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Nature in the City: Changes in Bangalore over Time and Space
Nature in the City: Changes in Bangalore over Time and Space
Harini Nagendra
00:51:53 · 16 November 2015

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: Create 3D scans with your mobile phone
00:02:16 · 24 August 2015

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
A Software only Mechanism for Device Pass-Through and Sharing
Piyus Kedia
00:34:35 · 4 August 2015
Bootstrapping Privacy Compliance in Big Data Systems
Bootstrapping Privacy Compliance in Big Data Systems
00:02:01 · 1 July 2015

With the rapid increase in cloud services collecting and using user data to offer personalized experiences, ensuring that these services comply with their privacy policies has become a business imperative for building user trust. However, most compliance efforts in industry today rely on manual review processes and audits designed to safeguard user data, and therefore are resource intensive and lack coverage. In this paper, we present our experience building and operating a system to automate privacy policy compliance checking in Bing.

Stochastic Methods for Complex Performance Measures: A Tale of Two Families
Stochastic Methods for Complex Performance Measures: A Tale of Two Families
Harikrishna Narasimhan
00:20:03 · 26 June 2015
Ordered Stick-breaking Prior for Sequential MCMC Inference of Bayesian Non-Parametric Models
Ordered Stick-breaking Prior for Sequential MCMC Inference of Bayesian Non-Parametric Models
Mrinal Das
00:17:10 · 26 June 2015
X1-Locally Non-linear Embeddings for Extreme Multi-label Learning
X1-Locally Non-linear Embeddings for Extreme Multi-label Learning
Kush Bhatia
00:22:39 · 25 June 2015
Extreme Multi-Label Classification
Extreme Multi-Label Classification
Yashoteja Prabhu
00:23:28 · 25 June 2015
An Introduction to Concentration Inequalities and Statistical Learning Theory
An Introduction to Concentration Inequalities and Statistical Learning Theory
Purushottam Kar
01:30:30 · 25 June 2015

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

  1. Https://www.khanacademy.org/math/probability
  2. Http://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/
  3. Https://en.wikipedia.org/wiki/Boole'sinequality
Non-Convex Robust PCA - Part 2
Non-Convex Robust PCA - Part 2
Praneeth Netrapalli
00:47:25 · 25 June 2015

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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