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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.

169,051 papers · 148 categories

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0111 · Nov 201019922001200920172026
4 results for covariance-penalties

This paper proposes a new method to prevent backtesting overfitting in trading strategies.

problem Preventing misleading results in backtesting of trading strategies.
method Covariance-Penalty Correction approach to reduce risk metrics based on the number of parameters and data used.
result Covariance-Penalties are effective in avoiding backtesting overfitting, with Total Least Squares outperforming Ordinary Least Squares.

Study ablated data augmentation techniques and their mathematical equivalence to penalties.

problem Lack of mathematical understanding of differences between ablated data augmentation techniques.
method Formal model of mean ablated data augmentation and inverted dropout for linear regression; empirical validation for deep networks.
result Ablated data augmentation and inverted dropout are mathematically equivalent to penalties in optimization.

With a growing interest in using non-representative samples to train prediction models for numerous outcomes it is necessary to account for the sampling design that gives rise to the data in order to assess the generalized predictive utility of a proposed prediction rule. After learning a prediction rule based on a non…

2017-11-13abs ↗pdf ↗

For high-dimensional classification, it is well known that naively performing the Fisher discriminant rule leads to poor results due to diverging spectra and noise accumulation. Therefore, researchers proposed independence rules to circumvent the diverse spectra, and sparse independence rules to mitigate the issue of n…

2010-11-28abs ↗pdf ↗