54 citations · 227 across the 36 of their papers we have counts for
36 papers
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii +4
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linea…
From graphs to DAGs: a low-complexity model and a scalable algorithm
Shuyu Dong, Michèle Sebag
Learning directed acyclic graphs (DAGs) is long known a critical challenge at the core of probabilistic and causal modeling. The NoTears approach of (Zheng et al., 2018), through a…
Frugal Machine Learning
Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos +2
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet…
Boltzmann Tuning of Generative Models
Victor Berger, Michele Sebag
The paper focuses on the a posteriori tuning of a generative model in order to favor the generation of good instances in the sense of some external differentiable criterion. The pr…
Distribution-Based Invariant Deep Networks for Learning Meta-Features
Gwendoline De Bie, Herilalaina Rakotoarison, Gabriel Peyré +1
Recent advances in deep learning from probability distributions successfully achieve classification or regression from distribution samples, thus invariant under permutation of the…
Variational Auto-Encoder: not all failures are equal
Michele Sebag, Victor Berger, Michèle Sebag
We claim that a source of severe failures for Variational Auto-Encoders is the choice of the distribution class used for the observation model.A first theoretical and experimental…