Events Calendar

CS Colloquium: Haipeng Luo (Princeton) -Optimal and Adaptive Online Learning

Speaker: Haipeng Luo , Princeton

Talk Title: Optimal and Adaptive Online Learning

Series: CS Colloquium

Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium

Online learning is one of the most important and well-established learning models in machine learning. Generally speaking, the goal of online learning is to make a sequence of accurate predictions "on the fly" when interacting with the environment. Online learning has been extensively studied in recent years, and has also become of great interest to practitioners due to its applicability to large scale applications such as advertisement placement and recommendation systems.

In this talk, I will present novel, optimal and adaptive online learning algorithms for three problems. The first problem is online boosting, a theory of boosting the accuracy of any existing online learning algorithms; the second problem is on combining expert advice more efficiently and adaptively when making online predictions; the last part of the talk is about using data sketching techniques to obtain efficient online learning algorithms that make use of second order information and have robust performance against ill-conditioned data.

Biography: Haipeng Luo is currently a fifth year graduate student working with Prof. Rob Schapire at Princeton. His main research interest is in theoretical and applied machine learning, with a focus on adaptive and robust online learning and its connections to boosting, optimization, stochastic learning and game theory. He won the Wu Prize for Excellence and two best paper awards (ICML and NIPS) in 2015.

Host: CS Department

Location: Olin Hall of Engineering (OHE) - 136

Audiences: Everyone Is Invited

Contact: Assistant to CS chair