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New algorithm tackles group distributionally robust least squares problem

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new algorithm for the group distributionally robust (GDR) least squares problem. This algorithm can achieve a near-optimal solution with a significantly reduced number of linear system solves, particularly in moderate accuracy scenarios. The technical approach leverages a geometric construction known as block Lewis weights to connect the empirical GDR problem to a standard least squares problem, enhanced by accelerated proximal methods. AI

IMPACT This research advances optimization techniques relevant to machine learning, potentially improving the robustness and efficiency of statistical models.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for a statistical problem.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithm tackles group distributionally robust least squares problem

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Naren Sarayu Manoj, Kumar Kshitij Patel ·

    Distributionally Robust Linear Regression With Block Lewis Weights

    arXiv:2607.00252v1 Announce Type: cross Abstract: We present an algorithm for the group distributionally robust (GDR) least squares problem. Given $m$ groups, a parameter vector in $\mathbb{R}^d$, and stacked design matrices and responses $\mathbf{A}$ and $\mathbf{b}$, our algori…

  2. arXiv stat.ML TIER_1 English(EN) · Kumar Kshitij Patel ·

    Distributionally Robust Linear Regression With Block Lewis Weights

    We present an algorithm for the group distributionally robust (GDR) least squares problem. Given $m$ groups, a parameter vector in $\mathbb{R}^d$, and stacked design matrices and responses $\mathbf{A}$ and $\mathbf{b}$, our algorithm obtains a $(1+\varepsilon)$-multiplicative opt…

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