arXiv Statistics ML By Johan Mylius-Kroken, Elisabeth Wetzer, Ali Ramezani-Kebrya, Robert Jenssen, Kristoffer Knutsen Wickstr{\o}m

Intrinsic Information-Theoretic Analysis of ReLU Nets

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arXiv:2610. 36864v1 Announce Type: new Abstract: Mutual information (MI) is fundamental to representation-learning analysis in deep networks, but in high dimensions MI estimators depend on hyperparameters whose bias often dominates the signal.

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arXiv Machine Learning
Aug 26

Multi-Source Complex Network Reconstruction via Wasserstein Distributionally Robust Optimization and Algorithm Unrolling

The paper introduces MS‑WDRO, a multi‑source Wasserstein distributionally robust optimization framework for reconstructing complex network topologies from scarce target‑domain data and abundant heterogeneous source data. It fuses sources via a weighted Wasserstein barycenter, builds an ambiguity set around it, and solves a regularized Laplacian estimator using a provably convergent ADMM scheme. The authors provide finite‑sample guarantees, demonstrate that naive aggregation is suboptimal, and show through experiments on synthetic data and the ABIDE I neuroimaging dataset that MS‑WDRO outperforms seven baselines in graph recovery, sample efficiency, and diagnostic utility, especially when target samples are limited.

By Chuansen Peng, Yifan Xia, Jinshan Zhong, Xiaojing Shen