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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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5 results for “U-curve”

Learning the Hypotheses Space from data: Learning Space and U-curve Propertystat.ML

Paper introduces a new learning framework with U-curve properties for model selection.

problem Learning problems modeled by Hypothesis Space and Learning Space.

method Data-driven general learning algorithm for Model Selection on Learning Space.

result Conditions on Learning Space and loss function lead to U-curve estimated out-of-sample error surfaces.

Model Selection Hypothesis Space Learning Space U-curve Model Selection Algorithms

2020-01-26abs ↗pdf ↗

Learning the hypotheses space from data through a U-curve algorithmstat.ML

Paper proposes a new approach to Model Selection using a U-curve algorithm.

problem Model Selection in Machine Learning.

method Data-driven, non-exhaustive search of a Learning Space

\mathbb{L}(\mathcal{H})

result Conditions for non-exhaustive search to return optimal solutions.

Model Selection Learning Space U-curve Algorithm PAC Learning VC Dimension

2021-09-08abs ↗pdf ↗

Quintet Volume Projectionq-fin.TR

We present a set of models relevant for predicting various aspects of intra-day trading volume for equities and showcase them as an ensemble that projects volume in unison. We introduce econometric methods for predicting total and remaining daily volume, intra-day volume profile (u-curve), close auction volume and spec…

2019-04-02abs ↗pdf ↗

Learning the Hypotheses Space from data Part II: Convergence and Feasibilitystat.ML

Paper develops a consistent model selection framework for learning Hypotheses Space from data.

problem Avoiding overfitting in complex spaces with limited data.

method Develops a model selection framework based on Learning Spaces, selecting a Hypotheses Space from data.

result The method converges with probability one to a target Hypotheses Space, providing a consistent framework for model selection.

model selection Hypotheses Space Learning Space overfitting generalization error

2020-01-30abs ↗pdf ↗

Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regimecs.LG

Deep networks generalize well even when they fit training data perfectly, thanks to overparametrization.

problem Understanding generalization in overparametrized deep networks.

method Random features regression, asymptotic analysis, ensemble averaging.

result Bias remains constant beyond the interpolation threshold, while variance components decay with overparametrization.

deep learning generalization overparametrization random features bias-variance

2020-03-02abs ↗pdf ↗