Results 1  10
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45
Ranking on graph data
 In ICML
, 2006
"... In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a realvalued ranking function that induces a ranking or ordering over the object space. We consider the problem of learning such a ranking function when the data is represented as a ..."
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Cited by 33 (1 self)
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In ranking, one is given examples of order relationships among objects, and the goal is to learn from these examples a realvalued ranking function that induces a ranking or ordering over the object space. We consider the problem of learning such a ranking function when the data is represented as a graph, in which vertices correspond to objects and edges encode similarities between objects. Building on recent developments in regularization theory for graphs and corresponding Laplacianbased methods for classification, we develop an algorithmic framework for learning ranking functions on graph data. We provide generalization guarantees for our algorithms via recent results based on the notion of algorithmic stability, and give experimental evidence of the potential benefits of our framework. 1.
Statistical analysis of Bayes optimal subset ranking
 IEEE Transactions on Information Theory
, 2008
"... Abstract—The ranking problem has become increasingly important in modern applications of statistical methods in automated decision making systems. In particular, we consider a formulation of the statistical ranking problem which we call subset ranking, and focus on the DCG (discounted cumulated gain ..."
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Cited by 23 (0 self)
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Abstract—The ranking problem has become increasingly important in modern applications of statistical methods in automated decision making systems. In particular, we consider a formulation of the statistical ranking problem which we call subset ranking, and focus on the DCG (discounted cumulated gain) criterion that measures the quality of items near the top of the ranklist. Similar to error minimization for binary classification, direct optimization of natural ranking criteria such as DCG leads to a nonconvex optimization problems that can be NPhard. Therefore a computationally more tractable approach is needed. We present bounds that relate the approximate optimization of DCG to the approximate minimization of certain regression errors. These bounds justify the use of convex learning formulations for solving the subset ranking problem. The resulting estimation methods are not conventional, in that we focus on the estimation quality in the topportion of the ranklist. We further investigate the asymptotic statistical behavior of these formulations. Under appropriate conditions, the consistency of the estimation schemes with respect to the DCG metric can be derived. I.
Name reference resolution in organizational email archives
 In SIAM
, 2006
"... Online communications provide a rich resource for understanding social networks. Information about the actors, and their dynamic roles and relationships, can be inferred from both the communication content and traffic structure. A key component in the analysis of online communications such as email ..."
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Cited by 21 (7 self)
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Online communications provide a rich resource for understanding social networks. Information about the actors, and their dynamic roles and relationships, can be inferred from both the communication content and traffic structure. A key component in the analysis of online communications such as email is the resolution of name references within the body of the message. Name reference resolution relies on the context of the message; both the content of the message and the sender and recipients ’ relationships can help to resolve a reference. Here we investigate a variety of approaches which make use of the email traffic network to disambiguate email name references. The email traffic network serves as a proxy for inferring relationships. These relationships in turn help us infer likely candidates for the name references. Our initial findings suggest that simple temporal models can help us effectively resolve name references. For the class of models proposed, performance is maximized by exploiting longterm traffic statistics to rank candidates. 1
Ranking with a pnorm push
, 2005
"... Abstract. We are interested in supervised ranking with the following twist: our goal is to design algorithms that perform especially well near the top of the ranked list, and are only required to perform sufficiently well on the rest of the list. Towards this goal, we provide a general form of conve ..."
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Cited by 20 (1 self)
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Abstract. We are interested in supervised ranking with the following twist: our goal is to design algorithms that perform especially well near the top of the ranked list, and are only required to perform sufficiently well on the rest of the list. Towards this goal, we provide a general form of convex objective that gives highscoring examples more importance. This “push ” near the top of the list can be chosen arbitrarily large or small. We choose ℓpnorms to provide a specific type of push; as p becomes large, the algorithm concentrates harder near the top of the list. We derive a generalization bound based on the pnorm objective. We then derive a corresponding boostingstyle algorithm, and illustrate the usefulness of the algorithm through experiments on UCI data. We also prove that the minimizer of the objective is unique in a specific sense. 1
Stability and generalization of bipartite ranking algorithms
 Proceedings of the Eighteenth Annual Conference on Computational Learning Theory (COLT
, 2005
"... Abstract. The problem of ranking, in which the goal is to learn a realvalued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the rankin ..."
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Cited by 19 (2 self)
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Abstract. The problem of ranking, in which the goal is to learn a realvalued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability. In particular, we derive generalization bounds for bipartite ranking algorithms that have good stability properties. We show that kernelbased ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore our bounds can be applied to these algorithms; this is in contrast with previous generalization bounds for ranking, which are based on uniform convergence and in many cases cannot be applied to these algorithms. A comparison of the bounds we obtain with corresponding bounds for classification algorithms yields some interesting insights into the difference in generalization behaviour between ranking and classification. 1
Ranking the best instances
 Journal of Machine Learning Research
"... We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of realvalued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scor ..."
