| MLPACK: A scalable C++ machine learning library RR Curtin, JR Cline, NP Slagle, WB March, P Ram, NA Mehta, AG Gray The Journal of Machine Learning Research 14 (1), 801-805, 2013 | 201 | 2013 |
| Fast rates in statistical and online learning T Van Erven, P Grunwald, NA Mehta, M Reid, R Williamson MIT Press, 2015 | 74 | 2015 |
| Sparsity-based generalization bounds for predictive sparse coding N Mehta, A Gray International Conference on Machine Learning, 36-44, 2013 | 43 | 2013 |
| Fast Rates for General Unbounded Loss Functions: From ERM to Generalized Bayes. PD Grünwald, NA Mehta J. Mach. Learn. Res. 21, 56:1-56:80, 2020 | 42 | 2020 |
| Fast rates with high probability in exp-concave statistical learning N Mehta Artificial Intelligence and Statistics, 1085-1093, 2017 | 33 | 2017 |
| A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity PD Grünwald, NA Mehta Algorithmic Learning Theory, 433-465, 2019 | 28 | 2019 |
| On the sample complexity of predictive sparse coding NA Mehta, AG Gray arXiv preprint arXiv:1202.4050, 2012 | 22 | 2012 |
| Fast rates with unbounded losses PD Grünwald, NA Mehta arXiv preprint arXiv:1605.00252 2, 14, 2016 | 20 | 2016 |
| Computer detection approaches for the identification of phasic electromyographic (EMG) activity during human sleep JA Fairley, G Georgoulas, NA Mehta, AG Gray, DL Bliwise Biomedical signal processing and control 7 (6), 606-615, 2012 | 18 | 2012 |
| Generalized mixability via entropic duality MD Reid, RM Frongillo, RC Williamson, N Mehta Conference on Learning Theory, 1501-1522, 2015 | 17 | 2015 |
| Modeling software behavior using learned predicates AX Zheng, MS Musuvathi, NA Mehta US Patent 9,098,621, 2015 | 15 | 2015 |
| From stochastic mixability to fast rates NA Mehta, RC Williamson Advances in Neural Information Processing Systems 27, 2014 | 14 | 2014 |
| Independent component analysis N Mehta | 13 | 2009 |
| Distribution of guava wilt in relation to age, soil type, management practices and varieties grown in Haryana N Mehta Plant Disease Research 2, 116-119, 1987 | 12 | 1987 |
| Safe-Bayesian generalized linear regression R Heide, A Kirichenko, P Grunwald, N Mehta International Conference on Artificial Intelligence and Statistics, 2623-2633, 2020 | 10 | 2020 |
| Problem-dependent regret bounds for online learning with feedback graphs B Hu, NA Mehta, J Pan Uncertainty in Artificial Intelligence, 852-861, 2020 | 9 | 2020 |
| Fast rates for general unbounded loss functions: from ERM to generalized Bayes PD Grünwald, NA Mehta arXiv preprint arXiv:1605.00252, 2016 | 9 | 2016 |
| Generative and latent mean map kernels NA Mehta, AG Gray arXiv preprint arXiv:1005.0188, 2010 | 9 | 2010 |
| VisIRR: interactive visual information retrieval and recommendation for large-scale document data J Choo, C Lee, E Clarkson, Z Liu, H Lee, DHP Chau, F Li, R Kannan, ... Georgia Institute of Technology, 2013 | 8 | 2013 |
| Minimax multi-task learning and a generalized loss-compositional paradigm for MTL N Mehta, D Lee, A Gray Advances in Neural Information Processing Systems 25, 2012 | 8 | 2012 |
Peter GrünwaldGroup Leader Machine Learning, CWI; Professor of Statistics, Leiden UniversityGeverifieerd e-mailadres voor cwi.nl
