136 citations · 360 across the 45 of their papers we have counts for
8 papers · 1 filter
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…
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…
Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics
Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray +1
The usual setting for learning the structure and parameters of a graphical model assumes the availability of independent samples produced from the corresponding multivariate probab…
Learning Continuous Exponential Families Beyond Gaussian
Christopher X. Ren, Sidhant Misra, Marc Vuffray +1
We address the problem of learning of continuous exponential family distributions with unbounded support. While a lot of progress has been made on learning of Gaussian graphical mo…
Learning of Discrete Graphical Models with Neural Networks
Abhijith J., Andrey Y. Lokhov, Sidhant Misra +1
Graphical models are widely used in science to represent joint probability distributions with an underlying conditional dependence structure. The inverse problem of learning a disc…
Efficient Learning of Discrete Graphical Models
Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov
Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least…