Fitting a model to a collection of observations is one of the quintessential problems in machine learning. Since any model is only approximately valid, an estimator that is useful in practice must also be robust in the presence of model misspecification. It turns out that there is a striking tension between robustness and computational efficiency. Even for the most basic high-dimensional tasks, such as robustly computing the mean and covariance, until recently the only known estimators were either hard to compute or could only tolerate a negligible fraction of errors.
In this talk, I will survey the recent progress in algorithmic high-dimensional robust statistics. I will describe the first robust and efficiently computable estimators for several fundamental statistical tasks that were previously thought to be computationally intractable. These include robust estimation of mean and covariance in high dimensions, and robust learning of various latent variable models. The new robust estimators are scalable in practice and yield a number of applications in exploratory data analysis.
The Information Theory Forum (IT-Forum) at Stanford ISL is an interdisciplinary academic forum which focuses on mathematical aspects of information processing. With a primary emphasis on information theory, we also welcome researchers from signal processing, learning and statistical inference, control and optimization to deliver talks at our forum. We also warmly welcome industrial affiliates in the above fields. The forum is typically held in Packard 202 every Friday at 1:15 pm during the academic year.
The Information Theory Forum is organized by graduate students Jiantao Jiao and Yanjun Han. To suggest speakers, please contact any of the students.
Ilias Diakonikolas is an Assistant Professor and Andrew and Erna Viterbi Early Career Chair in the Department of Computer Science at USC. He obtained a Diploma in electrical and computer engineering from the National Technical University of Athens and a Ph.D. in computer science from Columbia University where he was advised by Mihalis Yannakakis. Before moving to USC, he was a faculty member at the University of Edinburgh, and prior to that he was the Simons postdoctoral fellow in theoretical computer science at the University of California, Berkeley. His research is on the algorithmic foundations of massive data sets, in particular on designing efficient algorithms for fundamental problems in machine learning. He is a recipient of a Sloan Fellowship, an NSF CAREER Award, a Google Faculty Research Award, a Marie Curie Fellowship, the IBM Research Pat Goldberg Best Paper Award, and an honorable mention in the George Nicholson competition from the INFORMS society.