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Researchers develop online Newton method with accelerated sketching for efficient inference

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

A new paper introduces an online Newton method that uses Nesterov's accelerated sketching to approximate Newton directions. This approach aims to provide robust uncertainty quantification for streaming data while maintaining computational efficiency comparable to first-order methods. The method quantifies uncertainty from both data and computation, establishing convergence guarantees and developing an online covariance estimator. Experiments show its superiority for online inference in regression models. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Introduces a more computationally efficient method for uncertainty quantification in online learning, potentially improving decision-making with streaming data.

RANK_REASON This is a research paper published on arXiv detailing a new method for online inference.

Read on arXiv stat.ML →

COVERAGE [2]

  1. arXiv stat.ML TIER_1 · Haoxuan Wang, Xinchen Du, Sen Na ·

    Inference of Online Newton Methods with Nesterov's Accelerated Sketching

    arXiv:2604.23436v1 Announce Type: new Abstract: Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order methods enable efficient iterate updates, their inference procedures still require updating proper (cov…

  2. arXiv stat.ML TIER_1 · Sen Na ·

    Inference of Online Newton Methods with Nesterov's Accelerated Sketching

    Reliable decision-making with streaming data requires principled uncertainty quantification of online methods. While first-order methods enable efficient iterate updates, their inference procedures still require updating proper (covariance) matrices, incurring $O(d^2)$ time and m…

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