arXiv:2610.33957v1 Announce Type: cross Abstract: We propose a joint sparse sample weighting and parameter estimation (SWAPE) framework that jointly optimizes model parameters and sample weights to t...
arXiv:2607. 07735v1 Announce Type: cross Abstract: Sparse precision matrix estimation provides an interpretable and computationally efficient framework for modeling conditional dependencies in high-dimensional, low-sample-size data.
arXiv:2606. 01155v1 Announce Type: cross Abstract: Scaling laws for dense LLMs under infinite data are well explored, but how sparsity interacts with limited data is not.
arXiv:2606. 11738v1 Announce Type: cross Abstract: We study online estimation for high-dimensional generalized linear models with streaming data.
arXiv:2610.08345v1 Announce Type: cross Abstract: Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample s...
arXiv:2609.36396v1 Announce Type: cross Abstract: As black-box machine learning models become increasingly common, extracting interpretations with uncertainty quantification has become a critical cha...
The paper introduces Conditional Poisson Sampling (CPS) as a fixed-size alternative to Poisson Sampling (PS) for variance reduction in statistical inference. CPS can replace PS in multivariate Z-estimation problems, including convex M-estimation, without increasing asymptotic variance and often improving it when predictions are imperfect. The authors demonstrate these benefits on synthetic and real datasets and provide a Python package for CPS implementation.
arXiv:2506. 01486v2 Announce Type: replace Abstract: Data imbalance persists as a pervasive challenge in regression tasks, introducing bias in model performance and undermining predictive reliability.
The paper proposes a unified framework for estimating sparse precision and covariance matrices by reparametrizing the last column of either matrix. This reparametrization reduces the likelihood optimization to a one‑dimensional minimization followed by a penalized multiple regression, applicable to any penalty separable in off‑diagonal entries and linear on the diagonal. When the penalty is the α1 norm, the method simultaneously recovers the graphical Lasso and the covariance graphical Lasso algorithms.
The paper introduces an online generalized-sparsity-constrained regression framework that addresses key challenges in online sparse regression, such as dynamic regularization, memory usage, and real-time computation. It proposes an efficient online hard‑thresholding algorithm that performs closed‑form updates using only summary statistics, achieving global convergence at optimal statistical rates when the projection set is overparameterized. Numerical experiments show the method consistently outperforms existing alternatives in online cardinality‑constrained linear regression and low‑rank matrix sensing.
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...
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.