We present the design and development of a data stream system that captures data uncertainty from data collection to query processing to final result generation. Our system focuse...
Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Char...
Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an i...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Determinantal point processes (DPPs), which arise in random matrix theory and quantum physics, are natural models for subset selection problems where diversity is preferred. Among...
This paper investigates fault tolerance issues in Bistro, a wide area upload architecture. In Bistro, clients first upload their data to intermediaries, known as bistros. A destin...