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NIPS papers to discuss (in order)
For Wed. 1/16:
- Loh & Wainwright, Structure estimation for discrete graphical models (Link): Qiang L.
- Hsieh et al., A Divide-and-Conquer Method for Sparse Inverse Covariance Estimation, (Link), Dave K.
For Wed. 1/23:
- Ermon et al., Density Propagation and Improved Bounds on the Partition Function, (Link), Qiang C.
For Wed. 1/30:
- Zhou et al., Learning from the Wisdom of Crowds by Minimax Entropy, (Link), Wei P.
For Wed. 2/6:
- Liu et al., Learning as MAP Inference in Discrete Graphical Models, (Link), Drew F.
For Wed. 2/13:
For Wed. 2/20:
For Wed. 2/27: Mike Carey
For Wed. 3/06:
- Kedem et al., Non-linear Metric Learning, Link, Wei
For Wed. 3/13:
Possible:
- Mohan et al., Structured Learning of Gaussian Graphical Models, Link, ?
- Jebara & Choromanska, Majorization for CRFs and Latent Likelihoods, Link, ?
- Defazio et al., A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation, (Link), Sholeh F.
- Zhang et al., Communication-Efficient Algorithms for Statistical Optimization, Link, ?
- Belanger et al., MAP Inference in Chains using Column Generation, Link, ?
Older papers of possible interest to read:
- Sheldon & Diettrich, Collective Graphical models Link
- Weller & Jebara, Bethe Bounds and Approximating the Global Optimum Link
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