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arXiv research

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.

169,181 papers · 148 categories

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2 results for “super-gradient”

Concave losses for robust dictionary learningcs.LG

Paper proposes robust dictionary learning using concave losses.

problem Sensitivity to outliers in traditional dictionary learning methods.

method Generic framework based on concave losses, with results on composition of concave functions.

result Method better detects outliers and generates better dictionaries.

dictionary learning robustness concave losses outliers super-gradient

2017-11-02abs ↗pdf ↗

Dual Iterative Hard Thresholding: From Non-convex Sparse Minimization to Non-smooth Concave Maximizationcs.LG

Iterative Hard Thresholding (IHT) is a class of projected gradient descent methods for optimizing sparsity-constrained minimization models, with the best known efficiency and scalability in practice. As far as we know, the existing IHT-style methods are designed for sparse minimization in primal form. It remains open t…

2017-03-01abs ↗pdf ↗