2 citations · 3 across the 4 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…
On Generation in Metric Spaces
Jiaxun Li, Vinod Raman, Ambuj Tewari
We study generation in separable metric instance spaces. We extend the language generation framework from Kleinberg and Mullainathan [2024] beyond countable domains by defining nov…
A Characterization of List Language Identification in the Limit
Moses Charikar, Chirag Pabbaraju, Ambuj Tewari
We study the problem of language identification in the limit, where given a sequence of examples from a target language, the goal of the learner is to output a sequence of guesses…
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…
Learning to Partially Defer for Sequences
Sahana Rayan, Ambuj Tewari
In the Learning to Defer (L2D) framework, a prediction model can either make a prediction or defer it to an expert, as determined by a rejector. Current L2D methods train the rejec…