arXiv Statistics ML
Scale-invariant Optimal Sampling for Rare-events Data with Sparse Models
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The Regularization Parameter: Sparse Precision Matrix Estimation
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.
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When Data Is Scarce: Scaling Sparse Language Models with Repeated Training
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.
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Renewable Lasso without Batch-Number Constraints: A Gradient-Enhanced Approach
arXiv:2606. 11738v1 Announce Type: cross Abstract: We study online estimation for high-dimensional generalized linear models with streaming data.
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Model-agnostic Mitigation Strategies of Data Imbalance for Regression
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.
arXiv Machine Learning
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Online Generalized Sparse Regression: How Does Overparametrization Help?
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 Machine Learning
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Formal Bayesian Transfer Learning via the Total Risk Prior
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