learning to optimize radar performance. The radar environment includes a single point target and a communications system that will potentially interfere with the radar. We demonstrate that the Deep-Q Network (DQN) we construct is able to …
-
Adaptive Reinforcement Learning-Based Fuzzer for 5G RRC Security Evaluation
… framework leveraging the UER- ANSIM simulator to assess the security and resilience of 5G networks. The proposed approach specifically targets potential vulnerabilities in message exchange sequences, authentication procedures, and resource allocation mechanisms. It employs Q-learning to …
-
Modular engineering of neuromuscular gait simulators
In this thesis we present a novel approach to the computer simulation of forward dynamic gait models and the optimization of parameters that must be tuned for such models. This methodology is not limited to gait simulation, and could be useful for any situation in which a complex Simulink model …
-
Metodologia e plataforma baseadas em aprendizado de máquina para inspeção de defeitos na indústria têxtil
… yet challenging process, particularly due to the diversity of defects and reliance on subjective criteria from human inspectors. This work proposes a methodology based on computer vision and deep learning to optimize the QI process in textiles, alongside the development of an associated …
-
Machine Learning and Quantum Computing for Optimization Problems in Power Systems
While optimization problems are ubiquitous in all domains of engineering, they are of critical importance to power systems engineers. A safe and economical operation of the power systems entails solving many optimization problems such as security-constrained unit commitment, economic dispatch, …
-
Exploring the Impact of Digital and Social Media on Recruiting Georgia High School Football Players by NCAA Division 1 Universities in Georgia
… college football recruiting, offering innovative tools and strategies to enhance recruitment. Modern technology, including platforms such as X and Hudl, facilitates direct communication between high school athletes and college coaches. At the same time, companies like Route and RosterSpot employ …
-
Unlocking the potential of neural networks in resource and data constrained environments
… ushered in a new era in the field of machine learning computer vision. Conventional algorithmic approaches are being replaced by end-to-end deep learning systems that can leverage big data. Deep learning has begun revolutionizing human centric fields such as health-care and finance, finding …
-
Physics-Aware Optimization and Data-Driven Methods for Low-Carbon Power Systems
The US electricity sector is undergoing a transformation with aggressive targets to achieve 100% carbon pollution-free electricity by 2035. To achieve this objective while maintaining a safe and reliable power grid in the presence of intermittent renewable generation, new operating paradigms of …
-
Smart Process Design with Machine Learning for Quality Assurance in Metal Additive Manufacturing
… parameter optimization, (2) reinforcement learning-enabled scan path planning, and (3) a multi-fidelity Bayesian optimization framework for efficient process parameter tuning. Collectively, these approaches enhance process control and reduce defects, advancing LPBF toward greater …
-
Dynamic Discrete Choice Estimation using Reinforcement Learning with Applications in Online Food Markets
… Discrete Choice (DDC) models are widely used to analyze sequential decision-making in economics and marketing. However, their estimation remains computationally challenging, especially as state spaces expand, limiting their application to large-scale consumer datasets. This thesis develops …
-
Congestion Control for V2V Communication in VANET
… are vital for ensuring traffic safety in autonomous driving systems and intelligent transportation networks, where timely exchange of safety information is crucial. However, VANETs face significant challenges related to congestion control due to the high mobility of vehicles, dynamic changes …
-
Investigating the influence of LH-projecting BLA neurons upon motivated behavioral responding and appetitive learning
To optimize survival, organisms must be able to learn contingencies between external stimuli and rewards and appropriately respond to these associations. Deficits in reward-related learning or reward-seeking are thought to occur in a host of psychopathologies, including depression (Drevets, 2001), …