OpenRaiser/Intern-Atlas huggingface dataset:
Big Data System for Analyzing Risky Procurement Entities
Authors: Amit Dhurandhar, Bruce Graves, Rajesh Kumar Ravi, Gopikrishnan Maniachari, Markus Ettl
Venue: KDD
Year: 2015

An accredited biennial 2014 study by the Association of Certified Fraud Examiners claims that on average 5% of a company's revenue is lost because of unchecked fraud every year. The reason for such heavy losses are that it takes around 18 months for a fraud to be caught and audits catch only 3% of the actual fraud. This begs the need for better tools and processes to be able to quickly and cheaply identify potential malefactors. In this paper, we describe a robust tool to identify procurement related fraud/risk, though the general design and the analytical components could be adapted to detecting fraud in other domains. Besides analyzing standard transactional data, our solution analyzes multiple public and private data sources leading to wider coverage of fraud types than what generally exists in the marketplace. Moreover, our approach is more principled in the sense that the learning component, which is based on investigation feedback has formal guarantees. Though such a tool is ever evolving, a deployment of this tool over the past 12 months has found many interesting cases from compliance risk and fraud point of view across more than 150 countries and 65000+ vendors, increasing the number of true positives found by over 80\% compared with other state-of-the-art tools that the domain experts were previously using.
Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams
Authors: Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J. Smola, Le Song
Venue: KDD
Year: 2015

Clusters in document streams, such as online news articles, can be induced by their textual contents, as well as by the temporal dynamics of their arriving patterns. Can we leverage both sources of information to obtain a better clustering of the documents, and distill information that is not possible to extract using contents only? In this paper, we propose a novel random process, referred to as the Dirichlet-Hawkes process, to take into account both information in a unified framework. A distinctive feature of the proposed model is that the preferential attachment of items to clusters according to cluster sizes, present in Dirichlet processes, is now driven according to the intensities of cluster-wise self-exciting temporal point processes, the Hawkes processes. This new model establishes a previously unexplored connection between Bayesian Nonparametrics and temporal Point Processes, which makes the number of clusters grow to accommodate the increasing complexity of online streaming contents, while at the same time adapts to the ever changing dynamics of the respective continuous arrival time. We conducted large-scale experiments on both synthetic and real world news articles, and show that Dirichlet-Hawkes processes can recover both meaningful topics and temporal dynamics, which leads to better predictive performance in terms of content perplexity and arrival time of future documents.
Probabilistic Modeling of a Sales Funnel to Prioritize Leads
Authors: Brendan Andrew Duncan, Charles Peter Elkan
Venue: KDD
Year: 2015

This paper shows how to learn probabilistic classifiers that model how sales prospects proceed through stages from first awareness to final success or failure. Specifically,we present two models, called DQM for direct qualification model and FFM for full funnel model, that can be used to rank initial leads based on their probability of conversion to a sales opportunity, probability of successful sale, and/or expected revenue. Training uses the large amount of historical data collected by customer relationship management or marketing automation software. The trained models can replace traditional lead scoring systems, which are hand-tuned and therefore error-prone and not probabilistic. DQM and FFM are designed to overcome the selection bias caused by available data being based on a traditional lead scoring system. Experimental results are shown on real sales data from two companies. Features in the training data include demographic and behavioral information about each lead. For both companies, both methods achieve high AUC scores. For one company, they result in a a 307% increase in number of successful sales, as well as a dramatic increase in total revenue. In addition, we describe the results of the DQM method in actual use. These results show that the method has additional benefits that include decreased time needed to qualify leads, and decreased number of calls placed to schedule a product demo. The proposed methods find high-quality leads earlier in the sales process because they focus on features that measure the fit of potential customers with the product being sold, in addition to their behavior.
Data, Knowledge and Discovery: Machine Learning meets Natural Science
Authors: Hugh F. Durrant-Whyte
Venue: KDD
Year: 2015

