| An empirical evaluation of generic convolutional and recurrent networks for sequence modeling S Bai, JZ Kolter, V Koltun arXiv preprint arXiv:1803.01271, 2018 | 6456 | 2018 |
| Certified adversarial robustness via randomized smoothing J Cohen, E Rosenfeld, Z Kolter international conference on machine learning, 1310-1320, 2019 | 2250 | 2019 |
| REDD: A public data set for energy disaggregation research JZ Kolter, MJ Johnson Workshop on data mining applications in sustainability (SIGKDD), San Diego …, 2011 | 1887 | 2011 |
| 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 | 1820 | 2011 |
| Provable defenses against adversarial examples via the convex outer adversarial polytope E Wong, Z Kolter International conference on machine learning, 5286-5295, 2018 | 1741 | 2018 |
| Multimodal transformer for unaligned multimodal language sequences YHH Tsai, S Bai, PP Liang, JZ Kolter, LP Morency, R Salakhutdinov Proceedings of the conference. Association for computational linguistics …, 2019 | 1515 | 2019 |
| Fast is better than free: Revisiting adversarial training E Wong, L Rice, JZ Kolter arXiv preprint arXiv:2001.03994, 2020 | 1368 | 2020 |
| Dynamic weighted majority: An ensemble method for drifting concepts JZ Kolter, MA Maloof The Journal of Machine Learning Research 8, 2755-2790, 2007 | 1367 | 2007 |
| Optnet: Differentiable optimization as a layer in neural networks B Amos, JZ Kolter International conference on machine learning, 136-145, 2017 | 1105 | 2017 |
| Overfitting in adversarially robust deep learning L Rice, E Wong, Z Kolter International conference on machine learning, 8093-8104, 2020 | 964 | 2020 |
| Learning to detect and classify malicious executables in the wild. JZ Kolter, MA Maloof Journal of Machine Learning Research 7 (12), 2006 | 926 | 2006 |
| Universal and transferable adversarial attacks on aligned language models A Zou, Z Wang, N Carlini, M Nasr, JZ Kolter, M Fredrikson arXiv preprint arXiv:2307.15043, 2023 | 912 | 2023 |
| Approximate inference in additive factorial hmms with application to energy disaggregation JZ Kolter, T Jaakkola Artificial intelligence and statistics, 1472-1482, 2012 | 867 | 2012 |
| Deep equilibrium models S Bai, JZ Kolter, V Koltun Advances in neural information processing systems 32, 2019 | 754 | 2019 |
| Learning to detect malicious executables in the wild JZ Kolter, MA Maloof Proceedings of the tenth ACM SIGKDD international conference on Knowledge …, 2004 | 742 | 2004 |
| Differentiable convex optimization layers A Agrawal, B Amos, S Barratt, S Boyd, S Diamond, JZ Kolter Advances in neural information processing systems 32, 2019 | 719 | 2019 |
| Input convex neural networks B Amos, L Xu, JZ Kolter International conference on machine learning, 146-155, 2017 | 718 | 2017 |
| Energy disaggregation via discriminative sparse coding J Kolter, S Batra, A Ng Advances in neural information processing systems 23, 2010 | 503 | 2010 |
| Patches are all you need? A Trockman, JZ Kolter arXiv preprint arXiv:2201.09792, 2022 | 488 | 2022 |
| Scaling provable adversarial defenses E Wong, F Schmidt, JH Metzen, JZ Kolter Advances in Neural Information Processing Systems 31, 2018 | 485 | 2018 |
