Robust Multi-Task Learning for Principal Component Analysis
math.ST, stat.ME, stat.ML, stat.TH
Submitted: 2026-09-17
Updated: 2026-09-17
Comments: 52 pages, 12 figures. All comments are warmly welcomed
Code: https://github.com/ChenMengjie/DepthDescent
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- LEAF: A Benchmark for Federated Settings
- Unveiling Invariant and Transferable Latent Factors Across Heterogeneous Environments via ATLAS
- Statistical Analysis of Conditional Group Distributionally Robust Optimization with Cross-Entropy Loss
- TransPCA for Large-dimensional Factor Analysis with Weak Factors: Power Enhancement via Knowledge Transfer
- Tuning-Free Efficient Estimation for Multi-Source Data via Covariance-Aware Shrinkage
- Multi-Task Dynamic Pricing in Credit Market with Contextual Information
- Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
- Knowledge Transfer across Multiple Principal Component Analysis Studies
- Multi-Task Learning with Covariate-Overlap Regularization
- Contaminated Multi-task Learning with Heterogeneity: Fundamental Limits and Optimal Algorithms
- StablePCA: Distributionally Robust Learning of Shared Representations from Multi-Source Data
- Estimating shared subspace with AJIVE: the power and limitation of multiple data matrices
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