OpenRaiser/Intern-Atlas huggingface dataset:
A VC View of Investing in ML
Authors: Greg Papadopoulos
Venue: KDD
Year: 2016

We are seeing a remarkable watershed in the application of data science across markets and industries. A trifecta of advances in algorithms, cheap cycles, and the capture of networked data from everywhere are no doubt the catalysts. The results for many are continuous improvements in efficiencies, and for some are a fundamental re-imagination and disruption of just about every industry. This talk will give examples we are seeing (and funding!) for the latter, and then focus on our views of the ecosystem of value-from-data infrastructure and end-application companies. A big question is whether the enormous collective advances in tools, techniques and education are in-fact converting would-be differentiated products into democratized features used everywhere. We'll follow the value and make our own predictions on future as ML as a business.
Scalable Learning of Graphical Models
Authors: François Petitjean, Geoffrey I. Webb
Venue: KDD
Year: 2016

From understanding the structure of data, to classification and topic modeling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modeling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialized fields including graph theory, statistics, pattern mining and graphical modeling.
Singapore in Motion: Insights on Public Transport Service Level Through Farecard and Mobile Data Analytics
Authors: Hasan Poonawala, Vinay Kolar, Sebastien Blandin, Laura Wynter, Sambit Sahu
Venue: KDD
Year: 2016

Given the changing dynamics of mobility patterns and rapid growth of cities, transport agencies seek to respond more rapidly to needs of the public with the goal of offering an effective and competitive public transport system. A more data-centric approach for transport planning is part of the evolution of this process. In particular, the vast penetration of mobile phones provides an opportunity to monitor and derive insights on transport usage. Real time and historical analyses of such data can give a detailed understanding of mobility patterns of people and also suggest improvements to current transit systems. On its own, however, mobile geolocation data has a number of limitations. We thus propose a joint telco-and-farecard-based learning approach to understanding urban mobility. The approach enhances telecommunications data by leveraging it jointly with other sources of real-time data. The approach is illustrated on the First- and last-mile problem as well as route choice estimation within a densely-connected train network.
Leveraging Propagation for Data Mining: Models, Algorithms and Applications
Authors: B. Aditya Prakash, Naren Ramakrishnan
Venue: KDD
Year: 2016

Can we infer if a user is sick from her tweet? How do opinions get formed in online forums? Which people should we immunize to prevent an epidemic as fast as possible? How do we quickly zoom out of a graph? Graphs---also known as networks---are powerful tools for modeling processes and situations of interest in real life domains of social systems, cyber-security, epidemiology, and biology. They are ubiquitous, from online social networks, gene-regulatory networks, to router graphs.
Robust Large-Scale Machine Learning in the Cloud
Authors: Steffen Rendle, Dennis Fetterly, Eugene J. Shekita, Bor-Yiing Su
Venue: KDD
Year: 2016

The convergence behavior of many distributed machine learning (ML) algorithms can be sensitive to the number of machines being used or to changes in the computing environment. As a result, scaling to a large number of machines can be challenging. In this paper, we describe a new scalable coordinate descent (SCD) algorithm for generalized linear models whose convergence behavior is always the same, regardless of how much SCD is scaled out and regardless of the computing environment. This makes SCD highly robust and enables it to scale to massive datasets on low-cost commodity servers. Experimental results on a real advertising dataset in Google are used to demonstrate SCD's cost effectiveness and scalability. Using Google's internal cloud, we show that SCD can provide near linear scaling using thousands of cores for 1 trillion training examples on a petabyte of compressed data. This represents 10,000x more training examples than the 'large-scale' Netflix prize dataset. We also show that SCD can learn a model for 20 billion training examples in two hours for about $10.
ABRA: Approximating Betweenness Centrality in Static and Dynamic Graphs with Rademacher Averages
Authors: Matteo Riondato, Eli Upfal
Venue: KDD
Year: 2016

We present ABRA, a suite of algorithms to compute and maintain probabilistically-guaranteed, high-quality, approximations of the betweenness centrality of all nodes (or edges) on both static and fully dynamic graphs. Our algorithms use progressive random sampling and their analysis rely on Rademacher averages and pseudodimension, fundamental concepts from statistical learning theory. To our knowledge, this is the first application of these concepts to the field of graph analysis. Our experimental results show that ABRA is much faster than exact methods, and vastly outperforms, in both runtime and number of samples, state-of-the-art algorithms with the same quality guarantees.
Sampling of Attributed Networks from Hierarchical Generative Models
Authors: Pablo Robles-Granda, Sebastián Moreno, Jennifer Neville
Venue: KDD
Year: 2016

