Avleen Singh Bijral

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We address the issue of using mini-batches in stochastic optimization of SVMs. We show that the same quantity, the spectral norm of the data, controls the parallelization speedup obtained for both primal stochastic subgradient descent (SGD) and stochastic dual coordinate ascent (SCDA) methods and use it to derive novel variants of mini-batched SDCA. Our(More)
Machine learning applications often involve data that can be analyzed as unit vectors on a d-dimensional hypersphere, or equivalently are directional in nature. Spectral clustering techniques generate embeddings that constitute an example of directional data and can result in different shapes on a hypersphere (depending on the original structure). Other(More)
We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distancebased supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the(More)
The role of a distance metric in many supervised and semi-supervised learning applications is central in the success of clustering algorithms. Since existing metrics like Euclidean do not necessarily reflect the true structure (clusters or manifolds) in the data, it becomes imperative that an appropriate metric be somehow learned from training or labeled(More)
Many terrains in outdoor robot navigation problems have paths that are distinct and continuous compared to the non-traversable regions. In image space these paths correspond to continuous segments that can be thought of as clusters embedded in image feature space. These segments very often translate directly to traversable ground plane. In this paper we(More)
We study a consensus-based distributed stochastic gradient method for distributed optimization in a setting common for machine learning applications. Nodes in the network hold disjoint data and seek to optimize a common objective which decomposes into a sum of convex functions of individual data points. We show that the rate of convergence for this method(More)
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