Convergence rate of stochastic k-means
Cheng Tang, Claire Monteleoni,
A short version will appear in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
On Lloyd's algorithm: new theoretical insights for clustering in practice
Cheng Tang, Claire Monteleoni, in Proceedings of the 19th International Conference on Artificial Intelligence and Statistics (AISTATS), 2016
Seasonal Prediction using unsupervised feature learning and regression
Mahesh Mohan, Cheng Tang, Claire Monteleoni, Tim DeSole, Ben Cash,
in Proceedings of the 5th International Workshop on Climate Informatics, 2015
Detecting extreme events from climate time-series via topic modeling
Cheng Tang, Claire Monteleoni
In Proceedings of the 4th Climate Informatics Workshop, 2015
The convergence rate of stochastic k-means
Cheng Tang, Claire Monteleoni
ICML workshop on non-convex analysis and optimization, 2016
Scalable constant k-means approximation via heuristics on well-clusterable data
Cheng Tang, Claire Monteleoni
NIPS workshop on Learning faster with easy data II, 2015
On Lloyd's algorithm: new theoretical insights for clustering in practice
Cheng Tang, Claire Monteleoni
NIPS workshop on non-convex optimization for machine learning, 2015
Scaling up Lloyd's algorithm: stochastic and parallel block-wise optimization perspectives
Cheng Tang, Claire Monteleoni
7th NIPS workshop on optimization for machine learning (OPT2014), 2014
On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions
Cheng Tang, Claire Monteleoni
ROKS, 2013