ABSTRACT With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are typically unknown or difficult to characterize. Finding a good network control policy is of significant importance to achieving desirable network performance (e.g., high throughput or low average job delay). Online/sequential learning algorithms are well-suited…

ABSTRACT Tianyi will discuss the work about how to achieve the optimal detection rate for detecting anomalies in a low-rank matrix. The concrete application we are studying is a crucial inventory management problem ('phantom inventory') that by some measures costs retailers approximately 4% in annual sales. We observe that this problem can be modeled as…

ABSTRACT The design of data markets has gained in importance as firms increasingly use predictions from machine learning models to make their operations more effective, yet need to externally acquire the necessary training data to fit such models. This is particularly true in the context of the Internet where an ever-increasing amount of user data…

ABSTRACT While the identification of nonlinear dynamical systems is a fundamental building block of model-based reinforcement learning and feedback control, its sample complexity is only understood for systems that either have discrete states and actions or for systems that can be identified from data generated by i.i.d. random inputs. Nonetheless, many interesting dynamical systems have…