Network sampling is a widely used procedure in social network analysis where a random network is sampled from a generative network model (GNM). Recently proposed GNMs, allow generation of networks with more realistic structural characteristics than earlier ones. This facilitates tasks such as hypothesis testing and sensitivity analysis. However, sampling of networks with correlated vertex attributes remains a challenging problem. While the recent work of \cite{Pfeiffer:14} has provided a promising approach for attributed-network sampling, the approach was developed for use with relatively simple GNMs and does not work well with more complex hierarchical GNMs (which can model the range of characteristics and variation observed in real world networks more accurately). In contrast to simple GNMs where the probability mass is spread throughout the space of edges more evenly, hierarchical GNMs concentrate the mass to smaller regions of the space to reflect dependencies among edges in the network---this produces more realistic network characteristics, but also makes it more difficult to identify candidate networks from the sampling space.
Dynamic and Robust Wildfire Risk Prediction System: An Unsupervised Approach
Authors: Mahsa Salehi, Laura Irina Rusu, Timothy M. Lynar, Anna Phan
Venue: KDD
Year: 2016

Ability to predict the risk of damaging events (e.g. wildfires) is crucial in helping emergency services in their decision making processes, to mitigate and reduce the impact of such events. Today, wildfire rating systems have been in operation extensively in many countries around the world to estimate the danger of wildfires. In this paper we propose a data-driven approach to predict wildfire risk using weather data. We show how we address the inherent challenge arising due to the temporal dynamicity of weather data. Weather observations naturally change in time, with finer-scale variation (e.g. stationary day or night) or large variations (nonstationary day or night), and this determines a temporal variation of the predicted wildfire danger. We show how our dynamic wildfire danger prediction model addresses the aforementioned challenge using context-based anomaly detection techniques. We call our predictive model a Context-Based Fire Risk (CBFR) model. The advantage of our model is that it maintains multiple historical models for different temporal variations (e.g. day versus night), and uses ensemble learning techniques to predict wildfire risk with high accuracy. In addition, it is completely unsupervised and does not rely on expert knowledge, which makes it flexible and easily applied to any region of interest. Our CBFR model is also scalable and can potentially be parallelised to speed up computation. We have considered multiple wildfire locations in the Blue Mountains, Australia as a case study, and compared the results of our system with the existing well-established Australian wildfire rating system. The experimental results show that our predictive model has a substantially higher accuracy in predicting wildfire risk, which makes it an effective model to supplement the operational Australian wildfire rating system.
EMBERS AutoGSR: Automated Coding of Civil Unrest Events
Authors: Parang Saraf, Naren Ramakrishnan
Venue: KDD
Year: 2016

We describe the EMBERS AutoGSR system that conducts automated coding of civil unrest events from news articles published in multiple languages. The nuts and bolts of the AutoGSR system constitute an ecosystem of filtering, ranking, and recommendation models to determine if an article reports a civil unrest event and, if so, proceed to identify and encode specific characteristics of the civil unrest event such as the when, where, who, and why of the protest. AutoGSR is a deployed system for the past 6 months continually processing data 24x7 in languages such as Spanish, Portuguese, English and encoding civil unrest events in 10 countries of Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. We demonstrate the superiority of AutoGSR over both manual approaches and other state-of-the-art encoding systems for civil unrest.
Bayesian Optimization and Embedded Learning Systems
Authors: Jeff Schneider
Venue: KDD
Year: 2016

An important property of embedded learning systems is the ever-changing environment they create for all algorithms operating in the system. Optimizing the performance of those algorithms becomes a perpetual on-line activity rather than a one-off task. I will review some of these challenges in autonomous vehicles. I will discuss Bayesian optimization methods and their application in robotics and scientific applications, focusing on scaling up the dimensionality and managing multi-fidelity evaluations. I will finish with lessons learned and thoughts on future directions as these methods move into embedded systems.
CNTK: Microsoft's Open-Source Deep-Learning Toolkit
Authors: Frank Seide, Amit Agarwal
Venue: KDD
Year: 2016