Increasingly it is data, vast amounts of data, that drives scientific discovery. At the heart of this so-called "fourth paradigm of science" is the rapid development of large scale statistical data fusion and machine learning methods. While these developments in "big data" methods are largely driven by commercial applications such as internet search or customer modelling, the opportunity for applying these to scientific discovery is huge. This talk will describe a number of applied machine learning projects addressing real-world inference problems in physical, life and social science areas. In particular, I will describe a major Science and Industry Endowment Fund (SIEF) project, in collaboration with the NICTA and Macquarie University, looking to apply machine learning techniques to discovery in the natural sciences. This talk will look at the key methods in machine learning that are being applied to the discovery process, especially in areas like geology, ecology and biological discovery.
Beyond Triangles: A Distributed Framework for Estimating 3-profiles of Large Graphs
Authors: Ethan R. Elenberg, Karthikeyan Shanmugam, Michael Borokhovich, Alexandros G. Dimakis
Venue: KDD
Year: 2015

We study the problem of approximating the 3-profile of a large graph. 3-profiles are generalizations of triangle counts that specify the number of times a small graph appears as an induced subgraph of a large graph. Our algorithm uses the novel concept of 3-profile sparsifiers: sparse graphs that can be used to approximate the full 3-profile counts for a given large graph. Further, we study the problem of estimating local and ego 3-profiles, two graph quantities that characterize the local neighborhood of each vertex of a graph. Our algorithm is distributed and operates as a vertex program over the GraphLab PowerGraph framework. We introduce the concept of edge pivoting which allows us to collect 2-hop information without maintaining an explicit 2-hop neighborhood list at each vertex. This enables the computation of all the local 3-profiles in parallel with minimal communication. We test our implementation in several experiments scaling up to 640 cores on Amazon EC2. We find that our algorithm can estimate the 3-profile of a graph in approximately the same time as triangle counting. For the harder problem of ego 3-profiles, we introduce an algorithm that can estimate profiles of hundreds of thousands of vertices in parallel, in the timescale of minutes.
Online Topic-based Social Influence Analysis for the Wimbledon Championships
Authors: Varun R. Embar, Indrajit Bhattacharya, Vinayaka Pandit, Roman Vaculín
Venue: KDD
Year: 2015

Various industries are turning to social media to identify key influencers on topics of interest. Following this trend, the All England Lawn Tennis and Croquet Club (AELTC) is keen to analyze the `social pulse' around the famous Wimbledon Championships. IBM developed and deployed social influence analysis capability for AELTC during the 2014 edition of the Championship. The design and implementation of influence analysis technology in the real world involves several challenges. In this paper, we define various functional and usability criteria that social influence scores should satisfy, and propose a multi-dimensional definition of influence that satisfies these criteria. We highlight the need to identify both all-time influencers and recent influencers, and track user influences over multiple time-scales for this purpose. We also stress the importance of aspect-specific influence analysis, and investigate an approach that uses an aspect hierarchy that annotates tweets with topics or aspects before analyzing them for influence. We also describe interesting insights discovered by our tool and the lessons that we learnt from this engagement.
Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions
Authors: Ronen Feldman, Oded Netzer, Aviv Peretz, Binyamin Rosenfeld
Venue: KDD
Year: 2015

We present an end-to-end text mining methodology for relation extraction of adverse drug reactions (ADRs) from medical forums on the Web. Our methodology is novel in that it combines three major characteristics: (i) an underlying concept of using a head-driven phrase structure grammar (HPSG) based parser; (ii) domain-specific relation patterns, the acquisition of which is done primarily using unsupervised methods applied to a large, unlabeled text corpus; and (iii) automated post-processing algorithms for enhancing the set of extracted relations. We empirically demonstrate the ability of our proposed approach to predict ADRs prior to their reporting by the Food and Drug Administration (FDA). Put differently, we put our approach to a predictive test by demonstrating that our methodology can credibly point to ADRs that were not uncovered in clinical trials for evaluating new drugs that come to market but were only reported later on by the FDA as a label change.
A Clustering-Based Framework to Control Block Sizes for Entity Resolution
Authors: Jeffrey Fisher, Peter Christen, Qing Wang, Erhard Rahm
Venue: KDD
Year: 2015

Entity resolution (ER) is a common data cleaning task that involves determining which records from one or more data sets refer to the same real-world entities. Because a pairwise comparison of all records scales quadratically with the number of records in the data sets to be matched, it is common to use blocking or indexing techniques to reduce the number of comparisons required. These techniques split the data sets into blocks and only records within blocks are compared with each other. Most existing blocking techniques do not provide control over the size of the generated blocks, despite this control being important in many practical applications of ER, such as privacy-preserving record linkage and real-time ER. We propose two novel hierarchical clustering approaches which can generate blocks within a specified size range, and we present a penalty function which allows control of the trade-off between block quality and block size in the clustering process. We evaluate our techniques on three real-world data sets and compare them against three baseline approaches. The results show our proposed techniques perform well on the measures of pairs completeness and reduction ratio compared to the baseline approaches, while also satisfying the block size restrictions.
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression
Authors: Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola
Venue: KDD
Year: 2015

