On-device research index

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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8 results for “split-improvement”

Unbiased Measurement of Feature Importance in Tree-Based Methodsstat.ML

Corrects bias in feature importance measures of tree-based methods.

problem Bias in feature importance measures of tree-based methods.

method Corrects bias by incorporating out-of-sample split-improvement.

result Better summaries and screening tools of feature importance.

feature importance tree-based methods bias correction split-improvement random forest

2019-03-12abs ↗pdf ↗

Data splitting improves statistical performance in overparametrized regimesstat.ML

Data splitting enhances model performance in overparametrized ridgeless regression.

problem Computational inefficiency in training models with large datasets.

method Data splitting as a regularization technique in overparametrized ridgeless regression.

result Data splitting improves statistical performance and computational complexity.

data splitting overparametrization ridgeless regression benign overfitting statistical performance

2021-10-21abs ↗pdf ↗

Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretabilitystat.ML

Proposes SCD-split for CP to balance interpretability and efficiency.

problem Difficult interpretation of disconnected subintervals in CP prediction sets.

method Incorporates smoothing operations into CP framework.

result SCD-split balances interval length and subinterval number, theoretically provable.

Conformal Prediction Smoothing Interpretability Efficiency Subintervals

2025-09-26abs ↗pdf ↗

Temporal Difference Learning as Gradient Splittingcs.LG

Temporal difference learning explained through gradient splitting, improving convergence times.

problem Learning value functions in Markov Decision Processes with linear approximations.

method Interpreting TD learning as gradient splitting and applying convergence proofs from gradient descent.

result Improved convergence times for TD learning, especially with a minor variation.

temporal difference learning gradient descent convergence Markov decision processes linear function approximation

2020-10-27abs ↗pdf ↗

Adaptive Multilevel Splitting: First Application to Rare-Event Derivative Pricingq-fin.CP

Adaptive Multilevel Splitting improves rare event pricing for financial derivatives.

problem Efficient pricing of binary options in rare event regimes with discontinuous payoffs.

method Adaptive Multilevel Splitting (AMS) reformulates rare-event problem as conditional events.

result AMS achieves up to 200-fold improvements over standard Monte Carlo, preserving unbiasedness.

Rare-event pricing Binary options Monte Carlo Adaptive multilevel splitting Financial derivatives

2025-10-27abs ↗pdf ↗

Reshuffling Resampling Splits Can Improve Generalization of Hyperparameter Optimizationstat.ML

Reshuffling splits improves hyperparameter optimization's generalization performance.

problem Improving peak performance of machine learning models through better hyperparameter optimization.

method Reshuffling splits for every hyperparameter configuration improves generalization performance.

result Reshuffling leads to better generalization performance compared to fixed splits.

hyperparameter optimization machine learning generalization resampling reshuffling

2024-05-24abs ↗pdf ↗

Optimal Data Splitting for Holdout Cross-Validation in Large Covariance Matrix Estimationmath.ST

Optimal data splitting improves covariance matrix estimation in large datasets.

problem Improving large covariance matrix estimation in high-dimensional settings.

method Focus on holdout method, derive closed-form error expression, connect to eigenvalue variance.

result Optimal train-test split scales as square root of matrix dimension.

covariance matrix cross-validation large data white inverse Wishart eigenvalue distribution

2025-03-19abs ↗pdf ↗

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcs.LG

Efficiently verifies neural networks by handling neuron splits, improving speed and accuracy.

problem Handling neuron split constraints in incomplete neural network verification.

method β-CROWN, which optimizes parameters β to encode neuron splits and uses them in bound propagation.

result β-CROWN significantly speeds up verification while maintaining high accuracy.

neural network verification bound propagation neuron splits complete verification incomplete verification

2021-03-11abs ↗pdf ↗