Task period selection to minimize hyperperiod. - Vicent Brocal; Patricia Balbastre; Rafael Ballester; Ismael Ripoll (ETFA, 2011)

Period Selection for Minimal Hyperperiod in Periodic Task Systems. - Ismael Ripoll; Rafael Ballester-Ripoll (IEEE Trans. Computers, 2013)

Analysis of tensor approximation for compression-domain volume visualization. - Rafael Ballester-Ripoll; Susanne K. Suter; Renato Pajarola (Comput. Graph., 2015)

Compressing Bidirectional Texture Functions via Tensor Train Decomposition. - Rafael Ballester-Ripoll; Renato Pajarola (PG (Short Papers), 2016)

A surrogate visualization model using the tensor train format. - Rafael Ballester-Ripoll; Enrique G. Paredes; Renato Pajarola (SIGGRAPH Asia Symposium on Visualization, 2016)

Lossy volume compression using Tucker truncation and thresholding. - Rafael Ballester-Ripoll; Renato Pajarola (Vis. Comput., 2016)

Tensor Approximation of Advanced Metrics for Sensitivity Analysis. - Rafael Ballester-Ripoll; Enrique G. Paredes; Renato Pajarola (CoRR, 2017)

Visualization of High-dimensional Scalar Functions Using Principal Parameterizations. - Rafael Ballester-Ripoll; Renato Pajarola (CoRR, 2018)

Tensor Algorithms for Advanced Sensitivity Metrics. - Rafael Ballester-Ripoll; Enrique G. Paredes; Renato Pajarola (SIAM/ASA J. Uncertain. Quantification, 2018)

Multiresolution Volume Filtering in the Tensor Compressed Domain. - Rafael Ballester-Ripoll; David Steiner 003; Renato Pajarola (IEEE Trans. Vis. Comput. Graph., 2018)

SGEMM GPU kernel performance. - Enrique G. Paredes; Rafael Ballester-Ripoll (UCI Machine Learning Repository, 2018)

Tensor Methods for Global Sensitivity Analysis. - Rafael Ballester-Ripoll; Enrique G. Paredes; Renato Pajarola (GI-Jahrestagung, 2019)

VIAN: A Visual Annotation Tool for Film Analysis. - Gaudenz Halter; Rafael Ballester-Ripoll; Barbara Flückiger; Renato Pajarola (Comput. Graph. Forum, 2019)

Sobol tensor trains for global sensitivity analysis. - Rafael Ballester-Ripoll; Enrique G. Paredes; Renato Pajarola (Reliab. Eng. Syst. Saf., 2019)

Tensor Decompositions for Integral Histogram Compression and Look-Up. - Rafael Ballester-Ripoll; Renato Pajarola (IEEE Trans. Vis. Comput. Graph., 2019)

TTHRESH: Tensor Compression for Multidimensional Visual Data. - Rafael Ballester-Ripoll; Peter Lindstrom 001; Renato Pajarola (IEEE Trans. Vis. Comput. Graph., 2020)

Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation. - Mikhail Usvyatsov; Anastasia Makarova; Rafael Ballester-Ripoll; Maxim V. Rakhuba; Andreas Krause 001; Konrad Schindler (ICCV, 2021)

SenVis: Interactive Tensor-based Sensitivity Visualization. - Haiyan Yang; Rafael Ballester-Ripoll; Renato Pajarola (Comput. Graph. Forum, 2021)

Global sensitivity analysis in probabilistic graphical models. - Rafael Ballester-Ripoll; Manuele Leonelli (CoRR, 2021)

T4DT: Tensorizing Time for Learning Temporal 3D Visual Data. - Mikhail Usvyatsov; Rafael Ballester; Lina Bashaeva; Konrad Schindler; Gonzalo Ferrer 001; Ivan V. Oseledets (BMVC, 2022)

You Only Derive Once (YODO): Automatic Differentiation for Efficient Sensitivity Analysis in Bayesian Networks. - Rafael Ballester-Ripoll; Manuele Leonelli (PGM, 2022)

Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks. - Artyom Nikitin; Andrei Chertkov; Rafael Ballester-Ripoll; Ivan V. Oseledets; Evgeny Frolov (CoRR, 2022)

Tensor approximation of cooperative games and their semivalues. - Rafael Ballester-Ripoll (Int. J. Approx. Reason., 2022)

tntorch: Tensor Network Learning with PyTorch. - Mikhail Usvyatsov; Rafael Ballester-Ripoll; Konrad Schindler (J. Mach. Learn. Res., 2022)

Computing Sobol indices in probabilistic graphical models. - Rafael Ballester-Ripoll; Manuele Leonelli (Reliab. Eng. Syst. Saf., 2022)

The YODO algorithm: An efficient computational framework for sensitivity analysis in Bayesian networks. - Rafael Ballester-Ripoll; Manuele Leonelli (Int. J. Approx. Reason., 2023)

Computing Statistical Moments Via Tensorization of Polynomial Chaos Expansions. - Rafael Ballester-Ripoll (SIAM/ASA J. Uncertain. Quantification, 2024)

High-dimensional scalar function visualization using principal parameterizations. - Rafael Ballester-Ripoll; Gaudenz Halter; Renato Pajarola (Vis. Comput., 2024)

Global sensitivity analysis of uncertain parameters in Bayesian networks. - Rafael Ballester-Ripoll; Manuele Leonelli (Int. J. Approx. Reason., 2025)