| 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 | 155 | 2014 |
| 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 | 130 | 2013 |
| Stability and hypothesis transfer learning I Kuzborskij, F Orabona International Conference on Machine Learning, 942-950, 2013 | 118 | 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 | 110 | 2012 |
| Data-Dependent Stability of Stochastic Gradient Descent I Kuzborskij, CH Lampert International Conference on Machine Learning, 2018 | 75 | 2018 |
| Fast Rates by Transferring from Auxiliary Hypotheses I Kuzborskij, F Orabona Machine Learning, 2016 | 43 | 2016 |
| 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 | 26 | 2016 |
| Transfer Learning through Greedy Subset Selection I Kuzborskij, B Caputo, F Orabona 18th International Conference on Image Analysis and Processing — ICIAP 2015, 2015 | 25 | 2015 |
| Scalable greedy algorithms for transfer learning I Kuzborskij, F Orabona, B Caputo Computer Vision and Image Understanding 156, 174-185, 2017 | 18 | 2017 |
| 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 | 16 | 2020 |
| Efficient linear bandits through matrix sketching I Kuzborskij, L Cella, N Cesa-Bianchi International Conference on Artificial Intelligence and Statistics (AISTATS), 2019 | 10 | 2019 |
| 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, 640-648, 2021 | 6 | 2021 |
| Distribution-Dependent Analysis of Gibbs-ERM Principle I Kuzborskij, N Cesa-Bianchi, C Szepesvári Conference on Learning Theory (COLT), 2019 | 6 | 2019 |
| Nonparametric Online Regression while Learning the Metric I Kuzborskij, N Cesa-Bianchi Advances in Neural Information Processing Systems 31 (NIPS-17), 2017 | 5 | 2017 |
| Efron-Stein PAC-Bayesian Inequalities I Kuzborskij, C Szepesvári arXiv preprint arXiv:1909.01931, 2019 | 4 | 2019 |
| Theory and algorithms for hypothesis transfer learning I Kuzborskij EPFL, 2018 | 4 | 2018 |
| On Optimality of Meta-Learning in Fixed-Design Regression with Weighted Biased Regularization M Konobeev, I Kuzborskij, C Szepesvári arXiv preprint arXiv:2011.00344, 2020 | 3 | 2020 |
| Locally-Adaptive Nonparametric Online Learning I Kuzborskij, N Cesa-Bianchi Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | | 2020 |
| Supplementary Material for Data-Dependent Stability of Stochastic Gradient Descent I Kuzborskij, CH Lampert | | |
| Supplementary Material for “Nonparametric Online Regression while Learning the Metric” I Kuzborskij, N Cesa-Bianchi | | |
Barbara CaputoPolitecnico di Torino & Italian Institute of TechnologyVerified email at polito.it
