737 research outputs found
Flexible combination of multiple diagnostic biomarkers to improve diagnostic accuracy
In medical research, it is common to collect information of multiple
continuous biomarkers to improve the accuracy of diagnostic tests. Combining
the measurements of these biomarkers into one single score is a popular
practice to integrate the collected information, where the accuracy of the
resultant diagnostic test is usually improved. To measure the accuracy of a
diagnostic test, the Youden index has been widely used in literature. Various
parametric and nonparametric methods have been proposed to linearly combine
biomarkers so that the corresponding Youden index can be optimized. Yet there
seems to be little justification of enforcing such a linear combination. This
paper proposes a flexible approach that allows both linear and nonlinear
combinations of biomarkers. The proposed approach formulates the problem in a
large margin classification framework, where the combination function is
embedded in a flexible reproducing kernel Hilbert space. Advantages of the
proposed approach are demonstrated in a variety of simulated experiments as
well as a real application to a liver disorder study
The Garden Hose Complexity for the Equality Function
The garden hose complexity is a new communication complexity introduced by H.
Buhrman, S. Fehr, C. Schaffner and F. Speelman [BFSS13] to analyze
position-based cryptography protocols in the quantum setting. We focus on the
garden hose complexity of the equality function, and improve on the bounds of
O. Margalit and A. Matsliah[MM12] with the help of a new approach and of our
handmade simulated annealing based solver. We have also found beautiful
symmetries of the solutions that have lead us to develop the notion of garden
hose permutation groups. Then, exploiting this new concept, we get even
further, although several interesting open problems remain.Comment: 16 page
Augmented Lagrangian Algorithms under Constraint Partitioning
We present a novel constraint-partitioning approach for solving continuous nonlinear optimization based on augmented Lagrange method. In contrast to previous work, our approach is based on a new constraint partitioning theory and can handle global constraints. We employ a hyper-graph partitioning method to recognize the problem structure. We prove global convergence under assumptions that are much more relaxed than previous work and solve problems as large as 40,000 variables that other solvers such as IPOPT [11] cannot solve
Explainable Reasoning over Knowledge Graphs for Recommendation
Incorporating knowledge graph into recommender systems has attracted
increasing attention in recent years. By exploring the interlinks within a
knowledge graph, the connectivity between users and items can be discovered as
paths, which provide rich and complementary information to user-item
interactions. Such connectivity not only reveals the semantics of entities and
relations, but also helps to comprehend a user's interest. However, existing
efforts have not fully explored this connectivity to infer user preferences,
especially in terms of modeling the sequential dependencies within and holistic
semantics of a path. In this paper, we contribute a new model named
Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for
recommendation. KPRN can generate path representations by composing the
semantics of both entities and relations. By leveraging the sequential
dependencies within a path, we allow effective reasoning on paths to infer the
underlying rationale of a user-item interaction. Furthermore, we design a new
weighted pooling operation to discriminate the strengths of different paths in
connecting a user with an item, endowing our model with a certain level of
explainability. We conduct extensive experiments on two datasets about movie
and music, demonstrating significant improvements over state-of-the-art
solutions Collaborative Knowledge Base Embedding and Neural Factorization
Machine.Comment: 8 pages, 5 figures, AAAI-201
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