Distributionally Robust Linear Regression With Block Lewis Weights
Naren Sarayu Manoj, Kumar Kshitij Patel
cs.LG, cs.DS, math.OC, stat.ML
Submitted: 2026-06-30
Comments: ICLR 2026. Comments welcome!
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Acceleration Meets Inverse Maintenance: Faster $\ell_{\infty}$-Regression
- Iterative Refinement for $\ell_p$-norm Regression
- Robust Wasserstein Profile Inference and Applications to Machine Learning
- An homotopy method for $\ell_p$ regression provably beyond self-concordance and in input-sparsity time
- Complexity of Highly Parallel Non-Smooth Convex Optimization
- Optimal and Adaptive Monteiro-Svaiter Acceleration
- Acceleration with a Ball Optimization Oracle
- Fair Assortment Planning
- The Frontiers of Fairness in Machine Learning
- Distributionally Robust Formulation and Model Selection for the Graphical Lasso
- Which $L_p$ norm is the fairest? Approximations for fair facility location across all "$p$"
- Sparsifying sums of norms
- Improved Iteration Complexities for Overconstrained $p$-Norm Regression
- Chaining, Group Leverage Score Overestimates, and Fast Spectral Hypergraph Sparsification
- Global linear convergence of Newton's method without strong-convexity or Lipschitz gradients
- Solving Linear Programs with Sqrt(rank) Linear System Solves
- Relatively-Smooth Convex Optimization by First-Order Methods, and Applications
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- Active Linear Regression for $\ell_p$ Norms and Beyond
- Finite-sample analysis of M-estimators using self-concordance
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