| Iteration Complexity of Feasible Descent Methods for Convex Optimization PW Wang, CJ Lin The Journal of Machine Learning Research 15, 1523-1548, 2014 | 156 | 2014 |
| 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 | 109 | 2019 |
| 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 | 62 | 2012 |
| 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 | 32 | 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 | 16 | 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 | 13 | 2017 |
| Differentiable learning of numerical rules in knowledge graphs PW Wang, D Stepanova, C Domokos, JZ Kolter International Conference on Learning Representations, 2019 | 12 | 2019 |
| Realtime query completion via deep language models PW Wang, H Zhang, V Mohan, IS Dhillon, JZ Kolter eCOM@ SIGIR, 2018 | 12 | 2018 |
| The mixing method: coordinate descent for low-rank semidefinite programming PW Wang, WC Chang, JZ Kolter | 12 | 2017 |
| 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 | 11 | 2017 |
| Epigraph projections for fast general convex programming PW Wang, M Wytock, Z Kolter International Conference on Machine Learning, 2868-2877, 2016 | 8 | 2016 |
| Convex programming with fast proximal and linear operators M Wytock, PW Wang, JZ Kolter arXiv preprint arXiv:1511.04815, 2015 | 8 | 2015 |
| 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 | 7 | 2019 |
| The common-directions method for regularized empirical risk minimization PW Wang, C Lee, CJ Lin The Journal of Machine Learning Research 20 (1), 2072-2120, 2019 | 6 | 2019 |
| The mixing method for maxcut-sdp problem PW Wang, JZ Kolter NIPS LHDS Workshop 4 (3), 4, 2016 | 6 | 2016 |
| 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 | 2 | 2020 |
| Community detection using fast low-cardinality semidefinite programming PW Wang, JZ Kolter arXiv preprint arXiv:2012.02676, 2020 | 1 | 2020 |
| 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 |
| Neural query auto-correction and completion I Dhillon, V Mohan, PW Wang US Patent 11,106,690, 2021 | | 2021 |
| Learning and Reasoning with Fast Semidefinite Programming and Mixing Methods PW Wang Robert Bosch GmbH, 2021 | | 2021 |
Zico KolterCarnegie Mellon UniversityVerified email at cs.cmu.edu
