30 citations · 59 across the 7 of their papers we have counts for
6 papers · 1 filter
Explaining Probabilistic Models with Distributional Values
Luca Franceschi, Michele Donini, Cédric Archambeau +1
A large branch of explainable machine learning is grounded in cooperative game theory. However, research indicates that game-theoretic explanations may mislead or be hard to interp…
DAG Learning on the Permutahedron
Valentina Zantedeschi, Luca Franceschi, Jean Kaddour +2
We propose a continuous optimization framework for discovering a latent directed acyclic graph (DAG) from observational data. Our approach optimizes over the polytope of permutatio…
Learning Discrete Directed Acyclic Graphs via Backpropagation
Andrew J. Wren, Pasquale Minervini, Luca Franceschi +1
Recently continuous relaxations have been proposed in order to learn Directed Acyclic Graphs (DAGs) from data by backpropagation, instead of using combinatorial optimization. Howev…
Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions
Mathias Niepert, Pasquale Minervini, Luca Franceschi
Combining discrete probability distributions and combinatorial optimization problems with neural network components has numerous applications but poses several challenges. We propo…
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
Michele Donini, Luca Franceschi, Massimiliano Pontil +2
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization. We describe the structure…
Learning Discrete Structures for Graph Neural Networks
Luca Franceschi, Mathias Niepert, Massimiliano Pontil +1
Graph neural networks (GNNs) are a popular class of machine learning models whose major advantage is their ability to incorporate a sparse and discrete dependency structure between…