| A collaborative kalman filter for time-evolving dyadic processes S Gultekin, J Paisley 2014 IEEE International Conference on Data Mining, 140-149, 2014 | 28 | 2014 |
| Noise enhanced hypothesis-testing according to restricted Neyman–Pearson criterion S Bayram, S Gultekin, S Gezici Digital Signal Processing 25, 17-27, 2014 | 22 | 2014 |
| Nonlinear Kalman filtering with divergence minimization S Gultekin, J Paisley IEEE Transactions on Signal Processing 65 (23), 6319-6331, 2017 | 19 | 2017 |
| Stochastic variational inference for the HDP-HMM A Zhang, S Gultekin, J Paisley Artificial Intelligence and Statistics, 800-808, 2016 | 19 | 2016 |
| Mba: Mini-batch auc optimization S Gultekin, A Saha, A Ratnaparkhi, J Paisley IEEE Transactions on Neural Networks and Learning Systems, 2020 | 8 | 2020 |
| Online forecasting matrix factorization S Gultekin, J Paisley IEEE Transactions on Signal Processing 67 (5), 1223-1236, 2018 | 4 | 2018 |
| Asymptotic Simulated Annealing for Variational Inference S Gultekin, A Zhang, J Paisley 2018 IEEE Global Communications Conference (GLOBECOM), 1-7, 2018 | 3 | 2018 |
| Noise enhanced detection in restricted Neyman-Pearson framework S Bayram, S Gultekin, S Gezici 2012 IEEE 13th International Workshop on Signal Processing Advances in …, 2012 | 3 | 2012 |
| Stochastic Annealing for Variational Inference S Gultekin, A Zhang, J Paisley arXiv preprint arXiv:1505.06723, 2015 | 2 | 2015 |
| Risk Bounds for Low Cost Bipartite Ranking S Gultekin, J Paisley Conference on Uncertainty in Artificial Intelligence (UAI), 2020 | | 2020 |
| Dynamic Machine Learning with Least Square Objectives S Gultekin Columbia University, 2019 | | 2019 |
| Probabilistic Canonical Tensor Decomposition for Predicting User Preference MR Rudolph, S Gultekin, J Paisley, SF Chang | | |
John PaisleyColumbia UniversityVerified email at columbia.edu
