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Ioana Dumitriu

3 accepted papers

2024

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models

ICLR 2024spotlight

A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines. A second trend has been to utilize kernel functions in various explain-by-example or data attribution tasks. In this work, we combine…

2023

Spectral Evolution and Invariance in Linear-width Neural Networks

NeurIPS 2023poster

We investigate the spectral properties of linear-width feed-forward neural networks, where the sample size is asymptotically proportional to network width. Empirically, we show that the spectra of weight in this high dimensional regime are invariant when trained by gradient descent for small constan…

Cited by 20SourcePDFScholar
2016

Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models

NeurIPS 2016poster

The Stochastic Block Model (SBM) is a widely used random graph model for networks with communities. Despite the recent burst of interest in community detection under the SBM from statistical and computational points of view, there are still gaps in understanding the fundamental limits of recovery. I…

Cited by 8SourcePDFScholar