| Pyro: Deep universal probabilistic programming E Bingham, JP Chen, M Jankowiak, F Obermeyer, N Pradhan, ... The Journal of Machine Learning Research 20 (1), 973-978, 2019 | 1044 | 2019 |
| Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks A Altheimer, S Arora, L Asquith, G Brooijmans, J Butterworth, ... Journal of Physics G: Nuclear and Particle Physics 39 (6), 063001, 2012 | 408 | 2012 |
| Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 A Altheimer, A Arce, L Asquith, J Backus Mayes, EB Kuutmann, J Berger, ... The European Physical Journal C 74, 1-24, 2014 | 285 | 2014 |
| Composable effects for flexible and accelerated probabilistic programming in NumPyro D Phan, N Pradhan, M Jankowiak arXiv preprint arXiv:1912.11554, 2019 | 208 | 2019 |
| Towards an understanding of the correlations in jet substructure: Report of BOOST2013, hosted by the University of Arizona, 12th–16th of August 2013 D Adams, A Arce, L Asquith, M Backovic, T Barillari, P Berta, D Bertolini, ... The European Physical Journal C 75, 1-52, 2015 | 177 | 2015 |
| Analysis of 6.4 million SARS-CoV-2 genomes identifies mutations associated with fitness F Obermeyer, M Jankowiak, N Barkas, SF Schaffner, JD Pyle, ... Science 376 (6599), 1327-1332, 2022 | 147 | 2022 |
| Constraining -violating Higgs sectors at the LHC using gluon fusion MJ Dolan, P Harris, M Jankowiak, M Spannowsky Physical Review D 90 (7), 073008, 2014 | 134 | 2014 |
| LHC probes the hidden sector J Jaeckel, M Jankowiak, M Spannowsky Physics of the Dark Universe 2 (3), 111-117, 2013 | 116 | 2013 |
| Jet dipolarity: top tagging with color flow A Hook, M Jankowiak, JG Wacker Journal of High Energy Physics 2012 (4), 1-15, 2012 | 110 | 2012 |
| Jet substructure without trees M Jankowiak, AJ Larkoski Journal of High Energy Physics 2011 (6), 1-21, 2011 | 109 | 2011 |
| Pathwise Derivatives Beyond the Reparameterization Trick M Jankowiak, F Obermeyer Proceedings of the 35th International Conference on Machine Learning, PMLR …, 2018 | 106 | 2018 |
| Variational Bayesian optimal experimental design A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ... Advances in Neural Information Processing Systems 32, 2019 | 99 | 2019 |
| LHC probes the 4251 hidden sector J Jaeckel, M Jankowiak, M Spannowsky Phys. Dark Univ 2, 111-117, 2013 | 84 | 2013 |
| Learning how to count: a high multiplicity search for the LHC S El Hedri, A Hook, M Jankowiak, JG Wacker Journal of High Energy Physics 2013 (8), 1-38, 2013 | 74 | 2013 |
| High-dimensional Bayesian optimization with sparse axis-aligned subspaces D Eriksson, M Jankowiak Uncertainty in Artificial Intelligence, 493-503, 2021 | 62 | 2021 |
| Parametric gaussian process regressors M Jankowiak, G Pleiss, J Gardner International Conference on Machine Learning, 4702-4712, 2020 | 60 | 2020 |
| Event extraction using distant supervision K Reschke, M Jankowiak, M Surdeanu, CD Manning, D Jurafsky Language Resources and Evaluation Conference (LREC), 2014 | 47 | 2014 |
| A unified stochastic gradient approach to designing bayesian-optimal experiments A Foster, M Jankowiak, M O’Meara, YW Teh, T Rainforth International Conference on Artificial Intelligence and Statistics, 2959-2969, 2020 | 46 | 2020 |
| Fast matrix square roots with applications to Gaussian processes and Bayesian optimization G Pleiss, M Jankowiak, D Eriksson, A Damle, J Gardner Advances in Neural Information Processing Systems 33, 22268-22281, 2020 | 38 | 2020 |
| Nearly supersymmetric dark atoms SR Behbahani, M Jankowiak, T Rube, JG Wacker Advances in High Energy Physics 2011, 1-34, 2011 | 38 | 2011 |
