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20152022
most citedLearning-to-Learn Stochastic Gradient Descent with Biased Regularization

45 citations · 168 across the 20 of their papers we have counts for

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8 papers · 1 filter

stat.ML202012 cited

Generalization Properties of Optimal Transport GANs with Latent Distribution Learning

Giulia Luise, Massimiliano Pontil, Carlo Ciliberto

The Generative Adversarial Networks (GAN) framework is a well-established paradigm for probability matching and realistic sample generation. While recent attention has been devoted…

stat.ML2020

Hyperbolic Manifold Regression

Gian Maria Marconi, Lorenzo Rosasco, Carlo Ciliberto

Geometric representation learning has recently shown great promise in several machine learning settings, ranging from relational learning to language processing and generative mode…

stat.ML20207 cited

A General Framework for Consistent Structured Prediction with Implicit Loss Embeddings

Carlo Ciliberto, Lorenzo Rosasco, Alessandro Rudi

We propose and analyze a novel theoretical and algorithmic framework for structured prediction. While so far the term has referred to discrete output spaces, here we consider more…

stat.ML20199 cited

Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm

Giulia Luise, Saverio Salzo, Massimiliano Pontil +1

We present a novel algorithm to estimate the barycenter of arbitrary probability distributions with respect to the Sinkhorn divergence. Based on a Frank-Wolfe optimization strategy…

stat.ML2018

Manifold Structured Prediction

Alessandro Rudi, Carlo Ciliberto, Gian Maria Marconi +1

Structured prediction provides a general framework to deal with supervised problems where the outputs have semantically rich structure. While classical approaches consider finite,…

stat.ML2018

Localized Structured Prediction

Carlo Ciliberto, Francis Bach, Alessandro Rudi

Key to structured prediction is exploiting the problem structure to simplify the learning process. A major challenge arises when data exhibit a local structure (e.g., are made by "…