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Found 14 papers, 8 papers with code
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning
The Yeo-Johnson (YJ) transformation is a standard parametrized per-feature unidimensional transformation often used to Gaussianize features in machine learning.
Near-optimal estimation of smooth transport maps with kernel sums-of-squares
It was recently shown that under smoothness conditions, the squared Wasserstein distance between two distributions could be efficiently computed with appealing statistical error upper bounds.
Learning PSD-valued functions using kernel sums-of-squares
Shape constraints such as positive semi-definiteness (PSD) for matrices or convexity for functions play a central role in many applications in machine learning and sciences, including metric learning, optimal transport, and economics.
A Note on Optimizing Distributions using Kernel Mean Embeddings
Kernel mean embeddings are a popular tool that consists in representing probability measures by their infinite-dimensional mean embeddings in a reproducing kernel Hilbert space.
A Dimension-free Computational Upper-bound for Smooth Optimal Transport Estimation
It is well-known that plug-in statistical estimation of optimal transport suffers from the curse of dimensionality.
Statistics Theory Optimization and Control Statistics Theory 62G05
Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form
Although optimal transport (OT) problems admit closed form solutions in a very few notable cases, e. g. in 1D or between Gaussians, these closed forms have proved extremely fecund for practitioners to define tools inspired from the OT geometry.
Entropic Optimal Transport between (Unbalanced) Gaussian Measures has a Closed Form
Although optimal transport (OT) problems admit closed form solutions in a very few notable cases, e. g. in 1D or between Gaussians, these closed forms have proved extremely fecund for practitioners to define tools inspired from the OT geometry.
Statistics Theory Statistics Theory
Dimension-free convergence rates for gradient Langevin dynamics in RKHS
In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dimensional Hilbert space.
Missing Data Imputation using Optimal Transport
Missing data is a crucial issue when applying machine learning algorithms to real-world datasets.
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
A popular approach to avoid this curse is to project input measures on lower-dimensional subspaces (1D lines in the case of sliced Wasserstein distances), solve the OT problem between these reduced measures, and settle for the Wasserstein distance between these reductions, rather than that between the original measures.
Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
We propose in this work an extension of that approach, which consists in embedding objects as elliptical probability distributions, namely distributions whose densities have elliptical level sets.
Large Margin Nearest Neighbor Classification using Curved Mahalanobis Distances
We consider the supervised classification problem of machine learning in Cayley-Klein projective geometries: We show how to learn a curved Mahalanobis metric distance corresponding to either the hyperbolic geometry or the elliptic geometry using the Large Margin Nearest Neighbor (LMNN) framework.
Tsallis Regularized Optimal Transport and Ecological Inference
We also present the first application of optimal transport to the problem of ecological inference, that is, the reconstruction of joint distributions from their marginals, a problem of large interest in the social sciences.