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Cited by 19 (10 self)
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We formulate a local form of the bipartite ranking problem where the goal is to focus on the best instances. We propose a methodology based on the construction of realvalued scoring functions. We study empirical risk minimization of dedicated statistics which involve empirical quantiles of the scores. We first state the problem of finding the best instances which can be cast as a classification problem with mass constraint. Next, we develop special performance measures for the local ranking problem which extend the Area Under an ROC Curve (AUC) criterion and describe the optimal elements of these new criteria. We also highlight the fact that the goal of ranking the best instances cannot be achieved in a stagewise manner where first, the best instances would be tentatively identified and then a standard AUC criterion could be applied. Eventually, we state preliminary statistical results for the local ranking problem.
Estimating Class Membership Probabilities using Classifier Learners
"... We present an algorithm, "Probing", which reduces learning an estimator of class probability membership to learning binary classifiers. The reduction comes with a theoretical guarantee: a small error rate for binary classification implies accurate estimation of class membership probabilities. We tes ..."
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Cited by 17 (5 self)
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We present an algorithm, "Probing", which reduces learning an estimator of class probability membership to learning binary classifiers. The reduction comes with a theoretical guarantee: a small error rate for binary classification implies accurate estimation of class membership probabilities. We tested our reduction on several datasets with several classifier learning algorithms. The results show strong performance as compared to other common methods for obtaining class membership probability estimates from classifiers.
RANKING AND EMPIRICAL MINIMIZATION OF USTATISTICS
"... The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous statistical framework. The goal is to learn a ranking rule for deciding, among two instances, which one ..."
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Cited by 17 (2 self)
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The problem of ranking/ordering instances, instead of simply classifying them, has recently gained much attention in machine learning. In this paper we formulate the ranking problem in a rigorous statistical framework. The goal is to learn a ranking rule for deciding, among two instances, which one is “better, ” with minimum ranking risk. Since the natural estimates of the risk are of the form of a Ustatistic, results of the theory of Uprocesses are required for investigating the consistency of empirical risk minimizers. We establish, in particular, a tail inequality for degenerate Uprocesses, and apply it for showing that fast rates of convergence may be achieved under specific noise assumptions, just like in classification. Convex risk minimization methods are also studied. 1. Introduction. Motivated
Marginbased Ranking and an Equivalence between AdaBoost and RankBoost
, 2009
"... We study boosting algorithms for learning to rank. We give a general marginbased bound for ranking based on covering numbers for the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin will generalize well. We then describe a new algorithm, smooth margin ranking, t ..."
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Cited by 14 (8 self)
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We study boosting algorithms for learning to rank. We give a general marginbased bound for ranking based on covering numbers for the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin will generalize well. We then describe a new algorithm, smooth margin ranking, that precisely converges to a maximum rankingmargin solution. The algorithm is a modification of RankBoost, analogous to “approximate coordinate ascent boosting. ” Finally, we prove that AdaBoost and RankBoost are equally good for the problems of bipartite ranking and classification in terms of their asymptotic behavior on the training set. Under natural conditions, AdaBoost achieves an area under the ROC curve that is equally as good as RankBoost’s; furthermore, RankBoost, when given a specific intercept, achieves a misclassification error that is as good as AdaBoost’s. This may help to explain the empirical observations made by Cortes and Mohri, and Caruana and NiculescuMizil, about the excellent performance of AdaBoost as a bipartite ranking algorithm, as measured by the area under the ROC curve.
An Efficient Reduction of Ranking to Classification
, 2007
"... This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice tha ..."
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Cited by 11 (2 self)
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This paper describes an efficient reduction of the learning problem of ranking to binary classification. The reduction is randomized and guarantees a pairwise misranking regret bounded by that of the binary classifier, improving on a recent result of Balcan et al. (2007) which ensures only twice that upperbound. Moreover, our reduction applies to a broader class of ranking loss functions, admits a simple proof, and the expected time complexity of our algorithm in terms of number of calls to a classifier or preference function is also improved from Ω(n 2) to O(n log n). In addition, when the top k ranked elements only are required (k ≪ n), as in many applications in information extraction or search engine design, the time complexity of our algorithm can be further reduced to O(k log k+n). Our reduction and algorithm are thus practical for realistic applications where the number of points to rank exceeds several thousands. Much of our results also extend beyond the bipartite case previously studied. To further complement them, we also derive lower bounds for any deterministic reduction of ranking to binary classification, proving that randomization is necessary to achieve our reduction guarantees. 1