96 citations · 298 across the 54 of their papers we have counts for
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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…
A Revenue Function for Comparison-Based Hierarchical Clustering
Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar
Comparison-based learning addresses the problem of learning when, instead of explicit features or pairwise similarities, one only has access to comparisons of the form: \emph{Objec…
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
The fundamental principle of Graph Neural Networks (GNNs) is to exploit the structural information of the data by aggregating the neighboring nodes using a `graph convolution' in c…
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…