4 papers
Distribution-Free Robust Predict-Then-Optimize in Function Spaces
Yash Patel, Ambuj Tewari
The need to rapidly solve PDEs in engineering design workflows has spurred the rise of neural surrogate models. In particular, neural operator models provide a discretization-invar…
Operator Learning for Schrödinger Equation: Unitarity, Error Bounds, and Time Generalization
Yash Patel, Unique Subedi, Ambuj Tewari
We consider the problem of learning the evolution operator for the time-dependent Schrödinger equation, where the Hamiltonian may vary with time. Existing neural network-based surr…
Continuum Transformers Perform In-Context Learning by Operator Gradient Descent
Abhiti Mishra, Yash Patel, Ambuj Tewari
Transformers robustly exhibit the ability to perform in-context learning, whereby their predictive accuracy on a task can increase not by parameter updates but merely with the plac…
TRENDY: Gene Regulatory Network Inference Enhanced by Transformer
Xueying Tian, Yash Patel, Yue Wang
Gene regulatory networks (GRNs) play a crucial role in the control of cellular functions. Numerous methods have been developed to infer GRNs from gene expression data, including me…