95 citations · 291 across the 29 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
The Multi-fidelity Multi-armed Bandit
Kirthevasan Kandasamy, Gautam Dasarathy, Jeff Schneider +1
We study a variant of the classical stochastic -armed bandit where observing the outcome of each arm is expensive, but cheap approximations to this outcome are available. For ex…
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification
Gautam Dasarathy, Robert Nowak, Xiaojin Zhu
This paper investigates the problem of active learning for binary label prediction on a graph. We introduce a simple and label-efficient algorithm called S2 for this task. At each…