Learning Graphical Models
Policy Regret in Repeated Games
Raman Arora, Michael Dinitz, Teodor Vanislavov Marinov, Mehryar Mohri
Neural Information Processing Systems http://nips.cc/
A Preliminaries
We now move to the misspecified case.
Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models
Shanshan Wu, Sujay Sanghavi, Alexandros G. Dimakis
Structure learning is the task of finding the dependency graph of a Markov random field (MRF) given i.i.d.
The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data
Amanda Gentzel, Dan Garant, David Jensen
Anactivecommunity ofresearchers developsandenhances algorithms that learn causal models from data, and this work has produced a series of impressivetechnicaladvances.
Policy-Conditioned UncertaintySetsfor RobustMarkovDecisionProcesses
What policy should be employed in a Markov decision process with uncertain parameters?
Combining Generative and Discriminative Models for Hybrid Inference
Victor Garcia Satorras, Zeynep Akata, Max Welling
Neural Information Processing Systems http://nips.cc/