When do random forests fail? C Tang, D Garreau, U von Luxburg Advances in neural information processing systems 31, 2018 | 120 | 2018 |
Exponentially convergent stochastic k-PCA without variance reduction C Tang Advances in Neural Information Processing Systems 32, 2019 | 36 | 2019 |
On Lloyd’s algorithm: new theoretical insights for clustering in practice C Tang, C Monteleoni Artificial Intelligence and Statistics, 1280-1289, 2016 | 31 | 2016 |
Convergence rate of stochastic k-means C Tang, C Monteleoni Artificial Intelligence and Statistics, 1495-1503, 2017 | 30 | 2017 |
Can topic modeling shed light on climate extremes? C Tang, C Monteleoni Computing in Science & Engineering 17 (6), 43-52, 2015 | 6 | 2015 |
On the convergence rate of stochastic gradient descent for strongly convex functions C Tang, C Monteleoni Regularization, optimization, kernels, and support vector machines, 159-175, 2015 | 4 | 2015 |
Detecting extreme events from climate time series via topic modeling C Tang, C Monteleoni Machine Learning and Data Mining Approaches to Climate Science: Proceedings …, 2015 | 4 | 2015 |
Neural document expansion for ad-hoc information retrieval C Tang, A Arnold arXiv preprint arXiv:2012.14005, 2020 | 3 | 2020 |
Demystifying overcomplete nonlinear auto-encoders: fast SGD convergence towards sparse representation from random initialization C Tang, C Monteleoni | 3 | 2018 |
Convergence analysis of stochastic gradient descent on strongly convex objective functions C Tang, C Monteleoni Proceedings of ROKS, 111-112, 2013 | 1 | 2013 |
Scalable constant k-means approximation via heuristics on well-clusterable data C Tang, C Monteleoni Poster Session of Learning faster from easy data II conference, Montreal, Canada, 0 | 1 | |
On the tightness of linear relaxation based robustness certification methods C Tang arXiv preprint arXiv:2210.00178, 2022 | | 2022 |
Transforming Machine Learning Heuristics into Provable Algorithms: Classical, Stochastic, and Neural C Tang The George Washington University, 2018 | | 2018 |
HOW FAR CAN WE EXPLOIT THE STRUCTURAL RICHNESS OF CLIMATE DATA?—A CASE STUDY M Mohan, C Tang, C Monteleoni, T DelSole, B Cash | | |
Scaling up Lloyd’s algorithm: stochastic and parallel block-wise optimization perspectives C Tang, C Monteleoni | | |
Seasonal prediction using unsupervised feature learning and regression M Mohan, C Tang, C Monteleoni, T DelSole, B Cash | | |