Statistical Consistency and Regret Bounds for Ranking

  • Shivani Agarwal | Indian Institute of Science

Ranking problems arise in an increasing number of applications, including for example information retrieval, recommendation systems, computational biology, drug discovery, and a variety of industrial prioritization tasks.
In recent years, there has been much interest in developing machine learning algorithms for ranking problems, and in understanding the statistical properties of such algorithms. This talk will start with a brief overview of some recent results on the statistical consistency properties of ranking algorithms. The second part of the talk will then present some new results on regret bounds for a popular setting of ranking known as “bipartite” ranking.

Speaker Details

Shivani Agarwal is an Assistant Professor in the Department of Computer Science and Automation at the Indian Institute of Science. Prior to this she was a postdoctoral lecturer and associate in the Computer Science and Artificial Intelligence Laboratory at MIT. She obtained her PhD in Computer Science at the University of Illinois, Urbana-Champaign, where she received the Liu Award for her research; an MA in Computer Science at Trinity College, University of Cambridge, where she was a Nehru Scholar; and a BSc with Honors in Mathematics at St Stephen’s College, University of Delhi. Her research interests include machine learning and learning theory, in particular the study of ranking and other new learning problems, as well as applications of machine learning methods, particularly in the life sciences. More broadly, she is excited by research at the intersection of computer science, mathematics, and statistics, and its applications in scientific discovery.

Watch Next