2 citations · 3 across the 9 of their papers we have counts for
9 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…
A Greedy PDE Router for Blending Neural Operators and Classical Methods
Sahana Rayan, Yash Patel, Ambuj Tewari
When solving PDEs, classical numerical solvers are often computationally expensive, while machine learning methods can suffer from spectral bias, failing to capture high-frequency…
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…
Conformal Robust Control of Linear Systems
Yash Patel, Sahana Rayan, Ambuj Tewari
End-to-end engineering design pipelines, in which designs are evaluated using concurrently defined optimal controllers, are becoming increasingly common in practice. To discover de…
Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
Eduardo Ochoa Rivera, Yash Patel, Ambuj Tewari
Distribution-free uncertainty estimation for ensemble methods is increasingly desirable due to the widening deployment of multi-modal black-box predictive models. Conformal predict…
Conformal Contextual Robust Optimization
Yash Patel, Sahana Rayan, Ambuj Tewari
Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate the risk of uncertainty region misspecification in safety-critical settings. Current appro…
Variational Inference with Coverage Guarantees in Simulation-Based Inference
Yash Patel, Declan McNamara, Jackson Loper +2
Amortized variational inference is an often employed framework in simulation-based inference that produces a posterior approximation that can be rapidly computed given any new obse…