Dimitris Politis presents “Model Free Prediction and Regression”

Headshot of Dimitris Politis

Headshot of Dimitris PolitisPrediction has been traditionally approached via a model-based paradigm, i.e., (a) fit a model to the data at hand, and (b) use the fitted model in order to extrapolate/predict future data. Due to both mathematical and computational constraints, 20th century statistical practice focused mostly on parametric models. Fortunately, with the advent of widely accessible powerful computing in the late 1970s, computer-intensive methods such as the bootstrap and cross-validation have freed practitioners from the limitations of parametric models, and paved the way towards the ‘big data’ era of the 21st century. Nonetheless, there is a further step one may take, namely going beyond even nonparametric models. The Model-Free Prediction Principle is based on the simple notion of transforming a complex dataset to one that is easier to work with, e.g., i.i.d. or Gaussian. As such, it restores the emphasis on observable quantities, i.e., current and future data, as opposed to unobservable model parameters and estimates thereof. Coupled with resampling, the Model-Free Prediction Principle allows us to go beyond point prediction in order to construct frequentist prediction intervals without resort to unrealistic assumptions such as normality or linearity.

Time:  5pm Pacific Time (San Diego), Wednesday October 21, 2020  (which is equivalent to 11AM on Oct 22 in Australia: Canberra, Melbourne, Sydney)

Jingbo Shang wins ACM SIGKDD Award

Man in black suit in front of a pure white background

Jingbo Shang, Runner-Up ACM SIGKDD Dissertation Award

Man in black suit in front of a pure white backgroundHDSI faculty member Jingbo Shang, Assistant Professor of Computer Science at University of California at San Diego, earned the runner-up ACM SIGKDD Dissertation Award for his thesis, “Constructing and Mining Heterogeneous Information Networks From Massive Text.”

The ACM SIGKDD Dissertation Award recognizes outstanding work done by graduate students in the areas of data science, machine learning and data mining.  “Since the inception of the conference 26 years ago, research conducted by the SIGKDD community and presented at KDD conferences has made a lasting impact in academia and industry and the lives of billions of global citizens,” said Dr. Jian Pei, chair of ACM SIGKDD and professor of Computing Science at Simon Fraser University. “The outstanding scientists honored today are recognized not only for their advancements in a specialized field but for their significant contributions to the world.”