Rafael Ballester-Ripoll

34 papers A* 1Journal 29Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Global sensitivity analysis of uncertain parameters in Bayesian networks. Int. J. Approx. Reason. Rafael Ballester-Ripoll, Manuele Leonelli
2024	J	`jnl`	Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions. SIAM/ASA J. Uncertain. Quantification Rafael Ballester-Ripoll
2024	J	`jnl`	Global Sensitivity Analysis of Uncertain Parameters in Bayesian Networks. CoRR Rafael Ballester-Ripoll, Manuele Leonelli
2024	J	`jnl`	High-dimensional scalar function visualization using principal parameterizations. Vis. Comput. Rafael Ballester-Ripoll, Gaudenz Halter, Renato Pajarola
2023	J	`jnl`	The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. CoRR Rafael Ballester-Ripoll, Manuele Leonelli
2023	J	`jnl`	The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. Int. J. Approx. Reason. Rafael Ballester-Ripoll, Manuele Leonelli
2022	J	`jnl`	Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks. CoRR Artyom Nikitin, Andrei Chertkov, Rafael Ballester-Ripoll, Ivan V. Oseledets, Evgeny Frolov
2022	J	`jnl`	Computing Sobol indices in probabilistic graphical models. Reliab. Eng. Syst. Saf. Rafael Ballester-Ripoll, Manuele Leonelli
2022	J	`jnl`	T4DT: Tensorizing Time for Learning Temporal 3D Visual Data. CoRR Mikhail Usvyatsov, Rafael Ballester-Ripoll, Lina Bashaeva, Konrad Schindler, Gonzalo Ferrer, Ivan V. Oseledets
2022	J	`jnl`	Tensor approximation of cooperative games and their semivalues. Int. J. Approx. Reason. Rafael Ballester-Ripoll
2022	—	`conf`	You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. PGM Rafael Ballester-Ripoll, Manuele Leonelli
2022	J	`jnl`	You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. CoRR Rafael Ballester-Ripoll, Manuele Leonelli
2022	J	`jnl`	tntorch: Tensor Network Learning with PyTorch. CoRR Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
2022	J	`jnl`	tntorch: Tensor Network Learning with PyTorch. J. Mach. Learn. Res. Mikhail Usvyatsov, Rafael Ballester-Ripoll, Konrad Schindler
2021	A*	`conf`	Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. ICCV Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler
2021	J	`jnl`	Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. CoRR Mikhail Usvyatsov, Anastasia Makarova, Rafael Ballester-Ripoll, Maxim V. Rakhuba, Andreas Krause, Konrad Schindler
2021	J	`jnl`	Global sensitivity analysis in probabilistic graphical models. CoRR Rafael Ballester-Ripoll, Manuele Leonelli
2021	J	`jnl`	SenVis: Interactive Tensor-based Sensitivity Visualization. Comput. Graph. Forum Haiyan Yang, Rafael Ballester-Ripoll, Renato Pajarola
2020	J	`jnl`	TTHRESH: Tensor Compression for Multidimensional Visual Data. IEEE Trans. Vis. Comput. Graph. Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola
2019	J	`jnl`	Sobol tensor trains for global sensitivity analysis. Reliab. Eng. Syst. Saf. Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
2019	J	`jnl`	Tensor Decompositions for Integral Histogram Compression and Look-Up. IEEE Trans. Vis. Comput. Graph. Rafael Ballester-Ripoll, Renato Pajarola
2019	—	`conf`	Tensor Methods for Global Sensitivity Analysis. GI-Jahrestagung Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
2019	J	`jnl`	VIAN: A Visual Annotation Tool for Film Analysis. Comput. Graph. Forum Gaudenz Halter, Rafael Ballester-Ripoll, Barbara Flückiger, Renato Pajarola
2018	J	`jnl`	Multiresolution Volume Filtering in the Tensor Compressed Domain. IEEE Trans. Vis. Comput. Graph. Rafael Ballester-Ripoll, David Steiner, Renato Pajarola
2018	J	`jnl`	TTHRESH: Tensor Compression for Multidimensional Visual Data. CoRR Rafael Ballester-Ripoll, Peter Lindstrom, Renato Pajarola
2018	J	`jnl`	Tensor Algorithms for Advanced Sensitivity Metrics. SIAM/ASA J. Uncertain. Quantification Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
2018	J	`jnl`	Visualization of High-dimensional Scalar Functions Using Principal Parameterizations. CoRR Rafael Ballester-Ripoll, Renato Pajarola
2017	J	`jnl`	Sobol Tensor Trains for Global Sensitivity Analysis. CoRR Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
2017	J	`jnl`	Tensor Approximation of Advanced Metrics for Sensitivity Analysis. CoRR Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
2016	—	`conf`	A surrogate visualization model using the tensor train format. SIGGRAPH Asia Symposium on Visualization Rafael Ballester-Ripoll, Enrique G. Paredes, Renato Pajarola
2016	—	`conf`	Compressing Bidirectional Texture Functions via Tensor Train Decomposition. PG (Short Papers) Rafael Ballester-Ripoll, Renato Pajarola
2016	J	`jnl`	Lossy volume compression using Tucker truncation and thresholding. Vis. Comput. Rafael Ballester-Ripoll, Renato Pajarola
2015	J	`jnl`	Analysis of tensor approximation for compression-domain volume visualization. Comput. Graph. Rafael Ballester-Ripoll, Susanne K. Suter, Renato Pajarola
2013	J	`jnl`	Period Selection for Minimal Hyperperiod in Periodic Task Systems. IEEE Trans. Computers Ismael Ripoll, Rafael Ballester-Ripoll