A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New findings show the large margins theory is insufficient for explaining ensemble methods.
We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm.
The paper improves robust optimization by introducing margin theory.
New lower bounds nearly match existing upper bounds for boosted classifiers.
Paper improves forest representation learning by optimizing margin distribution.
Paper explains AdaBoost's overfitting resistance from feature learning perspective.
Weisfeiler-Leman struggles with graph isomorphism; enhanced architectures improve generalization.
Study generalization of voting classifiers using margin-based bounds.
Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass of information about the entire margin distribution, which is crucial …
We propose a new active learning strategy designed for deep neural networks. The goal is to minimize the number of data annotation queried from an oracle during training. Previous active learning strategies scalable for deep networks were mostly based on uncertain sample selection. In this work, we focus on examples ly…
In the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional Euclidean space via a set of quadruple-wise comparisons. These ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. However, colle…
Ant colonies and boosting algorithms both reduce bias and variance through adaptive mechanisms.