Results 41 to 50 of about 16,381 (262)
Misspecification in Inverse Reinforcement Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 The aim of Inverse Reinforcement Learning (IRL) is to infer a reward function R from a policy pi. To do this, we need a model of how pi relates to R. In the current literature, the most common models are optimality, Boltzmann rationality, and causal entropy maximisation. One of the primary motivations behind IRL is to infer human preferences from human
Joar Skalse, Alessandro Abate
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Multicollinearity and Model Misspecification
Sociological Science, 2016 Multicollinearity in linear regression is typically thought of as a problem of large standard errors due to near-linear dependencies among independent variables. This problem can be solved by more informative data, possibly in the form of a larger sample.
Christopher Winship, Bruce Western
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Informational herding with model misspecification [PDF]
Journal of Economic Theory, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Confronting model misspecification in macroeconomics [PDF]
Journal of Econometrics, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel F. Waggoner, Tao Zha
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Advanced Science, EarlyView.An empirical‐aided active learning framework is developed to optimize high‐throughput laser‐induced photothermal annealing of silicon suboxide anodes. By integrating probabilistic machine learning with empirical domain knowledge, this approach achieves optimal electrochemical performance using limited experiments.
Chaeyoung Park +3 more
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Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions
Frontiers in Applied Mathematics and Statistics, 2017 Piecewise growth curve model (PGCM) is often used when the underlying growth process is not linear and is hypothesized to consist of phasic developments connected by turning points (or knots or change points).
Ling Ning, Wen Luo
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On the Model-Misspecification in Reinforcement Learning
, 2023 The success of reinforcement learning (RL) crucially depends on effective function approximation when dealing with complex ground-truth models. Existing sample-efficient RL algorithms primarily employ three approaches to function approximation: policy-based, value-based, and model-based methods.
Yunfan Li, Lin Yang
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Applied Economic Perspectives and Policy, EarlyView.ABSTRACT The California dairy industry has experienced considerable consolidation over the past 30 years. During this period, there has also been substantial technological innovation, implementation of the state's Low Carbon Fuel Standard program, and rapid adoption of anaerobic digesters.
Michael McCullough, Jarrett Hart
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System Misspecification Testing and Structural Change in the Demand for Meats
Journal of Agricultural and Resource Economics, 1995 A misspecification testing strategy designed to ensure that the statistical assumptions underlying a system of equations are appropriate is outlined. The system tests take into account information in, and interactions between, all equations in the system
Anya M. McGuirk +3 more
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How Competitive Is Myanmar's Rice Sector? A Comparison of Production Costs and Efficiency
Agribusiness, EarlyView.ABSTRACT This paper analyzes the cost competitiveness of rice production in Myanmar by examining production costs, cost efficiency, and the potential effect of improving cost efficiency on the country's global competitiveness. To achieve this, we conduct a comparative analysis of production costs among major rice‐producing countries and estimate the ...
Nandar Aye Chan +3 more
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