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cs.LG2026
Dual-Attention Convolution Experts for Sparse Tensor Completion
Yanlei Liu, Zhenyu Liao
Tensor factorization (TF) has been widely adopted for high-dimensional sparse data completion tasks. Despite significant progress, neural TF methods often struggle to capture compl…
math.NA2026
Debiasing Random Oblique Projections for Subsampled OLS and Fast CUR in High Dimensions
Chengmei Niu, Sachin Garg, Michał Dereziński +1
Random sampling is a fundamental tool in modern machine learning and numerical linear algebra for reducing the computational cost of large-scale matrix problems. Existing analyses,…
stat.ML2026
Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
Chiheb Yaakoubi, Cosme Louart, Malik Tiomoko +1
We study high-dimensional convex empirical risk minimization (ERM) under general non-Gaussian data designs. By heuristically extending the Convex Gaussian Min-Max Theorem (CGMT) to…