| REDD: A public data set for energy disaggregation research JZ Kolter, MJ Johnson Workshop on data mining applications in sustainability (SIGKDD), San Diego …, 2011 | 1134 | 2011 |
| Dynamic weighted majority: An ensemble method for drifting concepts JZ Kolter, MA Maloof Journal of Machine Learning Research 8 (Dec), 2755-2790, 2007 | 1046 | 2007 |
| An empirical evaluation of generic convolutional and recurrent networks for sequence modeling S Bai, JZ Kolter, V Koltun arXiv preprint arXiv:1803.01271, 2018 | 1024 | 2018 |
| Towards fully autonomous driving: Systems and algorithms J Levinson, J Askeland, J Becker, J Dolson, D Held, S Kammel, JZ Kolter, ... 2011 IEEE Intelligent Vehicles Symposium (IV), 163-168, 2011 | 968 | 2011 |
| Learning to detect and classify malicious executables in the wild JZ Kolter, MA Maloof Journal of Machine Learning Research 7 (Dec), 2721-2744, 2006 | 701 | 2006 |
| Provable defenses against adversarial examples via the convex outer adversarial polytope E Wong, Z Kolter International Conference on Machine Learning, 5286-5295, 2018 | 639* | 2018 |
| Learning to detect malicious executables in the wild JZ Kolter, MA Maloof Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 587 | 2004 |
| Approximate inference in additive factorial hmms with application to energy disaggregation JZ Kolter, T Jaakkola Artificial intelligence and statistics, 1472-1482, 2012 | 585 | 2012 |
| Energy disaggregation via discriminative sparse coding J Kolter, S Batra, A Ng Advances in neural information processing systems 23, 1153-1161, 2010 | 369 | 2010 |
| Certified adversarial robustness via randomized smoothing JM Cohen, E Rosenfeld, JZ Kolter arXiv preprint arXiv:1902.02918, 2019 | 362 | 2019 |
| Using additive expert ensembles to cope with concept drift JZ Kolter, MA Maloof Proceedings of the 22nd international conference on Machine learning, 449-456, 2005 | 278 | 2005 |
| Near-Bayesian exploration in polynomial time JZ Kolter, AY Ng Proceedings of the 26th annual international conference on machine learning …, 2009 | 248 | 2009 |
| Optnet: Differentiable optimization as a layer in neural networks B Amos, JZ Kolter arXiv preprint arXiv:1703.00443, 2017 | 247 | 2017 |
| Regularization and feature selection in least-squares temporal difference learning JZ Kolter, AY Ng Proceedings of the 26th annual international conference on machine learning …, 2009 | 233 | 2009 |
| Scaling provable adversarial defenses E Wong, F Schmidt, JH Metzen, JZ Kolter Advances in Neural Information Processing Systems, 8400-8409, 2018 | 218 | 2018 |
| A control architecture for quadruped locomotion over rough terrain JZ Kolter, MP Rodgers, AY Ng 2008 IEEE International Conference on Robotics and Automation, 811-818, 2008 | 202 | 2008 |
| Gradient descent GAN optimization is locally stable V Nagarajan, JZ Kolter Advances in neural information processing systems, 5585-5595, 2017 | 197 | 2017 |
| Hierarchical apprenticeship learning with application to quadruped locomotion J Kolter, P Abbeel, A Ng Advances in Neural Information Processing Systems 20, 769-776, 2007 | 165 | 2007 |
| A large-scale study on predicting and contextualizing building energy usage J Kolter, J Ferreira Proceedings of the AAAI Conference on Artificial Intelligence 25 (1), 2011 | 125 | 2011 |
| End-to-end differentiable physics for learning and control F de Avila Belbute-Peres, K Smith, K Allen, J Tenenbaum, JZ Kolter Advances in neural information processing systems, 7178-7189, 2018 | 124 | 2018 |
