| Explaining how a deep neural network trained with end-to-end learning steers a car M Bojarski, P Yeres, A Choromanska, K Choromanski, B Firner, L Jackel, ... arXiv preprint arXiv:1704.07911, 2017 | 256 | 2017 |
| Orthogonal random features FXX Yu, AT Suresh, KM Choromanski, DN Holtmann-Rice, S Kumar Advances in neural information processing systems, 1975-1983, 2016 | 222* | 2016 |
| Scale-free graph with preferential attachment and evolving internal vertex structure K Choromański, M Matuszak, J Miȩkisz Journal of Statistical Physics 151 (6), 1175-1183, 2013 | 95 | 2013 |
| Structured evolution with compact architectures for scalable policy optimization K Choromanski, M Rowland, V Sindhwani, RE Turner, A Weller arXiv preprint arXiv:1804.02395, 2018 | 60 | 2018 |
| Quantization based fast inner product search R Guo, S Kumar, K Choromanski, D Simcha Artificial Intelligence and Statistics, 482-490, 2016 | 55 | 2016 |
| Visualbackprop: visualizing cnns for autonomous driving M Bojarski, A Choromanska, K Choromanski, B Firner, L Jackel, U Muller, ... arXiv preprint arXiv:1611.05418 2, 2016 | 48 | 2016 |
| Tournaments and colouring E Berger, K Choromanski, M Chudnovsky, J Fox, M Loebl, A Scott, ... Journal of Combinatorial Theory, Series B 103 (1), 1-20, 2013 | 45 | 2013 |
| Recycling randomness with structure for sublinear time kernel expansions K Choromanski, V Sindhwani arXiv preprint arXiv:1605.09049, 2016 | 37 | 2016 |
| On learning from label proportions FX Yu, K Choromanski, S Kumar, T Jebara, SF Chang arXiv preprint arXiv:1402.5902, 2014 | 34 | 2014 |
| Binary embeddings with structured hashed projections A Choromanska, K Choromanski, M Bojarski, T Jebara, S Kumar, ... International Conference on Machine Learning, 344-353, 2016 | 33 | 2016 |
| Visualbackprop: efficient visualization of cnns M Bojarski, A Choromanska, K Choromanski, B Firner, L Jackel, U Muller, ... arXiv preprint arXiv:1611.05418, 2016 | 32 | 2016 |
| The unreasonable effectiveness of structured random orthogonal embeddings KM Choromanski, M Rowland, A Weller Advances in neural information processing systems, 219-228, 2017 | 30 | 2017 |
| Structured adaptive and random spinners for fast machine learning computations M Bojarski, A Choromanska, K Choromanski, F Fagan, C Gouy-Pailler, ... Artificial Intelligence and Statistics, 1020-1029, 2017 | 29 | 2017 |
| Es-maml: Simple hessian-free meta learning X Song, W Gao, Y Yang, K Choromanski, A Pacchiano, Y Tang arXiv preprint arXiv:1910.01215, 2019 | 23 | 2019 |
| Forcing large transitive subtournaments E Berger, K Choromanski, M Chudnovsky Journal of Combinatorial Theory, Series B 112, 1-17, 2015 | 22 | 2015 |
| Differentially-and non-differentially-private random decision trees M Bojarski, A Choromanska, K Choromanski, Y LeCun arXiv preprint arXiv:1410.6973, 2014 | 21 | 2014 |
| Adaptive Anonymity via -Matching KM Choromanski, T Jebara, K Tang Advances in Neural Information Processing Systems 26, 3192-3200, 2013 | 21 | 2013 |
| Method of transmitting packet data in a communication system CW You, JH Ahn, SH Yoon, YJ Lee US Patent 7,551,639, 2009 | 19* | 2009 |
| A theoretical and empirical comparison of gradient approximations in derivative-free optimization AS Berahas, L Cao, K Choromanski, K Scheinberg arXiv preprint arXiv:1905.01332, 2019 | 18 | 2019 |
| From complexity to simplicity: Adaptive es-active subspaces for blackbox optimization KM Choromanski, A Pacchiano, J Parker-Holder, Y Tang, V Sindhwani Advances in Neural Information Processing Systems, 10299-10309, 2019 | 17 | 2019 |
