activity
20192022
most citedOn the optimality of kernels for high-dimensional clustering

3 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML20221 cited

A Consistent Estimator for Confounding Strength

Luca Rendsburg, Leena Chennuru Vankadara, Debarghya Ghoshdastidar +1

Regression on observational data can fail to capture a causal relationship in the presence of unobserved confounding. Confounding strength measures this mismatch, but estimating it…

stat.ML2022

Interpolation and Regularization for Causal Learning

Leena Chennuru Vankadara, Luca Rendsburg, Ulrike von Luxburg +1

We study the problem of learning causal models from observational data through the lens of interpolation and its counterpart -- regularization. A large volume of recent theoretical…

cs.LG20211 cited

Graphon based Clustering and Testing of Networks: Algorithms and Theory

Mahalakshmi Sabanayagam, Leena Chennuru Vankadara, Debarghya Ghoshdastidar

Network-valued data are encountered in a wide range of applications and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical exa…

cs.LG2021

Recovery Guarantees for Kernel-based Clustering under Non-parametric Mixture Models

Leena Chennuru Vankadara, Sebastian Bordt, Ulrike von Luxburg +1

Despite the ubiquity of kernel-based clustering, surprisingly few statistical guarantees exist beyond settings that consider strong structural assumptions on the data generation pr…

stat.ML20193 cited

On the optimality of kernels for high-dimensional clustering

Leena Chennuru Vankadara, Debarghya Ghoshdastidar

This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high-dim…