|Nature in the City: Changes in Bangalore over Time and Space
Nature in the City examines the past, present, and future of nature in Bangalore, one of India's largest cities. Though threatened, nature in the city exhibits a remarkable tenacity. The book examines changes in nature over space and time from the 5th century CE to the present, concluding with a vision of a better future. This is a preview of a forthcoming book by Harini Nagendra 'Nature in the City: Bengaluru in the Past, Present, and Future', Oxford University Press (April 2016).
|MobileFusion: Create 3D scans with your mobile phone
MobileFusion is a research project that turns ordinary mobile phones into 3D scanners without any additional hardware. The resulting 3D scans are detailed enough for 3D printing or use in augmented-reality games.
|A Software only Mechanism for Device Pass-Through and Sharing
|Bootstrapping Privacy Compliance in Big Data Systems
|Stochastic Methods for Complex Performance Measures: A Tale of Two Families
|Ordered Stick-breaking Prior for Sequential MCMC Inference of Bayesian Non-Parametric Models
|X1-Locally Non-linear Embeddings for Extreme Multi-label Learning
|Extreme Multi-Label Classification
|An Introduction to Concentration Inequalities and Statistical Learning Theory
The aim of this tutorial is to introduce tools and techniques that are used to analyze machine learning algorithms in statistical settings. Our focus will be on learning problems such as classification, regression, and ranking. We will look at concentration inequalities and other commonly used techniques such as uniform convergence and symmetrization, and use them to prove learning theoretic guarantees for algorithms in these settings.
The talk will be largely self-contained. However, it would help if the audience could brush up basic probability and statistics concepts such as random variables, events, probability of events, Boole's inequality etc. There are several good resources for these online and I do not wish to recommend one over the other. However, a couple of nice resources are given below
|Non-Convex Robust PCA - Part 2
In this lecture, we will illustrate a novel technique due to Erdos et al. (2011) which can be used to obtain bounds on eigenvector perturbation in the ℓ∞ norm. Standard techniques give us optimal bounds only for perturbation in the ℓ2 norm. We will further use this technique to propose and analyze a new non-convex algorithm for robust PCA, where the task is to recover a low-rank matrix from sparse corruptions that are of unknown value and support. In the deterministic error setting, our method achieves exact recovery under the same conditions that are required by existing methods (which are based on convex optimization) but is much faster.
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