Introduces TSBM, a new Bayesian model for directed networks that enforces ordered blocks via transitivity-inducing priors on directional imbalance and jointly infers block count with an age-ordered partition prior.
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
There are various parametric models for analyzing pairwise comparison data, including the Bradley-Terry-Luce (BTL) and Thurstone models, but their reliance on strong parametric assumptions is limiting. In this work, we study a flexible model for pairwise comparisons, under which the probabilities of outcomes are required only to satisfy a natural form of stochastic transitivity. This class includes parametric models including the BTL and Thurstone models as special cases, but is considerably more general. We provide various examples of models in this broader stochastically transitive class for which classical parametric models provide poor fits. Despite this greater flexibility, we show that the matrix of probabilities can be estimated at the same rate as in standard parametric models. On the other hand, unlike in the BTL and Thurstone models, computing the minimax-optimal estimator in the stochastically transitive model is non-trivial, and we explore various computationally tractable alternatives. We show that a simple singular value thresholding algorithm is statistically consistent but does not achieve the minimax rate. We then propose and study algorithms that achieve the minimax rate over interesting sub-classes of the full stochastically transitive class. We complement our theoretical results with thorough numerical simulations.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.
citing papers explorer
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Ordering Stochastic Block Models via prior transitivity
Introduces TSBM, a new Bayesian model for directed networks that enforces ordered blocks via transitivity-inducing priors on directional imbalance and jointly infers block count with an age-ordered partition prior.
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Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences
Presents a robust algorithm for learning any coordinate-wise non-decreasing evaluator preference function, with theoretical guarantees that it matches linear performance when linearity holds.