Abstract
In reinforcement learning and deep learning, softmax parameterization is commonly used to represent discrete probability distributions.In this work, we study three possible softmax parametrizations of the transition matrix of the Markov chain. Through theoretical and empirical lenses, we provide several insights into the effect of such parametrizations on estimating the Markov transition matrix.
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Junghyun Lee
PhD Student
PhD student at GSAI, KAIST, jointly advised by Profs. Se-Young Yun and Chulhee Yun. Research focuses on interactive machine learning, “theoretical perspectives” of LLMs, optimization theory, and statistical analyses of large networks with an emphasis on community detection. Broadly interested in mathematical and theoretical AI, as well asrelated problems in mathematics and statistics.