Data Science Seminar
Game Theoretic Statistics
Arun Sai Suggala
Game Theoretic Statistics
| When | Friday, April 16, 2021, 2:00 PM – 3:00 PM (MT) |
|---|---|
| Where | Virtual |
Abstract
Game theory and statistics are often regarded as disparate research areas. This is because typical statistical estimation settings are non-adversarial, and the samples are assumed to be generated by some stationary non-reactive source. However, there is a great degree of commonality between the two fields. Classically, the mathematical philosophy of statistics, particularly frequentist statistics, posits that the source of samples is potentially adversarial. This resulted in the rich theory of minimax statistical games and estimation. Boosting algorithms, which are often regarded as best off-the-shelf classifiers, can be viewed as playing a zero-sum game against a weak learner. To allow for various departures of test environment'' fromtrain environments’’, the emerging field of robust machine learning allows for adversarial manipulation of the train or test environments. Finally, an emerging class of density estimators (GANs) in modern machine learning use an adversarial ``critic’’ of the density estimator to improve the final density estimation. The common theme among these classical and modern developments is an interplay between statistical estimation and two player games.
In this talk, I will present some of my recent work at the intersection of statistics and game theory and show how game theory can help statistics. In particular, I will present my work on minimax statistical estimation, where we develop algorithmic techniques for constructing minimax estimators. Our algorithms rely on tools from online nonconvex learning and help us construct minimax estimators for fundamental estimation problems such as covariance estimation and entropy estimation.
Speaker
Arun Sai Suggala
CMU
Arun Sai Suggala is a final year Machine Learning PhD student at Carnegie Mellon University (CMU), advised by Pradeep Ravikumar. Arun is broadly interested in online learning, game theory, and their applications to machine learning and statistics. He is particularly interested in designing new algorithmic and analytic tools in game theory for solving statistical and machine learning problems. His work has received the best student paper award at ALT’20. Prior to CMU, Arun completed his undergraduate studies in Computer Science and Engineering in the Indian Institute of Technology, Bombay.
Tags: algorithms & theory machine learning optimization statistics
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