This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux. CNTK is a powerful computation-graph based deep-learning toolkit for training and evaluating deep neural networks. Microsoft product groups use CNTK, for example to create the Cortana speech models and web ranking. CNTK supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads, also in combination. Popular network types are supported either natively (convolution) or can be described as a CNTK configuration (LSTM, sequence-to-sequence). CNTK scales to multiple GPU servers and is designed around efficiency. The tutorial will give an overview of CNTK's general architecture and describe the specific methods and algorithms used for automatic differentiation, recurrent-loop inference and execution, memory sharing, on-the-fly randomization of large corpora, and multi-server parallelization. We will then show how typical uses looks like for relevant tasks like image recognition, sequence-to-sequence modeling, and speech recognition.
Accelerating the Race to Autonomous Cars
Authors: Danny Shapiro
Venue: KDD
Year: 2016

Every automaker is working on driver assistance systems and self-driving cars. Conventional computer vision used for ADAS is reaching its threshold because it is impossible to write code for every possible scenario as a vehicle navigates. In order to develop a truly autonomous car, deep learning and artificial intelligence are required. With deep learning, the vehicle can be trained to have super human levels of perception, driving safer than anyone on the road. An end-to-end artificial intelligence platform based on supercomputers in the cloud and in the vehicle enables cars to get smarter and smarter. Coupled with an extensive software development kit with vision and AI libraries and software modules, automakers, tier 1s, and startups can build scalable systems from ADAS to full autonomy.
Graph Wavelets via Sparse Cuts
Authors: Arlei Silva, Xuan-Hong Dang, Prithwish Basu, Ambuj K. Singh, Ananthram Swami
Venue: KDD
Year: 2016

Modeling information that resides on vertices of large graphs is a key problem in several real-life applications, ranging from social networks to the Internet-of-things. Signal Processing on Graphs and, in particular, graph wavelets can exploit the intrinsic smoothness of these datasets in order to represent them in a compact and accurate manner. However, how to discover wavelet bases that capture the geometry of the data with respect to the signal as well as the graph structure remains an open problem. In this paper, we study the problem of computing graph wavelet bases via sparse cuts in order to produce low-dimensional encodings of data-driven bases. This problem is connected to known hard problems in graph theory (e.g. multiway cuts) and thus requires an efficient heuristic. We formulate the basis discovery task as a relaxation of a vector optimization problem, which leads to an elegant solution as a regularized eigenvalue computation. Moreover, we propose several strategies in order to scale our algorithm to large graphs. Experimental results show that the proposed algorithm can effectively encode both the graph structure and signal, producing compressed and accurate representations for vertex values in a wide range of datasets (e.g. sensor and gene networks) and significantly outperforming the best baseline.
Question Independent Grading using Machine Learning: The Case of Computer Program Grading
Authors: Gursimran Singh, Shashank Srikant, Varun Aggarwal
Venue: KDD
Year: 2016

Learning supervised models to grade open-ended responses is an expensive process. A model has to be trained for every prompt/question separately, which in turn requires graded samples. In automatic programming evaluation specifically, the focus of this work, this issue is amplified. The models have to be trained not only for every question but also for every language the question is offered in. Moreover, the availability and time taken by experts to create a labeled set of programs for each question is a major bottleneck in scaling such a system. We address this issue by presenting a method to grade computer programs which requires no manually assigned labeled samples for grading responses to a new, unseen question. We extend our previous work [25] wherein we introduced a grammar of features to learn question specific models. In this work, we propose a method to transform those features into a set of features that maintain their structural relation with the labels across questions. Using these features we learn one supervised model, across questions for a given language, which can then be applied to an ungraded response to an unseen question. We show that our method rivals the performance of both, question specific models and the consensus among human experts while substantially outperforming extant ways of evaluating codes. We demonstrate the system single s value by deploying it to grade programs in a high stakes assessment. The learning from this work is transferable to other grading tasks such as math question grading and also provides a new variation to the supervised learning approach.
Large-Scale Machine Learning at Verizon: Theory and Applications
Authors: Ashok Srivastava
Venue: KDD
Year: 2016

This talk will cover recent innovations in large-scale machine learning and their applications on massive, real-world data sets at Verizon. These applications power new revenue generating products and services for the company and are hosted on a massive computing and storage platform known as Orion. We will discuss the architecture of Orion and the underlying algorithmic framework. We will also cover some of the real-world aspects of building a new organization dedicated to creating new product lines based on data science.
TRIÈST: Counting Local and Global Triangles in Fully-Dynamic Streams with Fixed Memory Size
Authors: Lorenzo De Stefani, Alessandro Epasto, Matteo Riondato, Eli Upfal
Venue: KDD
Year: 2016

