Search Results for author: Sidhant Misra

Found 13 papers, 7 papers with code

DNN-based Policies for Stochastic AC OPF

no code implementations4 Dec 2021 Sarthak Gupta, Sidhant Misra, Deepjyoti Deka, Vassilis Kekatos

Stochastic optimal power flow (SOPF) formulations provide a mechanism to handle these uncertainties by computing dispatch decisions and control policies that maintain feasibility under uncertainty.

Exponential Reduction in Sample Complexity with Learning of Ising Model Dynamics

1 code implementation2 Apr 2021 Arkopal Dutt, Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra

We observe that for samples coming from a dynamical process far from equilibrium, the sample complexity reduces exponentially compared to a dynamical process that mixes quickly.

Learning Continuous Exponential Families Beyond Gaussian

1 code implementation18 Feb 2021 Christopher X. Ren, Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov

We address the problem of learning of continuous exponential family distributions with unbounded support.

Learning of Discrete Graphical Models with Neural Networks

1 code implementation NeurIPS 2020 Abhijith J., Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray

In addition, we also show a variant of NeurISE that can be used to learn a neural net representation for the full energy function of the true model.

Tractable learning in under-excited power grids

no code implementations4 May 2020 Deepjyoti Deka, Harish Doddi, Sidhant Misra, Murti Salapaka

This paper discusses statistical structure estimation in power grids in the "under-excited" regime, where a subset of internal nodes do not have external injection.

Efficient Learning of Discrete Graphical Models

1 code implementation NeurIPS 2020 Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov

We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models.

Statistical Learning For DC Optimal Power Flow

1 code implementation23 Jan 2018 Yeesian Ng, Sidhant Misra, Line A. Roald, Scott Backhaus

Although the number of possible bases is exponential in the size of the system, we show that only a few of them are relevant to system operation.

Optimization and Control

Information Theoretic Optimal Learning of Gaussian Graphical Models

no code implementations15 Mar 2017 Sidhant Misra, Marc Vuffray, Andrey Y. Lokhov

What is the optimal number of independent observations from which a sparse Gaussian Graphical Model can be correctly recovered?

Graph Reconstruction

A Modified Benders Decomposition for Chance-Constrained Unit Commitment with N-1 Security and Wind Uncertainty

1 code implementation15 Mar 2017 Kaarthik Sundar, Harsha Nagarajan, Line Roald, Sidhant Misra, Russell Bent, Daniel Bienstock

As renewable wind energy penetration rates continue to increase, one of the major challenges facing grid operators is the question of how to control transmission grids in a reliable and a cost-efficient manner.

Systems and Control

Optimal structure and parameter learning of Ising models

1 code implementation15 Dec 2016 Andrey Y. Lokhov, Marc Vuffray, Sidhant Misra, Michael Chertkov

Reconstruction of structure and parameters of an Ising model from binary samples is a problem of practical importance in a variety of disciplines, ranging from statistical physics and computational biology to image processing and machine learning.

Graphical Models for Optimal Power Flow

no code implementations21 Jun 2016 Krishnamurthy Dvijotham, Pascal Van Hentenryck, Michael Chertkov, Sidhant Misra, Marc Vuffray

In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors.

Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models

no code implementations NeurIPS 2016 Marc Vuffray, Sidhant Misra, Andrey Y. Lokhov, Michael Chertkov

We prove that with appropriate regularization, the estimator recovers the underlying graph using a number of samples that is logarithmic in the system size p and exponential in the maximum coupling-intensity and maximum node-degree.

A Note on Alternating Minimization Algorithm for the Matrix Completion Problem

no code implementations5 Feb 2016 David Gamarnik, Sidhant Misra

We consider the problem of reconstructing a low rank matrix from a subset of its entries and analyze two variants of the so-called Alternating Minimization algorithm, which has been proposed in the past.

Matrix Completion

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