Machine Learning NYC
New! Apply now for postdoctoral research position in Machine Learning at MSR-NYC.
Research of the Machine Learning group at MSR-NYC spans a wide variety of topics within theoretical and applied machine learning, including learning from interactive data (e.g., contextual bandits), large-scale machine learning, and convex optimization.
Publications
- Alina Beygelzimer, John Langford, and David Pennock, Learning performance of prediction markets with Kelly bettors, International Conference on Autonomous Agents and Multiagent Systems, 2012
- Alekh Agarwal, Miroslav Dudik, Satyen Kale, John Langford, and Robert E. Schapire, Contextual bandit learning with predictable rewards, in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012
- Jacob Abernethy, Yiling Chen, and Jennifer Wortman Vaughan, Efficient Market Making via Convex Optimization, and a Connection to Online Learning, in ACM Transactions on Economics and Computation (To appear), 2012
- Miroslav Dudik, Zaid Harchaoui, and Jerome Malick, Lifted coordinate descent for learning with trace-norm regularization, in Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012
- Zaid Harchaoui, Matthijs Douze, Mattis Paulin, Miroslav Dudik, and Jerome Malick, Large-scale image classification with trace-norm regularization, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR-12), 2012
- Miroslav Dudík, Dumitru Erhan, John Langford, and Lihong Li, Sample-efficient Nonstationary-policy Evaluation for Contextual Bandits, in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI-12), 2012
- Miroslav Dudik, Sebastien Lahaie, and David Pennock, A Tractable Combinatorial Market Maker Using Constraint Generation, in ACM Conference on Electronic Commerce, 2012
- Lihong Li, Wei Chu, John Langford, and Xuanhui Wang, Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms, in Proceedings of the Fourth International Conference on Web Search and Web Data Mining (WSDM-11), 2011
- Alexander L. Strehl, John Langford, Lihong Li, and Sham M. Kakade, Learning from Logged Implicit Exploration Data, in Advances in Neural Information Processing Systems 23 (NIPS-10), 2011
- Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, and Robert E. Schapire, Contextual Bandit Algorithms with Supervised Learning Guarantees, in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011
