| Almost optimal exploration in multi-armed bandits Z Karnin, T Koren, O Somekh International conference on machine learning, 1238-1246, 2013 | 615 | 2013 |
| Tabtransformer: Tabular data modeling using contextual embeddings X Huang, A Khetan, M Cvitkovic, Z Karnin arXiv preprint arXiv:2012.06678, 2020 | 524 | 2020 |
| Reducing dueling bandits to cardinal bandits N Ailon, Z Karnin, T Joachims International Conference on Machine Learning, 856-864, 2014 | 150 | 2014 |
| Elastic machine learning algorithms in amazon sagemaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 146 | 2020 |
| Optimal quantile approximation in streams Z Karnin, K Lang, E Liberty 2016 ieee 57th annual symposium on foundations of computer science (focs), 71-78, 2016 | 133 | 2016 |
| Distributed exploration in multi-armed bandits E Hillel, ZS Karnin, T Koren, R Lempel, O Somekh Advances in Neural Information Processing Systems 26, 2013 | 123 | 2013 |
| Copeland dueling bandits M Zoghi, ZS Karnin, S Whiteson, M De Rijke Advances in neural information processing systems 28, 2015 | 113 | 2015 |
| Black box polynomial identity testing of generalized depth-3 arithmetic circuits with bounded top fan-in ZS Karnin, A Shpilka 2008 23rd Annual IEEE Conference on Computational Complexity, 280-291, 2008 | 105 | 2008 |
| Amazon SageMaker Autopilot: a white box AutoML solution at scale P Das, N Ivkin, T Bansal, L Rouesnel, P Gautier, Z Karnin, L Dirac, ... Proceedings of the fourth international workshop on data management for end …, 2020 | 100 | 2020 |
| Online principal components analysis C Boutsidis, D Garber, Z Karnin, E Liberty Proceedings of the twenty-sixth annual ACM-SIAM symposium on Discrete …, 2014 | 89 | 2014 |
| How many folders do you really need? classifying email into a handful of categories M Grbovic, G Halawi, Z Karnin, Y Maarek Proceedings of the 23rd ACM International Conference on Conference on …, 2014 | 77 | 2014 |
| An empirical process approach to the union bound: Practical algorithms for combinatorial and linear bandits J Katz-Samuels, L Jain, KG Jamieson Advances in Neural Information Processing Systems 33, 10371-10382, 2020 | 73 | 2020 |
| Multi-armed bandits: Competing with optimal sequences ZS Karnin, O Anava Advances in Neural Information Processing Systems 29, 2016 | 68 | 2016 |
| Deterministic identity testing of depth-4 multilinear circuits with bounded top fan-in ZS Karnin, P Mukhopadhyay, A Shpilka, I Volkovich Proceedings of the forty-second ACM symposium on Theory of computing, 649-658, 2010 | 63 | 2010 |
| Optimal dynamic distributed MIS K Censor-Hillel, E Haramaty, Z Karnin Proceedings of the 2016 ACM Symposium on Principles of Distributed Computing …, 2016 | 59 | 2016 |
| You've got mail, and here is what you could do with it! analyzing and predicting actions on email messages D Di Castro, Z Karnin, L Lewin-Eytan, Y Maarek Proceedings of the ninth acm international conference on web search and data …, 2016 | 59 | 2016 |
| Dynamic accuracy-based deployment and monitoring of machine learning models in provider networks TA FAULHABER, E Liberty, S Stefani, Z Karnin, C Wiley, SA Loeppky, ... US Patent 11,257,002, 2022 | 57 | 2022 |
| Discrepancy, coresets, and sketches in machine learning Z Karnin, E Liberty Conference on Learning Theory, 1975-1993, 2019 | 56 | 2019 |
| Reconstruction of generalized depth-3 arithmetic circuits with bounded top fan-in ZS Karnin, A Shpilka 2009 24th Annual IEEE Conference on Computational Complexity, 274-285, 2009 | 55 | 2009 |
| Volumetric spanners: an efficient exploration basis for learning E Hazan, Z Karnin The Journal of Machine Learning Research 17 (1), 4062-4095, 2016 | 52 | 2016 |
