no code implementations • 8 Feb 2024 • Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet
We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions.
no code implementations • 19 May 2022 • Mathieu Blondel, Felipe Llinares-López, Robert Dadashi, Léonard Hussenot, Matthieu Geist
To learn the parameters of the energy function, the solution to that optimization problem is typically fed into a loss function.
1 code implementation • NeurIPS 2021 • Mathieu Blondel, Quentin Berthet, Marco Cuturi, Roy Frostig, Stephan Hoyer, Felipe Llinares-López, Fabian Pedregosa, Jean-Philippe Vert
In this paper, we propose automatic implicit differentiation, an efficient and modular approach for implicit differentiation of optimization problems.
1 code implementation • 7 Nov 2020 • Karsten Borgwardt, Elisabetta Ghisu, Felipe Llinares-López, Leslie O'Bray, Bastian Rieck
Graph-structured data are an integral part of many application domains, including chemoinformatics, computational biology, neuroimaging, and social network analysis.
2 code implementations • NeurIPS 2019 • Matteo Togninalli, Elisabetta Ghisu, Felipe Llinares-López, Bastian Rieck, Karsten Borgwardt
Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures.
Ranked #7 on Graph Classification on NCI1