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Found 6 papers, 2 papers with code
Fair Coresets via Optimal Transport
In this work, we present fair Wasserstein coresets (FWC), a novel coreset approach which generates fair synthetic representative samples along with sample-level weights to be used in downstream learning tasks.
FairWASP: Fast and Optimal Fair Wasserstein Pre-processing
FairWASP can therefore be used to construct datasets which can be fed into any classification method, not just methods which accept sample weights.
Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization
The Frank-Wolfe method has become increasingly useful in statistical and machine learning applications, due to the structure-inducing properties of the iterates, and especially in settings where linear minimization over the feasible set is more computationally efficient than projection.
Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
Data lies at the core of modern deep learning.
From an Interior Point to a Corner Point: Smart Crossover
The crossover method, which aims at deriving an optimal extreme point from a suboptimal solution (the output of a starting method such as interior-point methods or first-order methods), is crucial in this process.
Optimization and Control 90C05
Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem
Computing the Wasserstein barycenter of a set of probability measures under the optimal transport metric can quickly become prohibitive for traditional second-order algorithms, such as interior-point methods, as the support size of the measures increases.
