In the past several years, there has been a revival of interest in the field of distribution testing, with a flurry of results showing how to test and estimate properties with a sample complexity which is sublinear in the domain size. Indeed the number of recent works may even feel overwhelming to the casual observer, and the literature hard to navigate. The goal of this workshop is to catch the community up in recent developments, and highlight some of the most interesting frontiers in distribution testing.

Open Problems

The workshop included an Open Problems session (see the schedule below), which resulted in a list of 12 open questions and directions to tackle and explore (available here).

Schedule (Tentative)


Clément Canonne
Clément Canonne will/may be graduating from Columbia University in September 2017, where his advisor is Rocco Servedio. His research focuses on the fields of property testing and sublinear algorithms; specifically, on understanding the strengths and limitations of the standard models in property and distribution testing, as well as in related areas. He also really likes elephants.
Constantinos Daskalakis
Constantinos Daskalakis is an associate professor of computer science and electrical engineering at MIT. He holds a diploma in electrical and computer engineering from the National Technical University of Athens, and a Ph.D. in electrical engineering and computer sciences from UC-Berkeley. His research interests lie in theoretical computer science and its interface with economics, probability, learning and statistics. He has been honored with the 2007 Microsoft Graduate Research Fellowship, the 2008 ACM Doctoral Dissertation Award, the Game Theory and Computer Science Prize from the Game Theory Society, the 2010 Sloan Fellowship in Computer Science, the 2011 SIAM Outstanding Paper Prize, the 2011 Ruth and Joel Spira Award for Distinguished Teaching, the 2012 Microsoft Research Faculty Fellowship, and the 2015 Research and Development Award by the Vatican Giuseppe Sciacca Foundation. He is also a recipient of Best Paper awards at the ACM Conference on Economics and Computation in 2006 and in 2013.
Ilias Diakonikolas
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.
Tom Gur
Tom Gur is a postdoc in the EECS department at UC Berkeley, where he is hosted by Alessandro Chiesa. He received his Ph.D. from the Weizmann Institute of Science, where his advisor was Oded Goldreich. His research focuses on property testing, sublinear algorithms, probabilistic proof systems, and coding theory; he also likes to study all elements of the power set of these fields.
Gautam Kamath
Gautam Kamath is a final-year graduate student at MIT, advised by Constantinos Daskalakis. His research focuses broadly on theoretical machine learning and statistics, and more specifically on distribution learning, testing, and applied probability. He also likes elephants, but prefers pandas.
Jiantao Jiao
Jiantao Jiao is a final-year graduate student at Stanford University, advised by Tsachy Weissman. His research focuses on high-dimensional statistics, theoretical machine learning, information theory, and applied probability. More specifically, he is intrigued by the fundamental limits of data analysis, and the questions of how to identify, compute, estimate, and test those limits in practical applications with the least amount of samples and computational efforts.
Ryan O'Donnell
Ryan O'Donnell is a Professor of Computer Science at Carnegie Mellon University. He received his Ph.D. from MIT. Before joining CMU, he was a postdoc at IAS and Microsoft Research. His research interests include complexity theory, approximation algorithms, analysis of Boolean functions, learning theory, property testing, quantum computing and information, and probability. His awards include best paper and best student paper at CCC, and a Sloan Research Fellowship.
Alon Orlitsky
Alon Orlitsky is the Qualcomm Professor for Information Theory and its Applications at UCSD. He received a Ph.D. from Stanford University. Before joining UCSD, he spent time at AT&T Bell Labs' Mathematical Sciences Research Center and D.E. Shaw and Company. His research concerns information theory, compression, communication, probability estimation, prediction, machine learning, and speech recognition. His awards include the 1982 ITT International Fellowship, 1992 IEEE W.R.G. Baker award, 2006 IEEE Information Theory Paper Award, 2015 NIPS Best Paper Award, and 2017 ICML Best Paper Award, Honorable Mention.
Ronitt Rubinfeld
Ronitt Rubinfeld joined the MIT faculty in 2004, and is on the faculty at the University of Tel Aviv. Her research interests include randomized algorithms and computational complexity. She co-initiated the fields of Property Testing and Sub-linear time algorithms, providing the foundations for measuring the performance of algorithms that analyze data without looking at all of it. Her work on Linearity Testing has helped bridge between Computational Complexity, Analysis of Boolean Functions, and Additive Combinatorics. Rubinfeld has been an ONR Young Investigator, a Sloan Fellow, an invited speaker at the 2006 International Congress of Mathematicians, and is an ACM Fellow.

Organizers and support

This workshop was organized by Clément Canonne and Gautam "G" Kamath, with the support of the FOCS Tutorial and Workshop chairs James R. Lee and Aleksander Mądry.