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New research establishes near-optimal SQ lower bound for smoothed agnostic learning

Researchers have established a near-optimal Statistical Query (SQ) complexity lower bound for learning Boolean halfspaces with smoothed noise. The study focuses on a model where input coordinates are independently flipped with a certain probability, showing that $L^1$ polynomial regression achieves a specific complexity. This work complements existing research by providing analogous bounds in a continuous setting. AI

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IMPACT Establishes theoretical limits for learning with noisy data, potentially guiding future algorithm development in machine learning.

RANK_REASON Academic paper published on arXiv detailing theoretical computer science research.

Read on arXiv cs.LG →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 · Tim Sinen ·

    A Near-optimal SQ Lower Bound for Smoothed Agnostic Learning of Boolean Halfspaces

    arXiv:2605.02350v1 Announce Type: new Abstract: We study the complexity of smoothed agnostic learning of halfspaces on $\{\pm 1\}^n$ under the uniform distribution in the model of \citet{KM25} where each input coordinate is independently flipped with probability $\sigma \in (0, {…

  2. arXiv cs.LG TIER_1 · Tim Sinen ·

    A Near-optimal SQ Lower Bound for Smoothed Agnostic Learning of Boolean Halfspaces

    We study the complexity of smoothed agnostic learning of halfspaces on $\{\pm 1\}^n$ under the uniform distribution in the model of \citet{KM25} where each input coordinate is independently flipped with probability $σ\in (0, {1}/{2})$. We show that $L^1$ polynomial regression ach…