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Researchers find exponential gap in AI learning complexity between deterministic and randomized methods

By PulseAugur Editorial · [1 sources] ·

Researchers have identified an exponential gap in the complexity of online learning between deterministic and randomized approaches when dealing with thresholds on an unknown order. A study by Attias, Hanneke, and Ramaswami, presented at NeurIPS 2025, demonstrates that while deterministic learners require a total of T (or T-epsilon) oracle calls and mistakes, a randomized learner can achieve logarithmic bounds for both. This separation is contingent on the specific rule used by the consistency-type ERM oracle, with different rules leading to varying performance outcomes. AI

IMPACT Highlights theoretical limitations and potential improvements in AI learning algorithms.

RANK_REASON Academic paper detailing theoretical findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers find exponential gap in AI learning complexity between deterministic and randomized methods

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Academic paper detailing theoretical findings in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Xuan Li ·

    An Exponential Deterministic--Randomized Gap in ERM-Oracle Complexity for Thresholds on an Unknown Order

    arXiv:2609.10196v1 Announce Type: cross Abstract: Attias, Hanneke and Ramaswami (NeurIPS 2025) asked whether randomization provably reduces the oracle calls needed for online learning when the class is accessible only through an oracle. We study the instance they singled out: tra…

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