The paper introduces μs’ autotune, an automatic tuning strategy for the Lasso that optimizes a penalized Gaussian log‑likelihood over regression coefficients and noise standard deviation. Extensive simulations on regression and VAR models show that autotune is faster and yields better generalization and model selection, especially in low signal‑to‑noise regimes. The method also delivers a new noise‑standard‑deviation estimator, a visual diagnostic for sparsity, and is demonstrated on a real‑world financial dataset, with an accompanying R package available on GitHub.
By Tathagata Sadhukhan, Ines Wilms, Stephan Smeekes, Sumanta Basu
The paper discusses the limitations of LASSO in high‑dimensional regression models when cointegrated regressors are present, noting that the convex penalty can overly shrink their coefficients and lead to inconsistent estimation. It proposes using folded concave penalties (FCP), such as SCAD and MCP, to overcome this issue, establishing both local and global oracle properties for the penalized regression. Simulation results and an application to euro area unemployment forecasting demonstrate that FCP improves variable selection and out‑of‑sample prediction compared to LASSO.
By Hongqi Chen, Chengwang Liao, Ziwei Mei, Zhentao Shi
arXiv:2605.18147v3 Announce Type: replace
Abstract: Predictive models are central to credit risk management, where the accuracy of default-probability and loss estimates affects the profitability of...
By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke
arXiv:1907.06994v2 Announce Type: replace-cross
Abstract: Mixtures of experts (MoE) are conditional mixture models in which both the mixing proportions and the component densities depend on the predi...
By Thin Nguyen-Van, Faicel Chamroukhi, Ha Hoang Van, Bao Tuyen Huynh
arXiv:2610.19351v1 Announce Type: cross
Abstract: In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure out...
By Yundi Kong, Min Yang, Ping-Shou Zhong
SelectArena is a new benchmark for feature selection on real-world tabular data, evaluating 15 popular methods across 71 curated datasets for both classification and regression tasks. The study uses standardized protocols and modern foundation models, revealing trade-offs among predictive performance, runtime, stability, and validity. Tree-based methods (CART, RFImportance) and the F-Test emerge as Pareto-optimal choices, while search-based methods perform poorly in speed and stability.
By Bastian Sch\"afer, Dominika Matus, Lennart Purucker, Ricardo Knauer, Erik Rodner, Frank Hutter, Anton Sokolov, Arnaud Liefooghe
arXiv:2412. 11257v4 Announce Type: replace-cross Abstract: For many complex simulation tasks spanning areas such as healthcare, engineering, and finance, Monte Carlo (MC) methods are invaluable due to their unbiased estimates and precise error quantification.
By Fengpei Li, Haoxian Chen, Jiahe Lin, Arkin Gupta, Xiaowei Tan, Honglei Zhao, Gang Xu, Yuriy Nevmyvaka, Agostino Capponi, Henry Lam
arXiv:2602. 08913v3 Announce Type: replace Abstract: In underdetermined regression and classification problems, multiple feature subsets often yield equivalent predictive performance.
By Kate\v{r}ina Henclov\'a, V\'aclav \v{S}m\'idl
arXiv:2609.22654v1 Announce Type: cross
Abstract: Federated learning (FL) has emerged as a leading privacy-preserving framework for collaborative machine learning across decentralized environments. W...
By Brigham Halverson, Sharmistha Guha, Jessica Bernard, Rajarshi Guhaniyogi
arXiv:2608. 27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns.
By Joshua Le Grice
The paper explores tabular deep learning for equity signal generation, training five model classes on daily data from about 300 large‑cap US stocks over eleven years. By using Bayesian optimisation that targets trading performance across three distinct market regimes, the authors achieve regime‑robust hyperparameter selection, yielding out‑of‑sample signal precision above random and a Hybrid ensemble (XGBoost + TabNet) with an annualised return of 51.26% and a Sharpe ratio of 2.44. The study also finds that alternative data adds limited value beyond technical and fundamental features, and that the ensemble’s outperformance is driven by stock selection rather than market exposure.
arXiv:2609.23937v1 Announce Type: cross
Abstract: Robust linear fits can resist response contamination yet remain too dense or unstable for useful global explanations. We propose penalized distillati...
By Wooyoung Shin, Seunghwan Park