Omar Rivasplata

25 papers A* 5B 1Journal 19

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Generalization and Distributed Learning of GFlowNets. ICLR Tiago Silva, Amauri H. Souza, Omar Rivasplata, Vikas Garg, Samuel Kaski, Diego Mesquita
2025	J	`jnl`	PAC-Bayesian Reinforcement Learning Trains Generalizable Policies. CoRR Abdelkrim Zitouni, Mehdi Hennequin, Juba Agoun, Ryan Horache, Nadia Kabachi, Omar Rivasplata
2025	J	`jnl`	Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. Trans. Mach. Learn. Res. Matteo Tucat, Anirbit Mukherjee, Procheta Sen, Mingfei Sun, Omar Rivasplata
2025	J	`jnl`	Semi-pessimistic Reinforcement Learning. CoRR Jin Zhu, Xin Zhou, Jiaang Yao, Gholamali Aminian, Omar Rivasplata, Simon Little, Lexin Li, Chengchun Shi
2024	B	`conf`	A Note on Generalization Bounds for Losses with Finite Moments. ISIT Borja Rodríguez Gálvez, Omar Rivasplata, Ragnar Thobaben, Mikael Skoglund
2024	J	`jnl`	A Note on the Convergence of Denoising Diffusion Probabilistic Models. Trans. Mach. Learn. Res. Sokhna Diarra Mbacke, Omar Rivasplata
2024	J	`jnl`	A note on generalization bounds for losses with finite moments. CoRR Borja Rodríguez Gálvez, Omar Rivasplata, Ragnar Thobaben, Mikael Skoglund
2023	J	`jnl`	A Note on the Convergence of Denoising Diffusion Probabilistic Models. CoRR Sokhna Diarra Mbacke, Omar Rivasplata
2022	J	`jnl`	Semi-Counterfactual Risk Minimization Via Neural Networks. CoRR Gholamali Aminian, Roberto Vega, Omar Rivasplata, Laura Toni, Miguel R. D. Rodrigues
2021	J	`jnl`	Learning PAC-Bayes Priors for Probabilistic Neural Networks. CoRR María Pérez-Ortiz, Omar Rivasplata, Benjamin Guedj, Matthew Gleeson, Jingyu Zhang, John Shawe-Taylor, Miroslaw Bober, Josef Kittler
2021	A*	`conf`	On the Role of Optimization in Double Descent: A Least Squares Study. NeurIPS Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu
2021	J	`jnl`	On the Role of Optimization in Double Descent: A Least Squares Study. CoRR Ilja Kuzborskij, Csaba Szepesvári, Omar Rivasplata, Amal Rannen-Triki, Razvan Pascanu
2021	J	`jnl`	PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. Entropy Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
2021	J	`jnl`	Progress in Self-Certified Neural Networks. CoRR María Pérez-Ortiz, Omar Rivasplata, Emilio Parrado-Hernández, Benjamin Guedj, John Shawe-Taylor
2021	J	`jnl`	Tighter Risk Certificates for Neural Networks. J. Mach. Learn. Res. María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári
2020	A*	`conf`	Logarithmic Pruning is All You Need. NeurIPS Laurent Orseau, Marcus Hutter, Omar Rivasplata
2020	J	`jnl`	Logarithmic Pruning is All You Need. CoRR Laurent Orseau, Marcus Hutter, Omar Rivasplata
2020	A*	`conf`	PAC-Bayes Analysis Beyond the Usual Bounds. NeurIPS Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor
2020	J	`jnl`	PAC-Bayes Analysis Beyond the Usual Bounds. CoRR Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, John Shawe-Taylor
2020	J	`jnl`	PAC-Bayes unleashed: generalisation bounds with unbounded losses. CoRR Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
2020	J	`jnl`	Tighter risk certificates for neural networks. CoRR María Pérez-Ortiz, Omar Rivasplata, John Shawe-Taylor, Csaba Szepesvári
2020	J	`jnl`	Upper and Lower Bounds on the Performance of Kernel PCA. CoRR Maxime Haddouche, Benjamin Guedj, Omar Rivasplata, John Shawe-Taylor
2019	J	`jnl`	PAC-Bayes with Backprop. CoRR Omar Rivasplata, Vikram M. Tankasali, Csaba Szepesvári
2018	A*	`conf`	PAC-Bayes bounds for stable algorithms with instance-dependent priors. NeurIPS Omar Rivasplata, Csaba Szepesvári, John Shawe-Taylor, Emilio Parrado-Hernández, Shiliang Sun
2018	J	`jnl`	PAC-Bayes bounds for stable algorithms with instance-dependent priors. CoRR Omar Rivasplata, Emilio Parrado-Hernández, John Shawe-Taylor, Shiliang Sun, Csaba Szepesvári