Joe Neeman
University of Texas at Austin
72 Papers
690 Citations
Joe Neeman is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Gaussian measure & Random graph. The author has an hindex of 24, co-authored 67 publications. Previous affiliations of Joe Neeman include University of Bonn & University of California.
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Papers
Spectral redemption in clustering sparse networks
Florent Krzakala,Cristopher Moore,Elchanan Mossel,Joe Neeman,Allan Sly,Lenka Zdeborová,Pan Zhang +6 more
TL;DR: A way of encoding sparse data using a “nonbacktracking” matrix, and it is shown that the corresponding spectral algorithm performs optimally for some popular generative models, including the stochastic block model.
Reconstruction and estimation in the planted partition model
TL;DR: This work establishes a rigorous connection between the clustering problem, spin-glass models on the Bethe lattice and the so called reconstruction problem and provides a simple and efficient algorithm for estimating a and b when clustering is possible.
479
A Proof of the Block Model Threshold Conjecture
TL;DR: In this article, it was shown that it is information theoretically impossible to cluster if s2 ≤ d and moreover it is even impossible to even estimate the model parameters from the graph when s2 d.
320
Consistency Thresholds for the Planted Bisection Model
Elchanan Mossel,Joe Neeman,Allan Sly +2 more
- 14 Jun 2015
TL;DR: It is shown that the planted bisection is recoverable asymptotically if and only if with high probability every node belongs to the same community as the majority of its neighbors.
180
Regularization in kernel learning
Shahar Mendelson,Joe Neeman +1 more
TL;DR: In this article, the authors obtained the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS) under mild assumptions on the kernel, and showed that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.
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