We present a new solution to the ``ecological inference'' problem, of learning individual-level associations from aggregate data. This problem has a long history and has attracted much attention, debate, claims that it is unsolvable, and purported solutions. Unlike other ecological inference techniques, our method makes use of unlabeled individual-level data by embedding the distribution over these predictors into a vector in Hilbert space. Our approach relies on recent learning theory results for distribution regression, using kernel embeddings of distributions. Our novel approach to distribution regression exploits the connection between Gaussian process regression and kernel ridge regression, giving us a coherent, Bayesian approach to learning and inference and a convenient way to include prior information in the form of a spatial covariance function. Our approach is highly scalable as it relies on FastFood, a randomized explicit feature representation for kernel embeddings. We apply our approach to the challenging political science problem of modeling the voting behavior of demographic groups based on aggregate voting data. We consider the 2012 US Presidential election, and ask: what was the probability that members of various demographic groups supported Barack Obama, and how did this vary spatially across the country? Our results match standard survey-based exit polling data for the small number of states for which it is available, and serve to fill in the large gaps in this data, at a much higher degree of granularity.
Data-Driven Product Innovation
Authors: Xin Fu, Hernán Asorey
Venue: KDD
Year: 2015

Data Science is an increasingly popular area of Knowledge Discovery and Data Mining. Leading consumer Web companies such as Amazon, Facebook, eBay, Google and LinkedIn, as well as B2B companies like Salesforce, possess Petabytes of data. Through effective mining of this data, they create products and services that benefit millions of users and generate tremendous amount of business value. It is widely acknowledged that Data Scientists play key roles in the creation of these products, from pattern identification, idea generation and product prototyping to experiment design and launch decisions. Nonetheless, they also face common challenges, such as the gap between creating a prototype and turning it into a scalable product, or the frustration of generating innovative product ideas that do not get adopted. Organizers of this tutorial have many years of experience leading Data Science teams in some of the most successful consumer Web companies. In this tutorial, we introduce the framework that we created to nurture data-driven product innovations. The core of this framework is the focus on scale and impact - we take the audience through a discussion on how to balance between velocity and scale, between product innovation and product operation, and between theoretical research and practical impact. We also share some guidelines for successful data-driven product innovation with real examples from our experiences.
Using Local Spectral Methods to Robustify Graph-Based Learning Algorithms
Authors: David F. Gleich, Michael W. Mahoney
Venue: KDD
Year: 2015

Graph-based learning methods have a variety of names including semi-supervised and transductive learning. They typically use a diffusion to propagate labels from a small set of nodes with known class labels to the remaining nodes of the graph. While popular, these algorithms, when implemented in a straightforward fashion, are extremely sensitive to the details of the graph construction. Here, we provide four procedures to help make them more robust: recognizing implicit regularization in the diffusion, using a scalable push method to evaluate the diffusion, using rank-based rounding, and densifying the graph through a matrix polynomial. We study robustness with respect to the details of graph constructions, errors in node labeling, degree variability, and a variety of other real-world heterogeneities, studying these methods through a precise relationship with mincut problems. For instance, the densification strategy explicitly adds new weighted edges to a sparse graph. We find that this simple densification creates a graph where multiple diffusion methods are robust to several types of errors. This is demonstrated by a study with predicting product categories from an Amazon co-purchasing network.
Diffusion in Social and Information Networks: Research Problems, Probabilistic Models and Machine Learning Methods
Authors: Manuel Gomez-Rodriguez, Le Song
Venue: KDD
Year: 2015

