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- Yuting Liu, Bin Gao, +4 authors Hang Li
- SIGIR
- 2008

This paper proposes a new method for computing page importance, referred to as BrowseRank. The conventional approach to compute page importance is to exploit the link graph of the web and to build a model based on that graph. For instance, PageRank is such an algorithm, which employs a discrete-time Markov process as the model. Unfortunately, the link graph… (More)

- Yuting Liu, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang Li
- WWW
- 2007

This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by means of <i>unsupervised</i> learning. To further enhance ranking accuracies, we propose employing <i>supervised</i> learning to perform the task, using labeled data. We refer to… (More)

- Qi Meng, Wei Chen, Jingcheng Yu, Taifeng Wang, Zhiming Ma, Tie-Yan Liu
- AAAI
- 2017

Regularized empirical risk minimization (R-ERM) is an important branch of machine learning, since it constrains the capacity of the hypothesis space and guarantees the generalization ability of the learning algorithm. Two classic proximal optimization algorithms, i.e., proximal stochastic gradient descent (ProxSGD) and proximal stochastic coordinate descent… (More)

- Yanyan Lan, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang Li
- ICML
- 2008

This paper is concerned with the generalization ability of learning to rank algorithms for information retrieval (IR). We point out that the key for addressing the learning problem is to look at it from the viewpoint of <i>query</i>. We define a number of new concepts, including query-level loss, query-level risk, and query-level stability. We then analyze… (More)

- Wei Chen, Tie-Yan Liu, Yanyan Lan, Zhiming Ma, Hang Li
- NIPS
- 2009

Learning to rank has become an important research topic in machine learning. While most learning-to-rank methods learn the ranking functions by minimizing loss functions, it is the ranking measures (such as NDCG and MAP) that are used to evaluate the performance of the learned ranking functions. In this work, we reveal the relationship between ranking… (More)

- Tianqi Zhu, Yucheng Hu, Zhiming Ma, De-Xing Zhang, Tiejun Li, Ziheng Yang
- Bioinformatics
- 2011

MOTIVATION
Cancer is well known to be the end result of somatic mutations that disrupt normal cell division. The number of such mutations that have to be accumulated in a cell before cancer develops depends on the type of cancer. The waiting time T(m) until the appearance of m mutations in a cell is thus an important quantity in population genetics models… (More)

- Xumin Ni, Xiong Yang, +4 authors Shuhua Xu
- Scientific reports
- 2016

The length of ancestral tracks decays with the passing of generations which can be used to infer population admixture histories. Previous studies have shown the power in recovering the histories of admixed populations via the length distributions of ancestral tracks even under simple models. We believe that the deduction of length distributions under a… (More)

- Yanyan Lan, Tie-Yan Liu, Zhiming Ma, Hang Li
- ICML
- 2009

This paper presents a theoretical framework for ranking, and demonstrates how to perform generalization analysis of listwise ranking algorithms using the framework. Many learning-to-rank algorithms have been proposed in recent years. Among them, the listwise approach has shown higher empirical ranking performance when compared to the other approaches.… (More)

- Guang Feng, Tie-Yan Liu, +4 authors Wei-Ying Ma
- SIGIR
- 2006

Since the website is one of the most important organizational structures of the Web, how to effectively rank websites has been essential to many Web applications, such as Web search and crawling. In order to get the ranks of websites, researchers used to describe the inter-connectivity among websites with a so-called HostGraph in which the nodes denote… (More)

- Bin Gao, Tie-Yan Liu, Zhiming Ma, Taifeng Wang, Hang Li
- CIKM
- 2009

We propose a <i>General Markov Framework</i> for computing page importance. Under the framework, a <i>Markov Skeleton Process</i> is used to model the random walk conducted by the web surfer on a given graph. Page importance is then defined as the product of <i>page reachability</i> and <i>page utility</i>, which can be computed from the transition… (More)