arXiv Machine Learning
Learning Positive Linear Combinations of ReLUs in Fully Polynomial Time
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arXiv Machine Learning
Oct 21
Polynomial Learning of Positive ReLU Networks under Gaussian Inputs
arXiv:2610. 25116v2 Announce Type: replace Abstract: We study positive, bias-free, one-hidden-layer ReLU networks under standard Gaussian inputs.
arXiv Machine Learning
Sep 3
Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method
The paper proposes a tensor‑optimization‑powered ensemble method that uses confidence tensors to capture how each weak base classifier performs across different classes. By integrating these tensors and a smooth, partially convex objective that emphasizes margin, the method improves both classification accuracy and generalization while requiring fewer base learners. The authors also prove a property of the loss gradient that enables efficient gradient‑based optimization of the constrained problem.
arXiv Machine Learning
Jun 26
Fast algorithms for learning a Gaussian under halfspace truncation with optimal sample complexity
arXiv:2606. 27298v1 Announce Type: cross Abstract: We study the fundamental problem of learning a high-dimensional Gaussian truncated to an unknown halfspace.
arXiv Machine Learning
Jun 30
Replica Symmetry Breaking and Algorithmic Thresholds in Empirical Risk Minimization under Multi-Index Model
arXiv:2606. 28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions.
arXiv Machine Learning
Jun 16
Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model
arXiv:2606. 15219v1 Announce Type: new Abstract: In this work, we tackle the following question: Can neural networks trained with gradient-based methods achieve the optimal computational-statistical tradeoff in learning Gaussian single-index models?
arXiv Machine Learning
Jul 3
Fast approximation and learning of binary classification tasks in o-minimal structures using ReLU neural networks
arXiv:2607. 01266v1 Announce Type: cross Abstract: We study binary classification problems whose decision sets are given by definable sets in o-minimal expansions of the real field.