no code implementations • 30 Jan 2023 • Anirudh Rayas, Rajasekhar Anguluri, Jiajun Cheng, Gautam Dasarathy

Given the dynamic nature of the systems under consideration, an equally important task is estimating the change in the structure of the network from data -- the so called differential network analysis problem.

no code implementations • 30 Jan 2023 • Weizhi Li, Karthikeyan Natesan Ramamurthy, Prad Kadambi, Pouria Saidi, Gautam Dasarathy, Visar Berisha

The classification model is adaptively updated and then used to guide an active query scheme called bimodal query to label sample features in the regions with high dependency between the feature variables and the label variables.

no code implementations • 10 Nov 2022 • Abrar Zahin, Rajasekhar Anguluri, Oliver Kosut, Lalitha Sankar, Gautam Dasarathy

A recent line of work establishes that even for tree-structured graphical models, only partial structure recovery is possible and goes on to devise algorithms to identify the structure up to an (unavoidable) equivalence class of trees.

no code implementations • 28 Aug 2022 • Antos Cheeramban Varghese, Anamitra Pal, Gautam Dasarathy

The use of phasor measurement unit (PMU) data for transmission line parameter estimation (TLPE) is well-documented.

no code implementations • 21 Jun 2022 • Nafiseh Ghoroghchian, Rajasekhar Anguluri, Gautam Dasarathy, Stark C. Draper

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 summaries.

1 code implementation • 14 Jun 2022 • 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.

no code implementations • 14 Feb 2022 • Nima T. Bazargani, Gautam Dasarathy, Lalitha Sankar, Oliver Kosut

Using the obtained subset of features, we investigate the performance of two well-known classification models, namely, logistic regression (LR) and support vector machines (SVM) to identify generation loss and line trip events in two datasets.

1 code implementation • 17 Nov 2021 • Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

Two-sample tests evaluate whether two samples are realizations of the same distribution (the null hypothesis) or two different distributions (the alternative hypothesis).

no code implementations • 29 Sep 2021 • Nafiseh Ghoroghchian, Rajasekhar Anguluri, Gautam Dasarathy, Stark Draper

In contrast, in this paper the controllability aspects of the coarse system are derived from coarse summaries {\em without} knowledge of the fine-scale structure.

1 code implementation • 2 Aug 2021 • Parth K. Thaker, Mohit Malu, Nikhil Rao, Gautam Dasarathy

In this paper, we consider the pure exploration problem in stochastic multi-armed bandits where the similarities between the arms are captured by a graph and the rewards may be represented as a smooth signal on this graph.

1 code implementation • 15 Apr 2021 • Behrouz Azimian, Reetam Sen Biswas, Shiva Moshtagh, Anamitra Pal, Lang Tong, Gautam Dasarathy

Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability.

1 code implementation • 25 Feb 2021 • Nafiseh Ghoroghchian, Gautam Dasarathy, Stark C. Draper

Our objective is to develop conditions on the graph structure, the quantity, and properties of measurements, under which we can recover the community organization in this coarse graph.

1 code implementation • NeurIPS 2020 • Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

We theoretically analyze the proposed framework and show that the query complexity of our active learning algorithm depends naturally on the intrinsic complexity of the underlying manifold.

1 code implementation • ECCV 2020 • John Janiczek, Parth Thaker, Gautam Dasarathy, Christopher S. Edwards, Philip Christensen, Suren Jayasuriya

The dispersion model is introduced to simulate realistic spectral variation, and an efficient method to fit the parameters is presented.

no code implementations • 22 Jun 2020 • Tyler Sypherd, Mario Diaz, Lalitha Sankar, Gautam Dasarathy

We analyze the optimization landscape of a recently introduced tunable class of loss functions called $\alpha$-loss, $\alpha \in (0,\infty]$, in the logistic model.

no code implementations • 4 Feb 2020 • Parth Thaker, Gautam Dasarathy, Angelia Nedić

We consider the problem of recovering a complex vector $\mathbf{x}\in \mathbb{C}^n$ from $m$ quadratic measurements $\{\langle A_i\mathbf{x}, \mathbf{x}\rangle\}_{i=1}^m$.

no code implementations • 7 Jan 2020 • Weizhi Li, Gautam Dasarathy, Visar Berisha

Regularization is an effective way to promote the generalization performance of machine learning models.

1 code implementation • 5 Jun 2019 • Tyler Sypherd, Mario Diaz, John Kevin Cava, Gautam Dasarathy, Peter Kairouz, Lalitha Sankar

We introduce a tunable loss function called $\alpha$-loss, parameterized by $\alpha \in (0,\infty]$, which interpolates between the exponential loss ($\alpha = 1/2$), the log-loss ($\alpha = 1$), and the 0-1 loss ($\alpha = \infty$), for the machine learning setting of classification.

1 code implementation • 22 May 2019 • Daniel LeJeune, Gautam Dasarathy, Richard G. Baraniuk

The main goal is to efficiently identify a subset of arms in a multi-armed bandit problem whose means are above a specified threshold.

no code implementations • 21 May 2019 • Indu Manickam, Andrew S. Lan, Gautam Dasarathy, Richard G. Baraniuk

We apply this framework to the last two months of the election period for a group of 47508 Twitter users and demonstrate that both liberal and conservative users became more polarized over time.

no code implementations • ICLR 2019 • Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.

1 code implementation • ICML 2018 • Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, baraniuk

We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.

1 code implementation • 12 Jun 2018 • Amirali Aghazadeh, Ryan Spring, Daniel LeJeune, Gautam Dasarathy, Anshumali Shrivastava, Richard G. Baraniuk

We demonstrate that MISSION accurately and efficiently performs feature selection on real-world, large-scale datasets with billions of dimensions.

no code implementations • 13 Jul 2017 • Gautam Dasarathy, Elchanan Mossel, Robert Nowak, Sebastien Roch

As a corollary, we also obtain a new identifiability result of independent interest: for any species tree with $n \geq 3$ species, the rooted species tree can be identified from the distribution of its unrooted weighted gene trees even in the absence of a molecular clock.

no code implementations • 11 Jul 2017 • Ali Mousavi, Gautam Dasarathy, Richard G. Baraniuk

In this paper we develop a novel computational sensing framework for sensing and recovering structured signals.

no code implementations • ICML 2017 • Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabas Poczos

Bandit methods for black-box optimisation, such as Bayesian optimisation, are used in a variety of applications including hyper-parameter tuning and experiment design.

1 code implementation • NeurIPS 2016 • Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos

However, in many cases, cheap approximations to $\func$ may be obtainable.

no code implementations • NeurIPS 2016 • Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider, Barnabás Póczos

We study a variant of the classical stochastic $K$-armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available.

1 code implementation • 20 Mar 2016 • Kirthevasan Kandasamy, Gautam Dasarathy, Junier B. Oliva, Jeff Schneider, Barnabas Poczos

However, in many cases, cheap approximations to $f$ may be obtainable.

no code implementations • 1 Feb 2016 • Gautam Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Hyuk Park

The problem of learning the structure of a high dimensional graphical model from data has received considerable attention in recent years.

no code implementations • 29 Jun 2015 • Gautam Dasarathy, Robert Nowak, Xiaojin Zhu

This paper investigates the problem of active learning for binary label prediction on a graph.

no code implementations • 28 Apr 2014 • Gautam Dasarathy, Robert Nowak, Sebastien Roch

We consider the problem of estimating the evolutionary history of a set of species (phylogeny or species tree) from several genes.

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