Computation meets Statistics: Trade-offs and fundamental limits
In this talk, I present two research threads that provide complementary lines of attack on this broader research agenda: lower bounds for statistical estimation with computational constraints, and (ii) distributed algorithms for statistical inference. The first characterizes fundamental limits in a uniform sense over all methods, whereas the latter provides explicit algorithms that exploits the interaction of computational and statistical considerations in a distributed computing environment.
[Joint work with John Duchi, Pradeep Ravikumar, Peter Bartlett and Martin Wainwright]
Alekh Agarwal is a fifth year PhD student at UC Berkeley, jointly advised by Peter Bartlett and Martin Wainwright. Alekh has received PhD fellowships from Microsoft Research and Google. His main research interests are in the areas of machine learning, convex optimization, high-dimensional statistics, distributed machine learning and understanding the computational trade-offs in machine learning problems.