scholar.google.com › citations
We study the problem of learning influence functions under incomplete observa- tions of node activations. Incomplete observations are a major concern as ...
Learning Influence Functions from Incomplete Observations
papers.nips.cc › paper › 6181-learning-i...
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most ( ...
Nov 7, 2016 · We study the problem of learning influence functions under incomplete observations of node activations.
Reviewer 1. Summary. The authors consider the problem of PAC-learning the influence function in cascade models under a incomplete observation model.
Experiments on synthetic and real-world datasets demonstrate the ability of the proper and improper PAC learnability of influence functions under randomly ...
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most ...
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most ...
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most ...
Mar 16, 2011 · Influence functions are basically an analytical tool that can be used to assess the effect (or "influence") of removing an observation on the value of a ...
Our results for full observation provide concrete sample complexity guarantees for learning influence functions using local estimation, to any desired accuracy; ...
Missing: Incomplete | Show results with:Incomplete