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Hedonic Prices and Quality Adjusted Price Indices Powered by AI
To accomplish this, we generate abstract product attributes, or ``features,'' from text descriptions and images using deep neural networks, and then use these attributes to estimate the hedonic price function.
Periodic-GP: Learning Periodic World with Gaussian Process Bandits
We consider the sequential decision optimization on the periodic environment, that occurs in a wide variety of real-world applications when the data involves seasonality, such as the daily demand of drivers in ride-sharing and dynamic traffic patterns in transportation.
Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making
The Markov assumption (MA) is fundamental to the empirical validity of reinforcement learning.
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