Author

D. Ghoshdastidar

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#artificial intelligence Preprint Oct 2026

Lifting Finite-Sample Gaussian Approximation of Wide Neural Networks to Functional Convergence

Randomly initialised neural networks of width $m$ converge to Gaussian processes as $m \to \infty$, with two types of quantitative bounds characterising this convergence. Finite-sample bounds compare the network with its Gaussian limit at a fixed finite sample set, on an arbitrary domain, in the quadratic Wasserstein d...

Luis Wiederhold, Nil Ayday, Debarghya Ghoshdastidar · 0 citations

#machine learning Preprint Nov 2024

Explainable Clustering of Mixture Models

This paper proposes an algorithm that leverages information about the distribution of the data to find better cuts, and extends the algorithm and the theoretical guarantees it provides to kernel clustering, thereby refining the existing worst-case analysis.

Maximilian Fleissner, Maedeh Zarvandi, D. Ghoshdastidar · 1 citation

#machine learning Preprint Sep 2026

An Analysis of Self-supervised Pre-training with Dependent Samples

It is shown that pooling augmentations together, despite inter-dependencies, is a better alternative than the baseline of partitioning the data into subsets of independent data and provides an intuition behind the practical preference towards using many augmentations.

Maximilian Fleissner, D. Ghoshdastidar, Samory Kpotufe · 0 citations