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Research PaperResearchia:202609.10029

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

Xuan Li

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: transductive online learning of thresholds on an unknown total order of T instances, with a consistency-type ERM oracle that returns a full concept consistent with a queried labeled set (or reports non-realizability). Our main result is a separation for a fixed natural ora...

Submitted: September 10, 2026Subjects: Statistics; Data Science

Description / Details

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: transductive online learning of thresholds on an unknown total order of T instances, with a consistency-type ERM oracle that returns a full concept consistent with a queried labeled set (or reports non-realizability). Our main result is a separation for a fixed natural oracle. When the oracle is the minimal-prefix rule (or the maximal-prefix rule), every deterministic learner makes M mistakes and Q calls with M+Qβ‰₯Tβˆ’Ξ΅M+Q\ge T-\varepsilon on some instance (Ρ∈{0,1}\varepsilon\in\{0,1\}, according to whether the empty prefix is a concept), and the constant is exact; hence O(log⁑T)O(\log T) mistakes cost Tβˆ’Ξ΅βˆ’O(log⁑T)T-\varepsilon-O(\log T) calls, whereas that paper's randomized learner achieves O(log⁑T)O(\log T) expected calls and mistakes under the same rule. The randomized order is optimal: on an explicit hard distribution under the minimal-prefix rule, every learner has expected mistakes at least ((T+1βˆ’Ξ΅) 128βˆ’E[Q]βˆ’1)/2((T+1-\varepsilon)\,128^{-\mathbb{E}[Q]}-1)/2, so Ξ©(log⁑T)Ξ©(\log T) expected calls are necessary for polylogarithmic mistakes. The separation is governed by the oracle's selection rule, not by the class alone: for a legal feasible-median ERM rule a deterministic learner achieves O(log⁑T)O(\log T) calls and mistakes, while a global-median rule again forces linear total cost. The same linear bound holds when the oracle's answers are chosen adversarially and then frozen into a memoryless oracle. We add partial tradeoff results for fixed query budgets (the middle regime is open) and an interface contrast: with only a weak consistency oracle, returning a realizability bit, both deterministic and randomized learners need Θ(T)Θ(T) calls.


Source: arXiv:2609.10196v1 - http://arxiv.org/abs/2609.10196v1 PDF: https://arxiv.org/pdf/2609.10196v1 Original Link: http://arxiv.org/abs/2609.10196v1

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Date:
Sep 10, 2026
Topic:
Data Science
Area:
Statistics
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