In recent years, there has been an increasing effort on developing realistic models, and learning and inference algorithms to understand, predict, and influence diffusion over networks. This has been in part due to the increasing availability and granularity of large-scale diffusion data, which, in principle, allows for understanding and modeling not only macroscopic diffusion but also microscopic (node-level) diffusion. To this aim, a bottom-up approach has been typically considered, which starts by considering how particular ideas, pieces of information, products, or, more generally, contagions spread locally from node to node apparently at random to later produce global, macroscopic patterns at a network level.However, this bottom-up approach also raises significant modeling, algorithmic and computational challenges which require leveraging methods from machine learning, probabilistic modeling, temporal point processes and graph theory, as well as the nascent field of network science. In this tutorial, we will present several diffusion models designed for fine-grained large-scale diffusion and social event data, present some canonical research problem in the context of diffusion, and introduce state-of-the-art algorithms to solve some of these problems, in particular, network estimation, influence estimation and control, and rumor source identification.
Instance Weighting for Patient-Specific Risk Stratification Models
Authors: Jen J. Gong, Thoralf M. Sundt, James D. Rawn, John V. Guttag
Venue: KDD
Year: 2015

Accurate risk models for adverse outcomes can provide important input to clinical decision-making. Surprisingly, one of the main challenges when using machine learning to build clinically useful risk models is the small amount of data available. Risk models need to be developed for specific patient populations, specific institutions, specific procedures, and specific outcomes. With each exclusion criterion, the amount of relevant training data decreases, until there is often an insufficient amount to learn an accurate model. This difficulty is compounded by the large class imbalance that is often present in medical applications.
Gender and Interest Targeting for Sponsored Post Advertising at Tumblr
Authors: Mihajlo Grbovic, Vladan Radosavljevic, Nemanja Djuric, Narayan Bhamidipati, Ananth Nagarajan
Venue: KDD
Year: 2015

As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity. Advertisers can tell their story through images, animation, text, music, video, and more, and can promote that content by sponsoring it to appear as an advertisement in the users' live feeds. In this paper, we present a framework that enabled two of the key targeted advertising components for Tumblr, gender and interest targeting. We describe the main challenges encountered during the development of the framework, which include the creation of a ground truth for training gender prediction models, as well as mapping Tumblr content to a predefined interest taxonomy. For purposes of inferring user interests, we propose a novel semi-supervised neural language model for categorization of Tumblr content (i.e., post tags and post keywords). The model was trained on a large-scale data set consisting of $6.8$ billion user posts, with a very limited amount of categorized keywords, and was shown to have superior performance over the baseline approaches. We successfully deployed gender and interest targeting capability in Yahoo production systems, delivering inference for users that covers more than 90% of daily activities on Tumblr. Online performance results indicate advantages of the proposed approach, where we observed 20% increase in user engagement with sponsored posts in comparison to untargeted campaigns.
Mining Administrative Data to Spur Urban Revitalization
Authors: Ben Green, Alejandra Caro, Matthew Conway, Robert Manduca, Tom Plagge, Abby Miller
Venue: KDD
Year: 2015

After decades of urban investment dominated by sprawl and outward growth, municipal governments in the United States are responsible for the upkeep of urban neighborhoods that have not received sufficient resources or maintenance in many years. One of city governments' biggest challenges is to revitalize decaying neighborhoods given only limited resources. In this paper, we apply data science techniques to administrative data to help the City of Memphis, Tennessee improve distressed neighborhoods. We develop new methods to efficiently identify homes in need of rehabilitation and to predict the impacts of potential investments on neighborhoods. Our analyses allow Memphis to design neighborhood-improvement strategies that generate greater impacts on communities. Since our work uses data that most US cities already collect, our models and methods are highly portable and inexpensive to implement. We also discuss the challenges we encountered while analyzing government data and deploying our tools, and highlight important steps to improve future data-driven efforts in urban policy.
Network Lasso: Clustering and Optimization in Large Graphs
Authors: David Hallac, Jure Leskovec, Stephen P. Boyd
Venue: KDD
Year: 2015

Convex optimization is an essential tool for modern data analysis, as it provides a framework to formulate and solve many problems in machine learning and data mining. However, general convex optimization solvers do not scale well, and scalable solvers are often specialized to only work on a narrow class of problems. Therefore, there is a need for simple, scalable algorithms that can solve many common optimization problems. In this paper, we introduce the network lasso, a generalization of the group lasso to a network setting that allows for simultaneous clustering and optimization on graphs. We develop an algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in a distributed and scalable manner, which allows for guaranteed global convergence even on large graphs. We also examine a non-convex extension of this approach. We then demonstrate that many types of problems can be expressed in our framework. We focus on three in particular --- binary classification, predicting housing prices, and event detection in time series data --- comparing the network lasso to baseline approaches and showing that it is both a fast and accurate method of solving large optimization problems.