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20132022
most citedBridging Mode Connectivity in Loss Landscapes and Adversarial Robustness

33 citations · 93 across the 16 of their papers we have counts for

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6 papers · 1 filter

stat.ML2018

Topological Data Analysis of Decision Boundaries with Application to Model Selection

Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Krishnan Mody

We propose the labeled Čech complex, the plain labeled Vietoris-Rips complex, and the locally scaled labeled Vietoris-Rips complex to perform persistent homology inference of decis…

stat.ML2018

Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models

Ming Yu, Karthikeyan Natesan Ramamurthy, Addie Thompson +1

We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along…

stat.ML2017

Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections

Jayaraman J. Thiagarajan, Shusen Liu, Karthikeyan Natesan Ramamurthy +1

Two-dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non-linear ones, which can capture complex r…

stat.ML2017

Distribution-Preserving k-Anonymity

Dennis Wei, Karthikeyan Natesan Ramamurthy, Kush R. Varshney

Preserving the privacy of individuals by protecting their sensitive attributes is an important consideration during microdata release. However, it is equally important to preserve…

stat.ML20174 cited

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties

Ming Yu, Addie M. Thompson, Karthikeyan Natesan Ramamurthy +2

Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps…

stat.ML2017

Learning Robust Representations for Computer Vision

Peng Zheng, Aleksandr Y. Aravkin, Karthikeyan Natesan Ramamurthy +1

Unsupervised learning techniques in computer vision often require learning latent representations, such as low-dimensional linear and non-linear subspaces. Noise and outliers in th…