28 citations · 33 across the 7 of their papers we have counts for
10 papers
Improved Representation Learning Through Tensorized Autoencoders
Pascal Mattia Esser, Satyaki Mukherjee, Mahalakshmi Sabanayagam +1
The central question in representation learning is what constitutes a good or meaningful representation. In this work we argue that if we consider data with inherent cluster struct…
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…
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…
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…
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…
Near-Optimal Comparison Based Clustering
Michaël Perrot, Pascal Mattia Esser, Debarghya Ghoshdastidar
The goal of clustering is to group similar objects into meaningful partitions. This process is well understood when an explicit similarity measure between the objects is given. How…