Ping Li graduated with his PhD in statistics from Stanford University, where he also earned two master’s degrees in computer science and electrical engineering. Prior to Stanford, Mr. Li completed graduate degrees from the University of Washington (Seattle). He received the Young Investigator Award from the Office of Naval Research (ONR-YIP) and the Young Investigator Award from the Air Force Office of Scientific Research (AFOSR-YIP). Mr. Li also received the NIPS 2014 Best Paper Award, the ASONAM 2014 Best Paper Award, and the KDD 2006 Best Student Paper Award. Mr. Li’s research interests include statistical machine learning, information retrieval, randomized algorithms, compressed sensing, knowledge and reasoning, and NLP, among other topics. Dr. Ping Li has supervised more than 10 PhD students and postdoctoral researchers, most of whom have become university faculty members.
Logician: A Unified End-to-End Neural Approach for Open-Domain Information ExtractionMingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li
In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them.Natural Language and Speech
Collaborative Filtering via Additive Ordinal RegressionJun Hu, Ping Li
Accurately predicting user preferences/ratings over items are crucial for many Internet applications, e.g., recommender systems, online advertising. In current main-stream algorithms regarding the rating prediction problem, discrete rating scores are often viewed as either numerical values or(nominal) categorical labels.Data Science and Data Mining
Simple strategies for recovering inner products from coarsely quantized random projectionsPing Li, Martin Slawski
Random projections have been increasingly adopted for a diverse set of tasks in machine learning involving dimensionality reduction. One specific line of research on this topic has investigated the use of quantization subsequent to projection with the aim of additional data compression.Data Science and Data Mining