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Found 7 papers, 4 papers with code
Learning to Re-rank with Constrained Meta-Optimal Transport
Many re-ranking strategies in search systems rely on stochastic ranking policies, encoded as Doubly-Stochastic (DS) matrices, that satisfy desired ranking constraints in expectation, e. g., Fairness of Exposure (FOE).
Approximate Birkhoff-von-Neumann decomposition: a differentiable approach
We offer an empirical evaluation of this approach in the fairness of exposure in rankings, where we show that the outcome of our method behaves similarly to greedy algorithms.
Aligning Hyperbolic Representations: an Optimal Transport-based approach
As a result of this formalism, we derive extensions to some existing Euclidean methods of OT-based domain adaptation to their hyperbolic counterparts.
Local Bures-Wasserstein Transport: A Practical and Fast Mapping Approximation
Optimal transport (OT)-based methods have a wide range of applications and have attracted a tremendous amount of attention in recent years.
Recursive nearest agglomeration (ReNA): fast clustering for approximation of structured signals
Our goal is to summarize the data to decrease computational costs and memory footprint of subsequent analysis.
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Decoding, ie prediction from brain images or signals, calls for empirical evaluation of its predictive power.
Fast clustering for scalable statistical analysis on structured images
The use of brain images as markers for diseases or behavioral differences is challenged by the small effects size and the ensuing lack of power, an issue that has incited researchers to rely more systematically on large cohorts.
