We present a family of adaptive pairwise tournaments which are provably robust against large error fractions when used to determine the largest element in a set. These tournaments use nk pairwise comparisons but have only O(k + log n) depth where n is the number of players and k is a robustness parameter (for reasonable values of n and k). We show how these tournaments can be used to prove mult...

This paper investigates whether meaningful shape categories can be identified in an unsupervised way by clustering shocktrees. We commence by computing weighted and unweighted edit distances between shock-trees extracted from the HamiltonJacobi skeleton of 2D binary shapes. Next we use an EMlike algorithm to locate pairwise clusters in the pattern of edit-distances. We show that when the tree e...

Observational studies provide a rich source of information for assessing effectiveness of treatment interventions in many situations where it is not ethical or practical to perform randomized controlled trials. However, such studies are prone to bias from hidden (unmeasured) confounding. A promising approach to identifying and reducing the impact of unmeasured confounding is prior event rate ra...

In this article, the weighted empirical likelihood is applied to a general setting of two-sample semiparametric models, which includes biased sampling models and case-control logistic regression models as special cases. For various types of censored data, such as right censored data, doubly censored data, interval censored data and partly interval-censored data, the weighted empirical likelihoo...

Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age a...

Recently, Krahenbuhl and Koltun proposed an efficient inference method for densely connected pairwise random fields using the mean-field approximation for a Conditional Random Field (CRF). However, they restrict their pairwise weights to take the form of a weighted combination of Gaussian kernels where each Gaussian component is allowed to take only zero mean, and can only be rescaled by a sing...

We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss (WARP). WMRB uses a new rank estimator and an efficient batch training algorithm. The approach allows more accurate item rank approximation and explicit utilization of parallel computation to accelerate training. In three item recommendation ta...

Neighborhood Preserving Embedding (NPE) and extensions of NPE are hot research topics of data mining at present. An algorithm called Constraint Sparse Neighborhood Preserving Embedding (CSNPE) for dimensionality reduction is proposed in the paper. The algorithm firstly creates the local sparse reconstructive relation information of samples; then, exacts the pairwise constrain information of sam...

The deficiency of the ability for preserving global geometric structure information of data is the main problem of existing semi-supervised dimensionality reduction with pairwise constraints. A dimensionality reduction algorithm called Semi-supervised Sparsity Pairwise Constraint Preserving Projections based on Genetic Algorithm (SSPCPPGA) is proposed. On the one hand, the algorithm fuses unsup...