Machine Learning

Automated reasoning and the applications of decision making


We pursue research on automated reasoning, adaptation, and the theories and applications of decision making and learning. Our research goals include learning from data and data mining. By building software that automatically learns from data, we design applications that have new functions and flexibility. Our research focuses on using statistical methods for the development of more advanced, intelligent computer systems.

 

Publications

Aditya V. Nori, Chung-Kil Hur, Sriram K. Rajamani, and Selva Samuel, R2: An Efficient MCMC Sampler for Probabilistic Programs, in AAAI Conference on Artificial Intelligence (AAAI), AAAI, July 2014

Emma Brunskill and Lihong Li, PAC-inspired Option Discovery in Lifelong Reinforcement Learning, in The 31st International Conference on Machine Learning (ICML 2014), June 2014

Alekh Agarwal, Daniel Hsu, Satyen Kale, John Langford, Lihong Li, and Robert E. Schapire, Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits, in The 31st International Conference on Machine Learning (ICML 2014), , June 2014

Shipra Agrawal and Nikhil R. Devanur, Bandits with concave rewards and convex knapsacks, in To appear in EC 2014, ACM conference on Economics and Computation, June 2014

Chung-Kil Hur, Aditya V. Nori, Sriram K. Rajamani, and Selva Samuel, Slicing Probabilistic Programs, in Programming Language Design and Implementation (PLDI), ACM, June 2014

More publications...