Mass Estimation: Enabling density-based or distance-based algorithms to do what they cannot do

Kai Ming Ting

This tutorial provides an overview of mass estimation, an alternative data modelling mechanism to density estimation; and details how it can overcome fundamental weaknesses of density-based or distance-based algorithms to enable them to do what they cannot do previously.

Mass estimation is attractive because the basic measure, mass, is not only more fundamental than density, but also more versatile---mass can be used to do density estimation, as a means for subspace selection and to find multi-dimensional median, and can be extended to measure dissimilarity of any two points. Example advantages of mass over density or distance are given as follows:

  • DEMass--Density estimator based on mass--runs orders of magnitude faster than kernel and kNN density estimators
  • Mass has been used, in place of density, as an effective means for subspace selection.
  • Half-space mass is the maximally robust and efficient method to find multi-dimensional median. Existing methods such as data depth are either less robust or computationally more expensive.
  • Simply replacing mass-based dissimilarity (a data dependent measure) with distance measure (a data independent measure) overcomes key weaknesses of density-based and distance-based methods in clustering, anomaly detection, information retrieval and classification.

This tutorial draws upon recent work on mass estimation and previous work which was also mass- based but was incorrectly categorised as density-based.

Tutorial homepage

Recent Advances in Distributed Machine Learning

Taifeng Wang, Wei Chen

In recent years, artificial intelligence has demonstrated its power in many important applications. Besides the novel machine learning algorithms (e.g., deep neural networks), their distributed implementations play a very critical role in these successes. In this tutorial, we will first review popular machine learning models and their corresponding optimization techniques. Second, we will introduce different ways of parallelizing machine learning algorithms, i.e., data parallelism, model parallelism, synchronous parallelism, asynchronous parallelism, and so on, and discuss their theoretical properties, advantages, and limitations. Third, we will discuss some recent research works that try to overcome the limitations of standard parallelization mechanisms, including advanced asynchronous parallelism and new communication and aggregation methods. Finally, we will introduce how to leverage popular distributed machine learning platforms, such as Spark MlLib, DMTK, Tensorflow, to parallelize a given machine learning algorithm, in order to give the audience some practical guidelines on this topic.

Tutorial homepage

Bayesian Nets from the ground up

Aish Fenton

In this tutorial Aish will take us through Bayesian Networks (i.e. Directed Graphical Models) from the ground up. Many real-world problems in machine learning benefit from building custom models and explicitly stating your distributional assumptions. Graphical models provide a general methodology for doing this. They’ve found success in such diverse settings as bioinformatics, speech processing, and driving parts of Netflix’s recommendation engine. Aish will start from the basics and build up to more advanced concepts, such as bayesian nonparametric extensions. By the end of the tutorial you should have a gasp on the theory underpinning Bayes nets, how to build your own models, and how to infer them.

Tutorial homepage

Deep Approaches to Semantic Matching for Text

Yanyan Lan, Jiafeng Guo

Semantic matching is critical in many text applications, including paraphrase identification, information retrieval, question answering, and machine translation. A variety of machine learning techniques have been developed for various semantic matching tasks, referred to as “learning to match”. Recently, deep learning approaches have shown their effectiveness in this area, and a number of methods have been proposed from different aspects of matching. In this tutorial, we will give a systematic and detailed survey on newly developed deep learning technologies for semantic matching. We will focus on the descriptions on the fundamental problems, as well as the novel solutions from bridging the word level semantic gap and conducting sentence level end-to-end semantic matching. We will also discuss the potential applications and future directions of semantic matching for text.

Tutorial homepage