Publications

Approximate Equivariance in Reinforcement Learning
Equivariant neural networks have shown great success in reinforcement learning, improving sample efficiency and generalization when …
Jung Yeon Park, Sujay Bhatt, Sihan Zeng, Lawson L.S. Wong, Alec Koppel, Sumitra Ganesh, Robin Walters
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Approximate Equivariance in Reinforcement Learning
Equivariant Action Sampling for Reinforcement Learning and Planning
Reinforcement learning (RL) algorithms for continuous control tasks require accurate sampling-based action selection. Many tasks, such …
Linfeng Zhao, Owen Lewis Howell, Xupeng Zhu, Jung Yeon Park, Zhewen Zhang, Robin Walters, Lawson L.S. Wong
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Equivariant Action Sampling for Reinforcement Learning and Planning
Symmetric Models for Radar Response Modeling
Many radar applications require complex radar signature models that incorporate characteristics of an object’s shape and dynamics …
Colin Kohler, Nathan Vaska, Ramya Muthukrishnan, Whangbong Choi, Jung Yeon Park, Justin Goodwin, Rajmonda Caceres, Robin Walters
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Symmetric Models for Radar Response Modeling
Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
Data over non-Euclidean manifolds, often discretized as surface meshes, naturally arise in computer graphics and biological and …
Jung Yeon Park, Lawson L.S. Wong, Robin Walters
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Modeling Dynamics over Meshes with Gauge Equivariant Nonlinear Message Passing
A General Theory of Correct, Incorrect, and Extrinsic Equivariance
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by …
Dian Wang, Xupeng Zhu, Jung Yeon Park, Robert Platt, Robin Walters
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The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by …
Dian Wang, Jung Yeon Park, Neel Sortur, Lawson L.S. Wong, Robin Walters, Robert Platt
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The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Robust Imitation Learning of a Few Demonstrations with a Backwards Model
Behavior cloning of expert demonstrations can speed up learning optimal policies in a more sample-efficient way over reinforcement …
Jung Yeon Park, Lawson L.S. Wong
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Robust Imitation Learning of a Few Demonstrations with a Backwards Model
Learning Symmetric Embedding Networks for Equivariant World Models
Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples …
Jung Yeon Park, Ondrej Biza, Linfeng Zhao, Jan-Willem van de Meent, Robin Walters
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Learning Symmetric Embedding Networks for Equivariant World Models
Generator Surgery for Compressed Sensing
Recent work has explored the use of generator networks with low latent dimension as signal priors for image recovery in compressed …
Jung Yeon Park, Niklas Smedemark-Margulies, Max Daniels, Rose Yu, Jan-Willem van de Meent, Paul Hand
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Generator Surgery for Compressed Sensing
Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
Efficient and interpretable spatial analysis is crucial in many fields such as geology, sports, and climate science. Tensor latent …
Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
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Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis
On the Consequences of Un-Modeled Dynamics to the Optimality of Schedules in Clustered Photolithography Tools
Clustered photography tools (CPTs) are very complex and can substantially influence the throughput of wafer fabrication facilities. …
Hyeong-Ook Kim, Se-Hyeon Park, Jung Yeon Park, James R. Morrison
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On the Consequences of Un-Modeled Dynamics to the Optimality of Schedules in Clustered Photolithography Tools
Exit Recursion Models of Clustered Photolithography Tools for Fab Level Simulation
James R. Morrison, Jung Yeon Park, Kyungsu Park, Sang Yoon Bae
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Models of Photolithography Tools for Fab- Level Simulation: From Affine to Flow Line
James R. Morrison, Jung Yeon Park, Kyungsu Park, Sang Yoon Bae
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Models of Clustered Photolithography Tools for Fab-Level Simulation: From Affine to Flow Line
Fab-level discrete-event simulation is an important practical tool for the analysis and optimization of semiconductor wafer …
Jung Yeon Park, Kyungsu Park, James R. Morrison
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Models of Clustered Photolithography Tools for Fab-Level Simulation: From Affine to Flow Line
Exit Recursion Models of Clustered Photolithography Tools for Fab Level Simulation
In semiconductor wafer fabricators (fabs), clustered photolithography tools (CPTs) are often the bottleneck. With a focus on fab-level …
Jung Yeon Park, Kyungsu Park, James R. Morrison
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Exit Recursion Models of Clustered Photolithography Tools for Fab Level Simulation