Algorithms and Limits in Statistical Inference

Date: 10:00am, April 18, 2016
Location: Costa Commons, CEPSR 750
Speaker:  Jayadev Acharya is a postdoctoral researcher in the EECS department at MIT, hosted by Piotr Indyk. 

Abstract:   The massive explosion of data and the demand for fast inference has resulted in great interest and progress in various aspects of data science.I will discuss some problems at the heart of statistics and machine learning, such as hypothesis testing, distribution learning, and property estimation. I will contrast the classical themes and modernconsiderations in addressing these questions, drawing tools frominformation theory, computer science, machine learning, and statistics. I will describe the problem of distribution estimation in greater detail, including a general framework that yields computationally efficient, and statistically optimal algorithms for estimating a wide range of probability density functions.

Biography:  Jayadev Acharya is a postdoctoral researcher in the EECS department at MIT, hosted by Piotr Indyk. His current research interests are in algorithmic statistics, machine learning, and information theory. He received his Ph.D in ECE from UC San Diego, where he was advised by Alon Orlitsky. He is a recipient of the Jack Wolf student paper award at the IEEE International Symposium on Information Theory, the Shannon Graduate Fellowship from UC San Diego, and an MIT-Energy Initiative fellowship.

Hosted by Professor John Wright 


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