| Iteration Complexity of Feasible Descent Methods for Convex Optimization PW Wang, CJ Lin The Journal of Machine Learning Research 15, 1523-1548, 2014 | 146 | 2014 |
| A linear ensemble of individual and blended models for music rating prediction PL Chen, CT Tsai, YN Chen, KC Chou, CL Li, CH Tsai, KW Wu, YC Chou, ... Proceedings of KDD Cup 2011, 21-60, 2012 | 63 | 2012 |
| Satnet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver PW Wang, P Donti, B Wilder, Z Kolter International Conference on Machine Learning, 6545-6554, 2019 | 56 | 2019 |
| On convergence rate of concave-convex procedure IEH Yen, N Peng, PW Wang, SD Lin Proceedings of the NIPS 2012 Optimization Workshop, 31-35, 2012 | 30 | 2012 |
| Novel models and ensemble techniques to discriminate favorite items from unrated ones for personalized music recommendation TG McKenzie, CS Ferng, YN Chen, CL Li, CH Tsai, KW Wu, YH Chang, ... Proceedings of KDD Cup 2011, 101-135, 2012 | 17 | 2012 |
| The mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints PW Wang, WC Chang, JZ Kolter arXiv preprint arXiv:1706.00476, 2017 | 11 | 2017 |
| The mixing method: coordinate descent for low-rank semidefinite programming PW Wang, WC Chang, JZ Kolter | 11 | 2017 |
| Realtime query completion via deep language models PW Wang, H Zhang, V Mohan, IS Dhillon, JZ Kolter eCOM@ SIGIR, 2018 | 10 | 2018 |
| Limited-memory common-directions method for distributed optimization and its application on empirical risk minimization CP Lee, PW Wang, W Chen, CJ Lin Proceedings of the 2017 SIAM International Conference on Data Mining, 732-740, 2017 | 10 | 2017 |
| Epigraph projections for fast general convex programming PW Wang, M Wytock, Z Kolter International Conference on Machine Learning, 2868-2877, 2016 | 7 | 2016 |
| Convex programming with fast proximal and linear operators M Wytock, PW Wang, JZ Kolter arXiv preprint arXiv:1511.04815, 2015 | 7 | 2015 |
| The Common-directions Method for Regularized Empirical Risk Minimization. PW Wang, C Lee, CJ Lin J. Mach. Learn. Res. 20, 58:1-58:49, 2019 | 6 | 2019 |
| The mixing method for maxcut-sdp problem PW Wang, JZ Kolter NIPS LHDS Workshop 4 (3), 4, 2016 | 6 | 2016 |
| Differentiable learning of numerical rules in knowledge graphs PW Wang, D Stepanova, C Domokos, JZ Kolter International Conference on Learning Representations, 2019 | 4 | 2019 |
| Low-rank semidefinite programming for the MAX2SAT problem PW Wang, JZ Kolter Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1641-1649, 2019 | 3 | 2019 |
| Polynomial optimization methods for matrix factorization PW Wang, CL Li, J Kolter Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 1 | 2017 |
| Method and system for learning rules from a data base C Domokos, D Stepanova, JZ Kolter, PW Wang US Patent App. 16/993,995, 2021 | | 2021 |
| Community detection using fast low-cardinality semidefinite programming PW Wang, JZ Kolter arXiv preprint arXiv:2012.02676, 2020 | | 2020 |
| Neural network with a layer solving a semidefinite program C Domokos, JZ Kolter, PW Wang, PL Donti US Patent App. 16/872,702, 2020 | | 2020 |
| Efficient semidefinite-programming-based inference for binary and multi-class MRFs C Pabbaraju, PW Wang, JZ Kolter Advances in Neural Information Processing Systems 33, 2020 | | 2020 |
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