We present TRIEST, a suite of one-pass streaming algorithms to compute unbiased, low-variance, high-quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions. Our algorithms use reservoir sampling and its variants to exploit the user-specified memory space at all times. This is in contrast with previous approaches, which require hard-to-choose parameters (e.g., a fixed sampling probability) and offer no guarantees on the amount of memory they use. We analyze the variance of the estimations and show novel concentration bounds for these quantities. Our experimental results on very large graphs demonstrate that TRIEST outperforms state-of-the-art approaches in accuracy and exhibits a small update time.
Scalable Partial Least Squares Regression on Grammar-Compressed Data Matrices
Authors: Yasuo Tabei, Hiroto Saigo, Yoshihiro Yamanishi, Simon J. Puglisi
Venue: KDD
Year: 2016

With massive high-dimensional data now commonplace in research and industry, there is a strong and growing demand for more scalable computational techniques for data analysis and knowledge discovery. Key to turning these data into knowledge is the ability to learn statistical models with high interpretability. Current methods for learning statistical models either produce models that are not interpretable or have prohibitive computational costs when applied to massive data. In this paper we address this need by presenting a scalable algorithm for partial least squares regression (PLS), which we call compression-based PLS (cPLS), to learn predictive linear models with a high interpretability from massive high-dimensional data. We propose a novel grammar-compressed representation of data matrices that supports fast row and column access while the data matrix is in a compressed form. The original data matrix is grammar-compressed and then the linear model in PLS is learned on the compressed data matrix, which results in a significant reduction in working space, greatly improving scalability. We experimentally test cPLS on its ability to learn linear models for classification, regression and feature extraction with various massive high-dimensional data, and show that cPLS performs superiorly in terms of prediction accuracy, computational efficiency, and interpretability.
Compute Job Memory Recommender System Using Machine Learning
Authors: Taraneh Taghavi, Maria Lupetini, Yaron Kretchmer
Venue: KDD
Year: 2016

This paper presents a machine learning approach to predict the amount of compute memory needed by jobs which are submitted to Load Sharing Facility (LSF® ) with a high level of accuracy. LSF® is the compute resource manager and job scheduler for Qualcomm chip design process. It schedules the jobs based on available resources: CPU, memory, storage, and software licenses. Memory is one of the key resources and its proper utilization leads to a substantial improvement in saving machine resources which in turn results in a significant reduction in overall job pending time. In addition, efficient memory utilization helps to reduce the operations cost by decreasing the number of servers needed for the end-to-end design process. In this paper, we explored a suite of statistical and machine learning techniques to develop a Compute Memory Recommender System for the Qualcomm chip design process with over 90% accuracy in predicting the amount of memory a job needs. Moreover, it demonstrates the potential to significantly reduce job pending time.
Scalable Time-Decaying Adaptive Prediction Algorithm
Authors: Yinyan Tan, Zhe Fan, Guilin Li, Fangshan Wang, Zhengbing Li, Shikai Liu, Qiuling Pan, Eric P. Xing, Qirong Ho
Venue: KDD
Year: 2016

Online learning is used in a wide range of real applications, e.g., predicting ad click-through rates (CTR) and personalized recommendations. Based on the analysis of users' behaviors in Video-On-Demand (VoD) recommender systems,we discover that the most recent users' actions can better reflect users' current intentions and preferences. Under this observation, we thereby propose a novel time-decaying online learning algorithm derived from the state-of-the-art FTRL-proximal algorithm, called Time-Decaying Adaptive Prediction (TDAP) algorithm.
Just One More: Modeling Binge Watching Behavior
Authors: William Trouleau, Azin Ashkan, Weicong Ding, Brian Eriksson
Venue: KDD
Year: 2016

Easy accessibility can often lead to over-consumption, as seen in food and alcohol habits. On video on-demand (VOD) services, this has recently been referred to as binge watching, where potentially entire seasons of TV shows are consumed in a single viewing session. While a user viewership model may reveal this binging behavior, creating an accurate model has several challenges, including censored data, deviations in the population, and the need to consider external influences on consumption habits. In this paper, we introduce a novel statistical mixture model that incorporates these factors and presents a first of its kind characterization of viewer consumption behavior using a real-world dataset that includes playback data from a VOD service. From our modeling, we tackle various predictive tasks to infer the consumption decisions of a user in a viewing session, including estimating the number of episodes they watch and classifying if they continue watching another episode. Using these insights, we then identify binge watching sessions based on deviation from normal viewing behavior. We observe different types of binging behavior, that binge watchers often view certain content out-of-order, and that binge watching is not a consistent behavior among our users. These insights and our findings have application in VOD revenue generation, consumer health applications, and customer retention analysis.