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Controlling the false dis- covery rate: A practical and powerful approach to multiple testing,

Canonical reference. 71% of citing Pith papers cite this work as background.

98 Pith papers citing it
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representative citing papers

Generative Robust Optimisation

cs.LG · 2026-06-21 · unverdicted · novelty 7.0

Generative Robust Optimisation defines uncertainty sets via neural network decoders over latent spaces and evaluates them with a five-point framework, validated on planning problems using Wasserstein autoencoders.

A Unified Theory of Ownership Concentration, Overlap, and Dependence

q-fin.PM · 2026-05-26 · unverdicted · novelty 7.0

Develops a unified quadratic framework for ownership concentration with exact row/column decompositions, benchmark-adjusted dependence, multiscale aggregation, spectral characterizations, and dynamic bounds on fire-sale vulnerability and alpha variance.

Priority Scheduling in the M/G/1 with Preemption Overhead

cs.PF · 2026-05-02 · unverdicted · novelty 7.0

Derives recursive Laplace transform for response time distribution in M/G/1 preemptive priority with stochastic preemption overhead and introduces job joint transform for general overhead models.

Audio-Based Understanding of Audiobook Narration Appeal

cs.CL · 2026-07-02 · unverdicted · novelty 6.0

Acoustic features from narration show a robust association with audiobook appeal independent of title effects, based on analysis of LibriVox data and proprietary metrics.

The Effortless Trap: Productive Struggle, AI, and the Illusion of Learning

cs.CY · 2026-06-24 · unverdicted · novelty 6.0

Proposes a six-move framework (Prime, Probe, Point, Attach, Strengthen, Test) for learning with AI, using an 'effortless' diagnostic to avoid illusion of mastery, backed by cited evidence of design-dependent outcomes including 17% harm from unguarded AI and doubled gains from engineered tutors.

Constrained Variable Projection for Structured Problems

math.OC · 2026-06-22 · unverdicted · novelty 6.0

Extends variable projection to constrained separable nonlinear least-squares via bilevel collapse, yielding exact reduced gradients and a convergent conditional-gradient algorithm.

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