Essays about: "Multi-armed bandits"
Showing result 1 - 5 of 8 essays containing the words Multi-armed bandits.
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1. An Empirical Survey of Bandits in an Industrial Recommender System Setting
University essay from Göteborgs universitet/Institutionen för data- och informationsteknikAbstract : In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content suggestion. The idea is to employ image features, extracted by a pre-trained convolutional neural network, and study the resulting bandit behaviors when including respective excluding this information in the typical context creation, which normally relies on structured data sources—such as metadata. READ MORE
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2. Causal Reinforcement Learning for Bandits with Unobserved Confounders
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Reinforcement Learning (RL) has been recognized as a valuable tool in various fields. However, its application is limited by its reliance on extensive data through a trial-and-error approach and challenges in generalizing learned policies. READ MORE
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3. Graph Bandits : Multi-Armed Bandits with Locality Constraints
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Multi-armed bandits (MABs) have been studied extensively in the literature and have applications in a wealth of domains, including recommendation systems, dynamic pricing, and investment management. On the one hand, the current MAB literature largely seems to focus on the setting where each arm is available to play at each time step, and ignores how agents move between the arms. READ MORE
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4. A Recommender System for Suggested Sites using Multi-Armed Bandits : Initialising Bandit Contexts by Neural Collaborative Filtering
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : The abundance of information available on the internet necessitates means of quickly finding what is relevant for the individual user. To this end, there has been much research concerning recommender systems and lately specifically methods using deep learning for such systems. READ MORE
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5. Reference Tracking with Adversarial Adaptive Output- Feedback Model Predictive Control
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Model Predictive Control (MPC) is a control strategy based on optimization that handles system constraints explicitly, making it a popular feedback control method in real industrial processes. However, designing this control policy is an expensive operation since an explicit model of the process is required when re-tuning the controller. READ MORE
