Approximate Inference in Collective Graphical Models

@inproceedings{Sheldon2013ApproximateII,
  title={Approximate Inference in Collective Graphical Models},
  author={Daniel Sheldon and Tao Sun and Akshat Kumar and Thomas G. Dietterich},
  booktitle={ICML},
  year={2013}
}
We study the problem of approximate inference in collective graphical models (CGMs), which were recently introduced to model the problem of learning and inference with noisy aggregate observations. We first analyze the complexity of inference in CGMs: unlike inference in conventional graphical models, exact inference in CGMs is NP-hard even for tree-structured models. We then develop a tractable convex approximation to the NPhard MAP inference problem in CGMs, and show how to use MAP inference… CONTINUE READING
Highly Cited
This paper has 21 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 15 extracted citations

Learning Mixtures of Markov Chains from Aggregate Data with Structural Constraints (Extended Abstract)

2017 IEEE 33rd International Conference on Data Engineering (ICDE) • 2016
View 3 Excerpts

Convex Risk Minimization to Infer Networks from probabilistic diffusion data at multiple scales

2015 IEEE 31st International Conference on Data Engineering • 2015
View 2 Excerpts

References

Publications referenced by this paper.
Showing 1-10 of 23 references

Convergence of a stochastic approximation version of the EM algorithm

B. Delyon, M. Lavielle, E. Moulines
Annals of Statistics, • 1999
View 3 Excerpts
Highly Influenced

From lifted inference to lifted models

D. Fierens, K. Kersting
In Second International Workshop on Statistical Relational AI, • 2012
View 1 Excerpt

eBird: A citizen-based bird observation network in the biological sciences

B. L. Sullivan, C. L. Wood, +3 authors S. Kelling
Biological Conservation, • 2009
View 1 Excerpt

Similar Papers

Loading similar papers…