Shivani Agarwal

TL;DR: 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.

TL;DR: 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.

TL;DR: The engineering of personalized tumour ecosystems that contextually conserve the tumour heterogeneity, and phenocopy the tumours microenvironment using tumour explants maintained in defined tumour grade-matched matrix support and autologous patient serum are reported.

TL;DR: 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.

TL;DR: 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.