7 citations · 9 across the 4 of their papers we have counts for
4 papers
Deep Symbolic Optimization: Reinforcement Learning for Symbolic Mathematics
Conor F. Hayes, Felipe Leno Da Silva, Jiachen Yang +15
Deep Symbolic Optimization (DSO) is a novel computational framework that enables symbolic optimization for scientific discovery, particularly in applications involving the search f…
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
Jacob F. Pettit, Chak Shing Lee, Jiachen Yang +4
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 tr…
Improving exploration in policy gradient search: Application to symbolic optimization
Mikel Landajuela, Brenden K. Petersen, Soo K. Kim +5
Many machine learning strategies designed to automate mathematical tasks leverage neural networks to search large combinatorial spaces of mathematical symbols. In contrast to tradi…
Increasing performance of electric vehicles in ride-hailing services using deep reinforcement learning
Jacob F. Pettit, Ruben Glatt, Jonathan R. Donadee +1
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 pape…