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20152026
most citedLearning-to-Learn Stochastic Gradient Descent with Biased Regularization

45 citations · 168 across the 23 of their papers we have counts for

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Showing 2018Show all

5 papers · 1 filter

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 "…

stat.ML2018

Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance

Giulia Luise, Alessandro Rudi, Massimiliano Pontil +1

Applications of optimal transport have recently gained remarkable attention thanks to the computational advantages of entropic regularization. However, in most situations the Sinkh…

quant-ph2018

Approximating Hamiltonian dynamics with the Nyström method

Alessandro Rudi, Leonard Wossnig, Carlo Ciliberto +3

Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms f…

stat.ML2018

Incremental Learning-to-Learn with Statistical Guarantees

Giulia Denevi, Carlo Ciliberto, Dimitris Stamos +1

In learning-to-learn the goal is to infer a learning algorithm that works well on a class of tasks sampled from an unknown meta distribution. In contrast to previous work on batch…