| Modeling tabular data using conditional gan L Xu, M Skoularidou, A Cuesta-Infante, K Veeramachaneni Advances in neural information processing systems 32, 2019 | 1661 | 2019 |
| The synthetic data vault N Patki, R Wedge, K Veeramachaneni 2016 IEEE international conference on data science and advanced analytics …, 2016 | 785 | 2016 |
| Opentuner: An extensible framework for program autotuning J Ansel, S Kamil, K Veeramachaneni, J Ragan-Kelley, J Bosboom, ... Proceedings of the 23rd international conference on Parallel architectures …, 2014 | 751 | 2014 |
| Fitness-distance-ratio based particle swarm optimization T Peram, K Veeramachaneni, CK Mohan Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No …, 2003 | 696 | 2003 |
| Deep feature synthesis: Towards automating data science endeavors JM Kanter, K Veeramachaneni 2015 IEEE international conference on data science and advanced analytics …, 2015 | 573 | 2015 |
| Synthesizing tabular data using generative adversarial networks L Xu, K Veeramachaneni arXiv preprint arXiv:1811.11264, 2018 | 393 | 2018 |
| Tadgan: Time series anomaly detection using generative adversarial networks A Geiger, D Liu, S Alnegheimish, A Cuesta-Infante, K Veeramachaneni 2020 ieee international conference on big data (big data), 33-43, 2020 | 386 | 2020 |
| AI^ 2: training a big data machine to defend K Veeramachaneni, I Arnaldo, V Korrapati, C Bassias, K Li 2016 IEEE 2nd international conference on big data security on cloud …, 2016 | 364 | 2016 |
| SteganoGAN: High capacity image steganography with GANs KA Zhang, A Cuesta-Infante, L Xu, K Veeramachaneni arXiv preprint arXiv:1901.03892, 2019 | 307 | 2019 |
| Automl to date and beyond: Challenges and opportunities SK Karmaker, MM Hassan, MJ Smith, L Xu, C Zhai, K Veeramachaneni ACM Computing Surveys (CSUR) 54 (8), 1-36, 2021 | 279 | 2021 |
| Optimization using particle swarms with near neighbor interactions K Veeramachaneni, T Peram, C Mohan, L Osadciw Genetic and Evolutionary Computation—GECCO 2003, 200-200, 2003 | 233 | 2003 |
| Distributed, multi-model, self-learning platform for machine learning WD Drevo, KK Veeramachaneni, U O'reilly US Patent App. 14/598,628, 2016 | 219 | 2016 |
| Likely to stop? predicting stopout in massive open online courses C Taylor, K Veeramachaneni, UM O'Reilly arXiv preprint arXiv:1408.3382, 2014 | 201 | 2014 |
| An adaptive multimodal biometric management algorithm K Veeramachaneni, LA Osadciw, PK Varshney IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and …, 2005 | 189 | 2005 |
| ATM: A distributed, collaborative, scalable system for automated machine learning T Swearingen, W Drevo, B Cyphers, A Cuesta-Infante, A Ross, ... 2017 IEEE international conference on big data (big data), 151-162, 2017 | 151 | 2017 |
| Autotuning algorithmic choice for input sensitivity Y Ding, J Ansel, K Veeramachaneni, X Shen, UM O’Reilly, ... ACM SIGPLAN Notices 50 (6), 379-390, 2015 | 148 | 2015 |
| Atmseer: Increasing transparency and controllability in automated machine learning Q Wang, Y Ming, Z Jin, Q Shen, D Liu, MJ Smith, K Veeramachaneni, ... Proceedings of the 2019 CHI conference on human factors in computing systems …, 2019 | 135 | 2019 |
| Transfer learning for predictive models in massive open online courses S Boyer, K Veeramachaneni Artificial Intelligence in Education: 17th International Conference, AIED …, 2015 | 129 | 2015 |
| Building predictive models via feature synthesis I Arnaldo, UM O'Reilly, K Veeramachaneni Proceedings of the 2015 annual conference on genetic and evolutionary …, 2015 | 106 | 2015 |
| Robust invisible video watermarking with attention KA Zhang, L Xu, A Cuesta-Infante, K Veeramachaneni arXiv preprint arXiv:1909.01285, 2019 | 104 | 2019 |
Alfredo Cuesta-InfanteUniversidad Rey Juan Carlosurjc.es의 이메일 확인됨
