Well File:
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- Well Plat ( results)
- Wellbore Schematic ( results)
- Directional Survey ( results)
- Fluid Sample ( results)
- Log ( results)
- Density ( results)
- Gamma Ray ( results)
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- Report ( results)
- Daily Report ( results)
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Yash Deshpande
Inference in Graphical Models via Semidefinite Programming Hierarchies
Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
Popular inference algorithms such as belief propagation (BP) and generalized belief propagation (GBP) are intimately related to linear programming (LP) relaxation within the Sherali-Adams hierarchy. Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares (SOS) hierarchy based on semidefinite programming (SDP) can provide superior guarantees.
Contextual Stochastic Block Models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel
We provide the first information theoretic tight analysis for inference of latent community structure given a sparse graph along with high dimensional node covariates, correlated with the same latent communities.
Contextual Stochastic Block Models
Yash Deshpande, Subhabrata Sen, Andrea Montanari, Elchanan Mossel
Neural Information Processing Systems http://nips.cc/
Inference in Graphical Models via Semidefinite Programming Hierarchies
Murat A. Erdogdu, Yash Deshpande, Andrea Montanari
Popular inference algorithms such as belief propagation (BP) and generalized belief propagation (GBP) are intimately related to linear programming (LP) relaxation within the Sherali-Adams hierarchy. Despite the popularity of these algorithms, it is well understood that the Sum-of-Squares (SOS) hierarchy based on semidefinite programming (SDP) can provide superior guarantees.