You think you want big data? You can't handle big data! (Next Week at the Statistics Seminar)
Fortunately, however, the methods of those who can handle big data
are neither grotesque nor incomprehensible, and we will hear about them on
- Alekh Agarwal, "Computation Meets Statistics: Trade-offs and Fundamental
Limits for Large Data Sets"
- Abstract: The past decade has seen the emergence of datasets of
unprecedented scale, with both large sample sizes and dimensionality. Massive
data sets arise in various domains, among them computer vision, natural
language processing, computational biology, social networks analysis and
recommendation systems, to name a few. In many such problems, the bottleneck
is not just the number of data samples, but also the computational resources
available to process the data. Thus, a fundamental goal in these problems is
to characterize how estimation error behaves as a function of the sample size,
number of parameters, and the computational budget available.
- In this talk, I present three research threads that provide complementary
lines of attack on this broader research agenda: (i) lower bounds for
statistical estimation with computational constraints; (ii) interplay between
statistical and computational complexities in structured high-dimensional
estimation; and (iii) a computational budgeted framework for model selection.
The first characterizes fundamental limits in a uniform sense over all methods,
whereas the latter two provide explicit algorithms that exploit the interaction
of computational and statistical considerations.
- Joint work with John Duchi, Sahand Negahban, Clement Levrard, Pradeep
Ravikumar, Peter Bartlett, and Martin Wainwright.
- Time and place: 4--5 pm on Monday, 6 February 2012, in Scaife Hall 125
As always, the talk is free and open to the public.
Posted at January 31, 2012 19:00 | permanent link