Differentially private Riemannian optimization.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1133, doi. 10.1007/s10994-023-06508-5
- By:
- Publication type:
- Article
Works matching IS 08856125 AND DT 2024 AND VI 113 AND IP 3
Results: 17
1
2
Utilising energy function and variational inference training for learning a graph neural network architecture.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1219, doi. 10.1007/s10994-024-06513-2
- By:
- Publication type:
- Article
3
Correction to: Learning to bid and rank together in recommendation systems.
- Published in:
- 2024
- By:
- Publication type:
- Correction Notice
4
OT-net: a reusable neural optimal transport solver.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1243, doi. 10.1007/s10994-023-06493-9
- By:
- Publication type:
- Article
5
Persistence B-spline grids: stable vector representation of persistence diagrams based on data fitting.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1373, doi. 10.1007/s10994-023-06492-w
- By:
- Publication type:
- Article
6
Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1445, doi. 10.1007/s10994-023-06447-1
- By:
- Publication type:
- Article
7
Fast deep mixtures of Gaussian process experts.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1483, doi. 10.1007/s10994-023-06491-x
- By:
- Publication type:
- Article
8
On the effects of biased quantum random numbers on the initialization of artificial neural networks.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1189, doi. 10.1007/s10994-023-06490-y
- By:
- Publication type:
- Article
9
Principled diverse counterfactuals in multilinear models.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1421, doi. 10.1007/s10994-023-06411-z
- By:
- Publication type:
- Article
10
Explainable models via compression of tree ensembles.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1303, doi. 10.1007/s10994-023-06463-1
- By:
- Publication type:
- Article
11
Modeling PU learning using probabilistic logic programming.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1351, doi. 10.1007/s10994-023-06461-3
- By:
- Publication type:
- Article
12
Structural causal models reveal confounder bias in linear program modelling.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1329, doi. 10.1007/s10994-023-06431-9
- By:
- Publication type:
- Article
13
No regret sample selection with noisy labels.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1163, doi. 10.1007/s10994-023-06478-8
- By:
- Publication type:
- Article
14
Improving fraud detection via imbalanced graph structure learning.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1069, doi. 10.1007/s10994-023-06464-0
- By:
- Publication type:
- Article
15
Word embeddings-based transfer learning for boosted relational dependency networks.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1269, doi. 10.1007/s10994-023-06404-y
- By:
- Publication type:
- Article
16
Composition of relational features with an application to explaining black-box predictors.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1091, doi. 10.1007/s10994-023-06399-6
- By:
- Publication type:
- Article
17
Partial counterfactual identification and uplift modeling: theoretical results and real-world assessment.
- Published in:
- Machine Learning, 2024, v. 113, n. 3, p. 1043, doi. 10.1007/s10994-023-06317-w
- By:
- Publication type:
- Article