• Publications
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Learning to detect objects in images via a sparse, part-based representation
  • S. Agarwal, A. Awan, D. Roth
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 November 2004
TLDR
A learning-based approach to the problem of detecting objects in still, gray-scale images that makes use of a sparse, part-based representation is developed and a critical evaluation of the approach under the proposed standards is presented. Expand
Learning a Sparse Representation for Object Detection
TLDR
An approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects, that achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation. Expand
Generalization Bounds for the Area Under the ROC Curve
TLDR
The expected accuracy of a ranking function is defined (analogous to the expected error rate of a classification function), and distribution-free probabilistic bounds on the deviation of the empirical AUC of aranking function (observed on a finite data sequence) are derived from its expected accuracy. Expand
Generalization Bounds for Ranking Algorithms via Algorithmic Stability
TLDR
It is shown that kernel-based ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore bounds can be applied to these algorithms; this is in contrast with generalization bounds based on uniform convergence, which in many cases cannot be appliedTo this point, earlier results that were derived in the special setting of bipartite ranking are generalized. Expand
The Infinite Push: A New Support Vector Ranking Algorithm that Directly Optimizes Accuracy at the Absolute Top of the List
TLDR
A new ranking algorithm that directly maximizes the number of relevant objects retrieved at the absolute top of the list using the recent l1, ∞ projection method of Quattoni et al (2009). Expand
A Structural SVM Based Approach for Optimizing Partial AUC
TLDR
A structural SVM framework for directly optimizing the partial AUC between any two false positive rates and an efficient algorithm for solving this combinatorial optimization problem that has the same computational complexity as Joachims' algorithm for optimizing the usual AUC is developed. Expand
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures
TLDR
This work considers plug-in algorithms that learn a classifier by applying an empirically determined threshold to a suitable 'estimate' of the class probability, and provides a general methodology to show consistency of these methods for any non-decomposable measure that can be expressed as a continuous function of true positive rate and true negative rate. Expand
Stability and Generalization of Bipartite Ranking Algorithms
TLDR
It is shown that kernel-based ranking algorithms that perform regularization in a reproducing kernel Hilbert space have such stability properties, and therefore the 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 appliedto these algorithms. Expand
Consistent algorithms for multiclass classification with an abstain option
We consider the problem of n-class classification (n ≥ 2), where the classifier can choose to abstain from making predictions at a given cost, say, a factor α of the cost of misclassification. OurExpand
Ranking on graph data
  • S. Agarwal
  • Mathematics, Computer Science
  • ICML
  • 25 June 2006
TLDR
An algorithmic framework for learning ranking functions on graph data, based on recent developments in regularization theory for graphs and corresponding Laplacian-based methods for classification, is developed. Expand
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