95 citations · 291 across the 29 of their papers we have counts for
31 papers
Differential Analysis for Networks Obeying Conservation Laws
Anirudh Rayas, Rajasekhar Anguluri, Jiajun Cheng +1
Networked systems that occur in various domains, such as the power grid, the brain, and opinion networks, are known to obey conservation laws. For instance, electric networks obey…
Active Sequential Two-Sample Testing
Weizhi Li, Prad Kadambi, Pouria Saidi +3
A two-sample hypothesis test is a statistical procedure used to determine whether the distributions generating two samples are identical. We consider the two-sample testing problem…
Robust Model Selection of Gaussian Graphical Models
Abrar Zahin, Rajasekhar Anguluri, Lalitha Sankar +2
In Gaussian graphical model selection, noise-corrupted samples present significant challenges. It is known that even minimal amounts of noise can obscure the underlying structure,…
Transmission Line Parameter Estimation Under Non-Gaussian Measurement Noise
Antos Cheeramban Varghese, Anamitra Pal, Gautam Dasarathy
Accurate knowledge of transmission line parameters is essential for a variety of power system monitoring, protection, and control applications. The use of phasor measurement unit (…
Controllability of Coarsely Measured Networked Linear Dynamical Systems (Extended Version)
Nafiseh Ghoroghchian, Rajasekhar Anguluri, Gautam Dasarathy +1
We consider the controllability of large-scale linear networked dynamical systems when complete knowledge of network structure is unavailable and knowledge is limited to coarse sum…
Learning the Structure of Large Networked Systems Obeying Conservation Laws
Anirudh Rayas, Rajasekhar Anguluri, Gautam Dasarathy
Many networked systems such as electric networks, the brain, and social networks of opinion dynamics are known to obey conservation laws. Examples of this phenomenon include the Ki…
A Machine Learning Framework for Event Identification via Modal Analysis of PMU Data
Nima T. Bazargani, Gautam Dasarathy, Lalitha Sankar +1
Power systems are prone to a variety of events (e.g. line trips and generation loss) and real-time identification of such events is crucial in terms of situational awareness, relia…
A label-efficient two-sample test
Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy +1
Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis). We consid…
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
Parth K. Thaker, Mohit Malu, Nikhil Rao +1
Pure exploration in multi-armed bandits has emerged as an important framework for modeling decision-making and search under uncertainty. In modern applications, however, one is oft…
State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
Behrouz Azimian, Reetam Sen Biswas, Shiva Moshtagh +3
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper addresses this challenge by formul…
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
Nafiseh Ghoroghchian, Gautam Dasarathy, Stark C. Draper
We study the problem of community recovery from coarse measurements of a graph. In contrast to the problem of community recovery of a fully observed graph, one often encounters sit…
Finding the Homology of Decision Boundaries with Active Learning
Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy +1
Accurately and efficiently characterizing the decision boundary of classifiers is important for problems related to model selection and meta-learning. Inspired by topological data…
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion Model
John Janiczek, Parth Thaker, Gautam Dasarathy +3
Hyperspectral unmixing is an important remote sensing task with applications including material identification and analysis. Characteristic spectral features make many pure materia…
On the alpha-loss Landscape in the Logistic Model
Tyler Sypherd, Mario Diaz, Lalitha Sankar +1
We analyze the optimization landscape of a recently introduced tunable class of loss functions called -loss, , in the logistic model. This family encapsulates t…
On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems
Parth Thaker, Gautam Dasarathy, Angelia Nedić
We consider the problem of recovering a complex vector from quadratic measurements . This pro…
Regularization via Structural Label Smoothing
Weizhi Li, Gautam Dasarathy, Visar Berisha
Regularization is an effective way to promote the generalization performance of machine learning models. In this paper, we focus on label smoothing, a form of output distribution r…
Graph quilting: graphical model selection from partially observed covariances
Giuseppe Vinci, Gautam Dasarathy, Genevera I. Allen
Graphical model selection is a seemingly impossible task when many pairs of variables are never jointly observed; this requires inference of conditional dependencies with no observ…
A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization
Tyler Sypherd, Mario Diaz, John Kevin Cava +3
We introduce a tunable loss function called -loss, parameterized by , which interpolates between the exponential loss (), the log-loss (), and the…
IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election
Indu Manickam, Andrew S. Lan, Gautam Dasarathy +1
The 2016 United States presidential election has been characterized as a period of extreme divisiveness that was exacerbated on social media by the influence of fake news, trolls,…
Thresholding Graph Bandits with GrAPL
Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk
In this paper, we introduce a new online decision making paradigm that we call Thresholding Graph Bandits. The main goal is to efficiently identify a subset of arms in a multi-arme…
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches
Amirali Aghazadeh, Ryan Spring, Daniel LeJeune +3
Feature selection is an important challenge in machine learning. It plays a crucial role in the explainability of machine-driven decisions that are rapidly permeating throughout mo…
Coalescent-based species tree estimation: a stochastic Farris transform
Gautam Dasarathy, Elchanan Mossel, Robert Nowak +1
The reconstruction of a species phylogeny from genomic data faces two significant hurdles: 1) the trees describing the evolution of each individual gene--i.e., the gene trees--may…
DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks
Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk
In this paper we develop a novel computational sensing framework for sensing and recovering structured signals. When trained on a set of representative signals, our framework learn…
Multi-fidelity Bayesian Optimisation with Continuous Approximations
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider +1
Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design. Recently, \e…