7 citations · 7 across the 3 of their papers we have counts for
3 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…
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…