| Higher spin gravity amplitudes from zero-form charges N Colombo, P Sundell arXiv preprint arXiv:1208.3880, 2012 | 76 | 2012 |
| Bayesian semi-supervised learning with graph gaussian processes YC Ng, N Colombo, R Silva Advances in Neural Information Processing Systems 31, 2018 | 71 | 2018 |
| A minimal BV action for Vasiliev’s four-dimensional higher spin gravity N Boulanger, N Colombo, P Sundell Journal of High Energy Physics 2012 (10), 1-36, 2012 | 50 | 2012 |
| Twistor space observables and quasi-amplitudes in 4D higher-spin gravity N Colombo, P Sundell Journal of High Energy Physics 2011 (11), 1-47, 2011 | 38 | 2011 |
| Tensor decomposition via joint matrix Schur decomposition N Colombo, N Vlassis International Conference on Machine Learning, 2820-2828, 2016 | 20 | 2016 |
| FastMotif: spectral sequence motif discovery N Colombo, N Vlassis Bioinformatics 31 (16), 2623-2631, 2015 | 17 | 2015 |
| Expression of the Parkinson’s disease-associated gene alpha-synuclein is regulated by the neuronal cell fate determinant TRIM32 MAS Pavlou, N Colombo, S Fuertes-Alvarez, S Nicklas, LG Cano, ... Molecular neurobiology 54 (6), 4257-4270, 2017 | 16 | 2017 |
| Training conformal predictors N Colombo, V Vovk https://arxiv.org/abs/2005.07037, 2020 | 9 | 2020 |
| Adapting by pruning: A case study on BERT Y Gao, N Colombo, W Wang arXiv preprint arXiv:2105.03343, 2021 | 8 | 2021 |
| Hybrid short-term load forecasting method based on empirical wavelet transform and bidirectional long short-term memory neural networks X Zhang, S Kuenzel, N Colombo, C Watkins Journal of Modern Power Systems and Clean Energy 10 (5), 1216-1228, 2022 | 5 | 2022 |
| Approximate joint matrix triangularization N Colombo, N Vlassis arXiv preprint arXiv:1607.00514, 2016 | 5 | 2016 |
| A posteriori error bounds for joint matrix decomposition problems N Colombo, N Vlassis Advances in Neural Information Processing Systems 29, 2016 | 4 | 2016 |
| Experimental design trade-offs for gene regulatory network inference: An in silico study of the yeast Saccharomyces cerevisiae cell cycle J Markdahl, N Colombo, J Thunberg, J Gonçalves 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 423-428, 2017 | 3 | 2017 |
| Tomography of the London underground: A scalable model for origin-destination data N Colombo, R Silva, SM Kang Advances in Neural Information Processing Systems 30, 2017 | 2 | 2017 |
| Disentangling neural architectures and weights: A case study in supervised classification N Colombo, Y Gao arXiv preprint arXiv:2009.05346, 2020 | 1 | 2020 |
| Counterfactual Distribution Regression for Structured Inference N Colombo, R Silva, SM Kang, A Gretton arXiv preprint arXiv:1908.07193, 2019 | 1 | 2019 |
| Stable Spectral Learning Based on Schur Decomposition. N Colombo, N Vlassis UAI, 220-227, 2015 | 1 | 2015 |
| Differentiable Architecture Pruning for Transfer Learning N Colombo, Y Gao arXiv preprint arXiv:2107.03375, 2021 | | 2021 |
| Multiple Metric Learning for Structured Data N Colombo https://arxiv.org/abs/2002.05747, 2020 | | 2020 |
