49 citations · 122 across the 14 of their papers we have counts for
14 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…
Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition
F. O. de Franca, M. Virgolin, M. Kommenda +21
Symbolic regression searches for analytic expressions that accurately describe studied phenomena. The main attraction of this approach is that it returns an interpretable model tha…
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Ketan Mittal, Tarik Dzanic +5
Adaptive mesh refinement (AMR) is necessary for efficient finite element simulations of complex physical phenomenon, as it allocates limited computational budget based on the need…
Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
T. Nathan Mundhenk, Mikel Landajuela, Ruben Glatt +3
Symbolic regression is the process of identifying mathematical expressions that fit observed output from a black-box process. It is a discrete optimization problem generally believ…
Incorporating domain knowledge into neural-guided search
Brenden K. Petersen, Claudio P. Santiago, Mikel Landajuela
Many AutoML problems involve optimizing discrete objects under a black-box reward. Neural-guided search provides a flexible means of searching these combinatorial spaces using an a…
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…
Distilling Wikipedia mathematical knowledge into neural network models
Joanne T. Kim, Mikel Landajuela, Brenden K. Petersen
Machine learning applications to symbolic mathematics are becoming increasingly popular, yet there lacks a centralized source of real-world symbolic expressions to be used as train…
Reinforcement Learning for Adaptive Mesh Refinement
Jiachen Yang, Tarik Dzanic, Brenden Petersen +9
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate c…
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…
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
Brenden K. Petersen, Mikel Landajuela, T. Nathan Mundhenk +3
Discovering the underlying mathematical expressions describing a dataset is a core challenge for artificial intelligence. This is the problem of . Des…
Single Episode Policy Transfer in Reinforcement Learning
Jiachen Yang, Brenden Petersen, Hongyuan Zha +1
Transfer and adaptation to new unknown environmental dynamics is a key challenge for reinforcement learning (RL). An even greater challenge is performing near-optimally in a single…
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl +4
Sepsis is a life-threatening condition affecting one million people per year in the US in which dysregulation of the body's own immune system causes damage to its tissues, resultin…
Modeling sepsis progression using hidden Markov models
Brenden K. Petersen, Michael B. Mayhew, Kalvin O. E. Ogbuefi +3
Characterizing a patient's progression through stages of sepsis is critical for enabling risk stratification and adaptive, personalized treatment. However, commonly used sepsis dia…