The procedure of registering a property involves paying stamp duty, and registering the sales deed for the property you have purchased. Property registration is done at the office of the sub-registrar who has jurisdic...
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While humans intuitively excel at classifying words according to their connotation, transcribing this innate skill into algorithms remains challenging. We present a human-guided methodology to learn binary word sentim...
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Nowadays, the credit card industry is more concerned about credit card fraud than any other problem. The main idea is to examine optional techniques used for fraud detection and to identify the different types of cred...
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Because of the rapid development of communication and service in Taiwan, competition among telecommunication companies has become ever fiercer. Differences in marketing strategy usually become the key factor in keepin...
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In a country like India, where most of the income depends on agriculture, it is challenging to rely only on soil-based agriculture in the future. These days, soil-based agriculture faces several difficulties, includin...
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In this paper the challenge of human decision-making (HDM) modeling by gambling task is targeted, specifically focusing on the Iowa Gambling Task (IGT). Traditional models train on data generated during gameplay, wher...
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The present investigation focuses on using original and segmented retinal images to apply deep learning models, particularly DenseNet 121 and Inception V3, for the detection of diabetic retinopathy. According to the e...
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Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential...
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Inverse Reinforcement Learning (IRL) and Reinforcement Learning from Human Feedback (RLHF) are pivotal methodologies in reward learning, which involve inferring and shaping the underlying reward function of sequential decision-making problems based on observed human demonstrations and feedback. Most prior work in reward learning has relied on prior knowledge or assumptions about decision or preference models, potentially leading to robustness issues. In response, this paper introduces a novel linear programming (LP) framework tailored for offline reward learning. Utilizing pre-collected trajectories without online exploration, this framework estimates a feasible reward set from the primal-dual optimality conditions of a suitably designed LP, and offers an optimality guarantee with provable sample efficiency. Our LP framework also enables aligning the reward functions with human feedback, such as pairwise trajectory comparison data, while maintaining computational tractability and sample efficiency. We demonstrate that our framework potentially achieves better performance compared to the conventional maximum likelihood estimation (MLE) approach through analytical examples and numerical experiments. Copyright 2024 by the author(s)
The ability to predict cow calving easiness cost-effectively, especially in the dairy industry where cattle suffer from a variety of unpredictable deadly illnesses and high breeding expenses assist farmers in improvin...
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Recent years have witnessed a substantial increase in website phishing attacks. Many researchers have developed software solutions to detect phishing websites, however it cannot detect these attacks completely. There ...
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