Jacob F. Pettit
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View article: Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics Open
Deep Symbolic Optimization (DSO) is a novel computational framework that enables symbolic optimization for scientific discovery, particularly in applications involving the search for intricate symbolic structures. One notable example is eq…
View article: DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces Open
We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (…
View article: DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces Open
We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (…
View article: Improving Exploration in Policy Gradient Search: Application to Symbolic Optimization
Improving Exploration in Policy Gradient Search: Application to Symbolic Optimization Open
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to traditional evolutionary approaches, using a neural network at t…
View article: Learning Sparse Symbolic Policies for Sepsis Treatment
Learning Sparse Symbolic Policies for Sepsis Treatment Open
View article: Improving exploration in policy gradient search: Application to symbolic optimization
Improving exploration in policy gradient search: Application to symbolic optimization Open
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to traditional evolutionary approaches, using a neural network at t…
View article: Discovering symbolic policies with deep reinforcement learning
Discovering symbolic policies with deep reinforcement learning Open
View article: Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning Open
New forms of on-demand transportation such as ride-hailing and connected autonomous vehicles are proliferating, yet are a challenging use case for electric vehicles (EV). This paper explores the feasibility of using deep reinforcement lear…