We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchi...
We present a noisy-OR Bayesian network model for simulation-based training, and an efficient search-based algorithm for automatic synthesis of plausible training scenarios from co...
Eugene Grois, William H. Hsu, Mikhail Voloshin, Da...
This article describes how robust error-kernels can be used as smoothness priors in recovering shape-from-shading (SFS). Conventionally, the smoothness error is added to the data-...
It is widely accepted that the use of more compact representations than lookup tables is crucial to scaling reinforcement learning (RL) algorithms to real-world problems. Unfortun...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...
We study some mathematical programming formulations for the origin-destination model in airline revenue management. In particular, we focus on the traditional probabilistic model ...