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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.

168,695 papers · 148 categories

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10 results for “Over-conditioning”

Bounded P-values in Parametric Programming-based Selective Inferencestat.ML

New method reduces computational cost for selective inference.

problem Over-conditioning in selective inference.

method Parametric programming-based selective inference (PP-based SI) with bounded p-values.

result Reduced computational cost while maintaining desired precision.

Selective inference Parametric programming Bounded p-values Feature selection Deep neural networks

2023-07-21abs ↗pdf ↗

More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingstat.ML

Enhances selective inference for generalized lasso using parametric programming.

problem Low statistical power in selective inference for generalized lasso.

method Parametric programming to compute solution paths and identify model selection events.

result Improves selective inference power and practicality for various problems.

Selective inference Generalized lasso Parametric programming Statistical power Model selection

2021-05-11abs ↗pdf ↗

Parametric Programming Approach for More Powerful and General Lasso Selective Inferencestat.ML

Proposes a new method to improve selective inference for Lasso models.

problem Over-conditioning due to conditioning on feature signs in selective inference for Lasso.

method Parametric programming approach to avoid conditioning on signs and identify feature selection events.

result Improves power and practicality of selective inference for Lasso models.

Selective Inference Lasso Parametric Programming Feature Selection Over-conditioning

2020-04-21abs ↗pdf ↗

Contextual Information-Directed Samplingcs.LG

IDS improves reinforcement learning with contextual information.

problem Optimizing IDS for contextual reinforcement learning.

method Investigated contextual bandit problems and proposed a computationally-efficient IDS.

result Contextual IDS outperforms conditional IDS by considering future contexts.

reinforcement learning IDS contextual bandits graph feedback sparse linear

2022-05-22abs ↗pdf ↗

More Powerful Selective Kernel Tests for Feature Selectioncs.LG

Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical analysis. An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate…

2019-10-14abs ↗pdf ↗

Coarsening Bias from Variable Discretization in Causal Functionalsstat.ME

New method reduces bias in estimating causal effects from discretized variables.

problem Bias in estimating causal effects from discretized continuous variables.

method Proposes a bias-reduced functional that evaluates outcome regression at within-bin conditional means.

result Demonstrates substantial bias reduction and near-nominal confidence interval coverage.

causal inference variable discretization bias reduction mediation effects nonparametric identification

2026-02-25abs ↗pdf ↗

More Powerful and General Selective Inference for Stepwise Feature Selection using the Homotopy Continuation Approachstat.ML

Develops a more powerful selective inference method for stepwise feature selection.

problem Loss of power in existing conditional SI methods due to over-conditioning.

method Uses homotopy continuation approach to overcome over-conditioning.

result Shows improved power and efficiency in selective inference for feature selection.

Selective Inference Feature Selection Homotopy Continuation Stepwise Feature Selection Powerful Inference

2020-12-25abs ↗pdf ↗

Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programmingstat.ML

Paper introduces a method to assess the statistical reliability of changepoints using selective inference and dynamic programming.

problem Assessing the statistical reliability of detected changepoints.

method Selective inference framework combined with dynamic programming for exact p-value computation.

result Proposes a method with high statistical power and decent computational efficiency.

changepoint detection selective inference dynamic programming p-value computation statistical reliability

2020-02-21abs ↗pdf ↗

Relaxing Faithfulness with Intervention-Only Causal Discoverycs.LG

Paper relaxes faithfulness assumption for causal discovery using interventions.

problem Violation of faithfulness assumption in natural systems leads to incorrect causal structure identification.

method Use intervention-immediacy faithfulness assumption to identify causal structures with hard interventions.

result Interventions contain information about causal structure that can identify causal structures when faithfulness is violated.

causal discovery intervention-immediacy faithfulness hard interventions systemic robustness faithfulness violation

2026-07-13abs ↗pdf ↗

Statistical Robustness of Interval CVaR Based Regression Models under Perturbation and Contaminationmath.OC

Study enhances robustness of In-CVaR based regression models under perturbation and contamination.

problem Enhancing robustness of nonlinear regression models under perturbation and contamination.

method Introduces interval conditional value-at-risk (In-CVaR) and rigorously analyzes its robustness properties under both perturbation and contamination.

result The In-CVaR based estimator is qualitatively robust in terms of the Prokhorov metric if and only if the largest portion of losses is trimmed.

robust regression In-CVaR interval CVaR nonlinear regression contamination

2026-01-16abs ↗pdf ↗