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NeurIPS 2026Telecom ParisTélécom ParisTechLearning theory

Beyond Kemeny Medians: Consensus Ranking Distributions. Definition, Properties and Statistical Learning

Consensus ranking distributions approximate ranking distributions via sparse Dirichlet mixtures with optimal Kendall τ distortion expressed through pairwise probabilities, enabling efficient tree-structured statistical learning.

Stephan Clémençon, Ekhine Irurozki

Paris Poster Session 2, Wed, Dec 9, 5:00 PM–7:00 PM, Paris Poster Hall · Published 2026

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medium 2/10
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NeurIPS 2026Institut Polytechnique de ParisVrije Universiteit AmsterdamTélécom ParisTechUniversity College LondonXuanwu HospitalExplainable AI

Predicting Only from Selected Evidence: A Tempered Product-of-Experts Bottleneck for Auditable EEG Diagnosis

tPoE-EIB constrains EEG diagnosis to selected evidence via tempered product-of-experts fusion, improving auditable selection and integration faithfulness while preserving accuracy.

Yinghao WANG, Shujian Yu, Duc-Han LE, Zhikai Yu and 2 more

Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026

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16/20 AI panelreviewers recommend it

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medium 10/10
strict 1/5
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NeurIPS 2026Télécom ParisTechTélécom ParisTelecom ParisMulti-agent LLM systems

Decentralized Ranking Aggregation via Gossip: Convergence and Robustness

Gossip algorithms enable decentralized ranking aggregation via local peer interactions, yielding global consensus without central authority despite corrupted nodes and distributed preferences.

Kerrian Le Caillec, Anna van Elst, Igor Colin, Stephan Clémençon

Paris Poster Session 4, Thu, Dec 10, 5:30 PM–7:30 PM, Paris Poster Hall · Published 2026

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AI panel: 4 of 20 reviewers recommend it
lenient 3/5
medium 1/10
strict 0/5