Search Results for author: Gautam Dasarathy

Found 24 papers, 9 papers with code

Label efficient two-sample test

no code implementations17 Nov 2021 Weizhi Li, Gautam Dasarathy, Karthikeyan Natesan Ramamurthy, Visar Berisha

In the traditional formulation of this problem, the statistician has access to both the measurements (feature variables) and the group variable (label variable).

State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks

no code implementations15 Apr 2021 B. Azimian, R. Sen Biswas, A. Pal, Lang Tong, Gautam Dasarathy

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

Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model

1 code implementation25 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.

Community Detection Stochastic Block Model

Finding the Homology of Decision Boundaries with Active Learning

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.

Active Learning Meta-Learning +2

On the alpha-loss Landscape in the Logistic Model

no code implementations22 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.

On the Sample Complexity and Optimization Landscape for Quadratic Feasibility Problems

no code implementations4 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 $\{\langle A_i\mathbf{x}, \mathbf{x}\rangle\}_{i=1}^m$.

Regularization via Structural Label Smoothing

no code implementations7 Jan 2020 Weizhi Li, Gautam Dasarathy, Visar Berisha

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

A Tunable Loss Function for Robust Classification: Calibration, Landscape, and Generalization

no code implementations5 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.

Classification General Classification +1

Thresholding Graph Bandits with GrAPL

1 code implementation22 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.

Decision Making

IdeoTrace: A Framework for Ideology Tracing with a Case Study on the 2016 U.S. Presidential Election

no code implementations21 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.

Ultra Large-Scale Feature Selection using Count-Sketches

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.

Feature Selection

MISSION: Ultra Large-Scale Feature Selection using Count-Sketches

1 code implementation12 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.

Feature Selection

Coalescent-based species tree estimation: a stochastic Farris transform

no code implementations13 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.

DeepCodec: Adaptive Sensing and Recovery via Deep Convolutional Neural Networks

no code implementations11 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.

Compressive Sensing

Multi-fidelity Bayesian Optimisation with Continuous Approximations

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.

Bayesian Optimisation

The Multi-fidelity Multi-armed Bandit

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.

Active Learning Algorithms for Graphical Model Selection

no code implementations1 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.

Active Learning Model Selection

Data Requirement for Phylogenetic Inference from Multiple Loci: A New Distance Method

no code implementations28 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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