Hugging Face Trending Papers

Feature Bagging Provides Stability

We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on randomly subsampled feature subsets, possibly in a data-dependent manner.

arXiv Machine Learning
Aug 4

Beyond Noise: A Hypothesis Testing Approach to Robust Feature Selection

arXiv:2511. 20851v3 Announce Type: replace-cross Abstract: Feature selection remains difficult in modern high-dimensional settings, and established methods such as Boruta and Recursive Feature Elimination are either computationally costly or lack a statistically justified stopping criterion for their importance scores.

By Mousam Sinha, Tirtha Sarathi Ghosh, Koushik Biswas, Ridam Pal
arXiv Machine Learning
Jul 7

Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning

arXiv:2607. 03839v1 Announce Type: new Abstract: Sparse feature selection is critical for high-dimensional machine learning, yet traditional $\ell_1$-regularized methods are often brittle under observational noise and spurious correlations, leading to unstable feature supports and degraded generalization.

By Zhen Huang, Peicheng Xu, Junbiao Pang, Yulong Zheng
Hugging Face Trending Papers
6d ago

Bagging Robustly Learns VC Classes with Linear Sample Complexity

We revisit the problem of learning predictors robust to adversarial examples at test-time. We prove that VC classes are adversarially robustly learnable with sample complexity linear in the VC dimension $d$, providing an exponential improvement over the previous upper bound of Montasser, Hanneke, and Srebro (2019).

arXiv Machine Learning
Jul 17

Cross-Cluster Weighted Forests

arXiv:2105. 07610v5 Announce Type: replace-cross Abstract: Building trustworthy machine learning algorithms for biological applications requires adapting to data heterogeneity from different sources, batches, distributions, or studies.

By Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani