327 citations · 1.2k across the 57 of their papers we have counts for
63 papers
Operator Score Matching for Learning Quantum Hamiltonians
Shreya Shukla, Abhijith Jayakumar, Andrey Y. Lokhov
Learning quantum Hamiltonians from low-temperature thermal state measurements is a fundamental problem in quantum physics. Scalability of existing methods is limited by the complex…
Finite Sample Bounds for Learning with Score Matching
Devin Smedira, Abhijith Jayakumar, Sidhant Misra +2
Learning of continuous exponential family distributions with unbounded support remains an important area of research for both theory and applications in high-dimensional statistics…
Selecting Optimal Variable Order in Autoregressive Ising Models
Shiba Biswal, Marc Vuffray, Andrey Y. Lokhov
Autoregressive models enable tractable sampling from learned probability distributions, but their performance critically depends on the variable ordering used in the factorization…
Computationally sufficient statistics for Ising models
Abhijith Jayakumar, Shreya Shukla, Marc Vuffray +2
Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms fo…
Discrete Diffusion with Sample-Efficient Estimators for Conditionals
Karthik Elamvazhuthi, Abhijith Jayakumar, Andrey Y. Lokhov
We study a discrete denoising diffusion framework that integrates a sample-efficient estimator of single-site conditionals with round-robin noising and denoising dynamics for gener…
Efficient Learning of Lattice Gauge Theories with Fermions
Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov +2
We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the S…