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In this paper we introduce a real-time system for action detection. The system uses a small set of robust features extracted from 3D skeleton data. Features are effectively described based on the probability distribution of skeleton data. The descriptor computes a pyramid of sample covariance matrices and mean vectors to encode the relationship between the(More)
Ranking is the central problem for many applications such as web search, recommendation systems, and visual comparison of images. In this paper, the multiple kernel learning framework is generalized for the learning to rank problem. This approach extends the existing learning to rank algorithms by considering multiple kernel learning and consequently(More)
We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new domain, using only bandit feedback: the system receives a German sentence to translate, produces an English sentence, and only gets a scalar score as feedback. Targeting these two(More)
In the multi-arm bandit problem with iid rewards, the learner selects an arm a ∈ A at every time step t. Learning proceeds in rounds, and we assume that the number of rounds is fixed, and indexed by t = 1 · · ·T . At each round, the algorithm chooses one action at (we’ll use the arms and actions interchangeably to mean the same thing). After taking the(More)
Oral-facial-digital syndrome type 1 (OMIM #311200) is an X-linked dominant, developmental disorder. Among the 13 described clinical variants of oral-facial-digital syndrome, oral-facial-digital syndrome type 1 is of significance to dermatologists due to presence of congenital milia and hypotrichosis, not described in other variants. Since(More)
In this lecture, we will study bandit problems with linear costs. In this setting, actions are represented by vectors in a low-dimensional real space. For simplicity, we will assume that all actions lie within a unit hypercube: a ∈ [0, 1]d. The action costs ct(a) are linear in the vector a, namely: ct(a) = a · vt for some weight vector vt ∈ Rd which is the(More)