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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.

168,742 papers · 148 categories

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0111 · Dec 201519922001200920172026
9 results for minimum-divergence

Unified framework improves neural network robustness against label noise and adversarial attacks.

problem High sensitivity of neural networks to data contamination, including label noises and adversarial perturbations.
method Unified minimum-divergence estimation problem, rSDNet framework.
result Improves robustness to label corruption and adversarial attacks while maintaining competitive accuracy on clean data.

A new robust PCA estimator combining M-estimators and minimum divergence estimators.

problem Adverse effect of outlying observations in PCA for high-dimensional data.
method Minimum density power divergence estimator combined with a computationally efficient algorithm.
result High breakdown guarantee regardless of data dimension with theoretical support and practical applications.

Generative model uses ODEs and RKHSs for measure matching.

problem Minimum divergence generative modeling and sampling.
method Diffeomorphic matching and image registration principles applied to ODEs and RKHSs.
result Theoretical error bounds and extensive numerical experiments demonstrate the method's properties and applicability.

Financial exchange operators cater to the needs of their users while simultaneously ensuring compliance with the financial regulations. In this work, we focus on the operators' commitment for fair treatment of all competing participants. We first discuss unbounded temporal fairness and then investigate its implementati…

2019-11-20abs ↗pdf ↗

In this work, a novel solution to the speaker identification problem is proposed through minimization of statistical divergences between the probability distribution (g). of feature vectors from the test utterance and the probability distributions of the feature vector corresponding to the speaker classes. This approac…

2015-12-16abs ↗pdf ↗

DM framework improves robustness and efficiency in latent-mixture models.

problem Efficient and robust inference in latent-mixture models.
method Divergence-minimization framework with monotonic convergence and robustness guarantees.
result DM yields consistent and asymptotically normal estimators under correct specification.