Omar Rivasplata

According to our database, Omar Rivasplata authored at least 16 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

Semi-pessimistic Reinforcement Learning.

Jin Zhu, Xin Zhou, Jiaang Yao, Gholamali Aminian, Omar Rivasplata, Simon Little, Lexin Li, Chengchun Shi

CoRR, May, 2025

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks.

Matteo Tucat, Anirbit Mukherjee, Procheta Sen, Mingfei Sun, Omar Rivasplata

Trans. Mach. Learn. Res., 2025

Generalization and Distributed Learning of GFlowNets.

Tiago Silva, Amauri H. Souza, Omar Rivasplata, Vikas Garg, Samuel Kaski, Diego Mesquita

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

A Note on the Convergence of Denoising Diffusion Probabilistic Models.

Sokhna Diarra Mbacke, Omar Rivasplata

Trans. Mach. Learn. Res., 2024

A Note on Generalization Bounds for Losses with Finite Moments.

Borja Rodríguez Gálvez, Omar Rivasplata, Ragnar Thobaben, Mikael Skoglund

Proceedings of the IEEE International Symposium on Information Theory, 2024

2022

Semi-Counterfactual Risk Minimization Via Neural Networks.

Gholamali Aminian, Roberto Vega, Omar Rivasplata, Laura Toni, Miguel R. D. Rodrigues

CoRR, 2022

2021

Tighter Risk Certificates for Neural Networks.

María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári

J. Mach. Learn. Res., 2021

PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses.

Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor

Entropy, 2021

Progress in Self-Certified Neural Networks.

María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor

CoRR, 2021

Learning PAC-Bayes Priors for Probabilistic Neural Networks.

María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler

CoRR, 2021

On the Role of Optimization in Double Descent: A Least Squares Study.

Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Upper and Lower Bounds on the Performance of Kernel PCA.

Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor

CoRR, 2020

PAC-Bayes Analysis Beyond the Usual Bounds.

Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Logarithmic Pruning is All You Need.

Laurent Orseau, Marcus Hutter, Omar Rivasplata

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

PAC-Bayes with Backprop.

Omar Rivasplata, Vikram M. Tankasali, Csaba Szepesvári

CoRR, 2019

2018

PAC-Bayes bounds for stable algorithms with instance-dependent priors.

Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018