| Characterization of a benchmark database for myoelectric movement classification M Atzori, A Gijsberts, I Kuzborskij, S Elsig, AGM Hager, O Deriaz, ... IEEE transactions on neural systems and rehabilitation engineering 23 (1), 73-83, 2014 | 293 | 2014 |
| Stability and hypothesis transfer learning I Kuzborskij, F Orabona International Conference on Machine Learning, 942-950, 2013 | 199 | 2013 |
| From n to n+ 1: Multiclass transfer incremental learning I Kuzborskij, F Orabona, B Caputo Proceedings of the IEEE conference on computer vision and pattern …, 2013 | 167 | 2013 |
| On the challenge of classifying 52 hand movements from surface electromyography I Kuzborskij, A Gijsberts, B Caputo 2012 annual international conference of the IEEE engineering in medicine and …, 2012 | 159 | 2012 |
| Data-Dependent Stability of Stochastic Gradient Descent I Kuzborskij, CH Lampert International Conference on Machine Learning, 2018 | 156 | 2018 |
| PAC-Bayes analysis beyond the usual bounds O Rivasplata, I Kuzborskij, C Szepesvári, J Shawe-Taylor Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 84 | 2020 |
| Fast Rates by Transferring from Auxiliary Hypotheses I Kuzborskij, F Orabona Machine Learning, 2016 | 71 | 2016 |
| Confident off-policy evaluation and selection through self-normalized importance weighting I Kuzborskij, C Vernade, A Gyorgy, C Szepesvári International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 48 | 2021 |
| When Naive Bayes Nearest Neighbours Meet Convolutional Neural Networks I Kuzborskij, FM Carlucci, B Caputo Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016 | 34 | 2016 |
| Tighter PAC-Bayes Bounds Through Coin-Betting K Jang, KS Jun, I Kuzborskij, F Orabona Conference on Learning Theory (COLT), 2023 | 28 | 2023 |
| Transfer Learning through Greedy Subset Selection I Kuzborskij, B Caputo, F Orabona 18th International Conference on Image Analysis and Processing — ICIAP 2015, 2015 | 27 | 2015 |
| Efron-stein pac-bayesian inequalities I Kuzborskij, C Szepesvári arXiv preprint arXiv:1909.01931, 2019 | 22 | 2019 |
| Efficient linear bandits through matrix sketching I Kuzborskij, L Cella, N Cesa-Bianchi International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | 21 | 2019 |
| Scalable greedy algorithms for transfer learning I Kuzborskij, F Orabona, B Caputo Computer Vision and Image Understanding 156, 174-185, 2017 | 21 | 2017 |
| Distribution-Dependent Analysis of Gibbs-ERM Principle I Kuzborskij, N Cesa-Bianchi, C Szepesvári Conference on Learning Theory (COLT), 2019 | 19 | 2019 |
| Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel D Richards, I Kuzborskij Advances in Neural Information Processing Systems 33, 2021 | 16 | 2021 |
| A Distribution-Dependent Analysis of Meta-Learning M Konobeev, I Kuzborskij, C Szepesvári International Conference on Machine Learning (ICML), 2021 | 14* | 2021 |
| On the role of optimization in double descent: A least squares study I Kuzborskij, C Szepesvári, O Rivasplata, A Rannen-Triki, R Pascanu Conference on Neural Information Processing Systems (NeurIPS), 2021 | 12 | 2021 |
| Theory and algorithms for hypothesis transfer learning I Kuzborskij EPFL, 2018 | 9 | 2018 |
| Learning lipschitz functions by gd-trained shallow overparameterized relu neural networks I Kuzborskij, C Szepesvári arXiv preprint arXiv:2212.13848, 2022 | 8* | 2022 |
Barbara CaputoDAUIN, Politecnico di Torino在 polito.it 的电子邮件经过验证
