Tuesday, 16:15 - 16:40 h, Room: H 2038


Venkat Chandrasekaran
Computational and sample tradeoffs via convex relaxation

Coauthor: Michael Jordan


In modern data analysis, one is frequently faced with statistical inference problems involving massive datasets. In this talk we discuss a computational framework based on convex relaxation in order to reduce the computational complexity of an inference procedure when one has access to increasingly larger datasets. Essentially, the statistical gains from larger datasets can be exploited to reduce the runtime of inference algorithms.


Talk 3 of the invited session Tue.3.H 2038
"Conic and convex programming in statistics and signal processing I" [...]
Cluster 4
"Conic programming" [...]


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