20 citations · 59 across the 35 of their papers we have counts for
10 papers · 1 filter
Vertical Symbolic Regression
Nan Jiang, Md Nasim, Yexiang Xue
Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact. Learning symbolic expressions from experimental dat…
End-to-end Phase Field Model Discovery Combining Experimentation, Crowdsourcing, Simulation and Learning
Md Nasim, Anter El-Azab, Xinghang Zhang +1
The availability of tera-byte scale experiment data calls for AI driven approaches which automatically discover scientific models from data. Nonetheless, significant challenges pre…
Hypothesis Network Planned Exploration for Rapid Meta-Reinforcement Learning Adaptation
Maxwell Joseph Jacobson, Rohan Menon, John Zeng +1
Meta-Reinforcement Learning (Meta-RL) learns optimal policies across a series of related tasks. A central challenge in Meta-RL is rapidly identifying which previously learned task…
Integrating Symbolic Reasoning into Neural Generative Models for Design Generation
Maxwell Joseph Jacobson, Yexiang Xue
Design generation requires tight integration of neural and symbolic reasoning, as good design must meet explicit user needs and honor implicit rules for aesthetics, utility, and co…
Racing Control Variable Genetic Programming for Symbolic Regression
Nan Jiang, Yexiang Xue
Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing equations from experimental data. Popular approaches based on genetic programming, Mont…
Efficient Learning of PDEs via Taylor Expansion and Sparse Decomposition into Value and Fourier Domains
Md Nasim, Yexiang Xue
Accelerating the learning of Partial Differential Equations (PDEs) from experimental data will speed up the pace of scientific discovery. Previous randomized algorithms exploit spa…
Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration With Provable Guarantees
Jinzhao Li, Nan Jiang, Yexiang Xue
Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world pro…
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
This paper proposes an algorithm that aims to improve generalization for reinforcement learning agents by removing overfitting to confounding features. Our approach consists of a m…
Symbolic Regression via Control Variable Genetic Programming
Nan Jiang, Yexiang Xue
Learning symbolic expressions directly from experiment data is a vital step in AI-driven scientific discovery. Nevertheless, state-of-the-art approaches are limited to learning sim…
Adversarial Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
The policy represented by the deep neural network can overfit the spurious features in observations, which hamper a reinforcement learning agent from learning effective policy. Thi…