OpenRaiser/Intern-Atlas huggingface dataset:
Not All Passes Are Created Equal: Objectively Measuring the Risk and Reward of Passes in Soccer from Tracking Data
Authors: Paul Power, Héctor Ruiz, Xinyu Wei, Patrick Lucey
Venue: KDD
Year: 2017

In soccer, the most frequent event that occurs is a pass. For a trained eye, there are a myriad of adjectives which could describe this event (e.g., "majestic pass", "conservative" to "poor-ball"). However, as these events are needed to be coded live and in real-time (most often by human annotators), the current method of grading passes is restricted to the binary labels 0 (unsuccessful) or 1 (successful). Obviously, this is sub-optimal because the quality of a pass needs to be measured on a continuous spectrum (i.e., 0 to 100%) and not a binary value. Additionally, a pass can be measured across multiple dimensions, namely: i) risk -- the likelihood of executing a pass in a given situation, and ii) reward -- the likelihood of a pass creating a chance. In this paper, we show how we estimate both the risk and reward of a pass across two seasons of tracking data captured from a recent professional soccer league with state-of-the-art performance, then showcase various use cases of our deployed passing system.
Mixture Factorized Ornstein-Uhlenbeck Processes for Time-Series Forecasting
Authors: Guo-Jun Qi, Jiliang Tang, Jingdong Wang, Jiebo Luo
Venue: KDD
Year: 2017

Forecasting the future observations of time-series data can be performed by modeling the trend and fluctuations from the observed data. Many classical time-series analysis models like Autoregressive model (AR) and its variants have been developed to achieve such forecasting ability. While they are often based on the white noise assumption to model the data fluctuations, a more general Brownian motion has been adopted that results in Ornstein-Uhlenbeck (OU) process. The OU process has gained huge successes in predicting the future observations over many genres of time series, however, it is still limited in modeling simple diffusion dynamics driven by a single persistent factor that never evolves over time. However, in many real problems, a mixture of hidden factors are usually present, and when and how frequently they appear or disappear are unknown ahead of time. This imposes a challenge that inspires us to develop a Mixture Factorized OU process (MFOUP) to model evolving factors. The new model is able to capture the changing states of multiple mixed hidden factors, from which we can infer their roles in driving the movements of time series. We conduct experiments on three forecasting problems, covering sensor and market data streams. The results show its competitive performance on predicting future observations and capturing evolution patterns of hidden factors as compared with the other algorithms.
MARAS: Signaling Multi-Drug Adverse Reactions
Authors: Xiao Qin, Tabassum Kakar, Susmitha Wunnava, Elke A. Rundensteiner, Lei Cao
Venue: KDD
Year: 2017

There is a growing need for computing-supported methods that facilitate the automated signaling of Adverse Drug Reactions (ADRs) otherwise left undiscovered from the exploding amount of ADR reports filed by patients, medical professionals and drug manufacturers. In this research, we design a Multi-Drug Adverse Reaction Analytics Strategy, called MARAS, to signal severe unknown ADRs triggered by the usage of a combination of drugs, also known as Multi-Drug Adverse Reactions (MDAR). First, MARAS features an efficient signal generation algorithm based on association rule learning that extracts non-spurious MDAR associations. Second, MARAS incorporates contextual information to detect drug combinations that are strongly associated with a set of ADRs. It groups related associations into Contextual Association Clusters (CACs) that then avail contextual information to evaluate the significance of the discovered MDAR Associations. Lastly, we use this contextual significance to rank discoveries by their notion of interestingness to signal the most compelling MDARs. To demonstrate the utility of MARAS, it is compared with state-of-the-art techniques and evaluated via case studies on datasets collected by U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).
Collecting and Analyzing Millions of mHealth Data Streams
Authors: Tom Quisel, Luca Foschini, Alessio Signorini, David C. Kale
Venue: KDD
Year: 2017

Players across the health ecosystem are initiating studies of thousands, even millions, of participants to gather diverse types of data, including biomedical, behavioral, and lifestyle in order to advance medical research. These efforts to collect multi-modal data sets on large cohorts coincide with the rise of broad activity and behavior tracking across industries, particularly in healthcare and the growing field of mobile health (mHealth). Government and pharmaceutical sponsored, as well as patient-driven group studies in this arena leverage the ability of mobile technology to continuously track behaviors and environmental factors with minimal participant burden. However, the adoption of mHealth has been constrained by the lack of robust solutions for large-scale data collection in free-living conditions and concerns around data quality. In this work, we describe the infrastructure Evidation Health has developed to collect mHealth data from millions of users through hundreds of different mobile devices and apps. Additionally, we provide evidence of the utility of the data for inferring individual traits pertaining to health, wellness, and behavior. To this end, we introduce and evaluate deep neural network models that achieve high prediction performance without requiring any feature engineering when trained directly on the densely sampled multivariate mHealth time series data. We believe that the present work substantiates both the feasibility and the utility of creating a very large mHealth research cohort, as envisioned by the many large cohort studies currently underway across therapeutic areas and conditions.
Dispatch with Confidence: Integration of Machine Learning, Optimization and Simulation for Open Pit Mines
Authors: Kosta Ristovski, Chetan Gupta, Kunihiko Harada, Hsiu-Khuern Tang
Venue: KDD
Year: 2017

Open pit mining operations require utilization of extremely expensive equipment such as large trucks, shovels and loaders. To remain competitive, mining companies are under pressure to increase equipment utilization and reduce operational costs. The key to this in mining operations is to have sophisticated truck assignment strategies which will ensure that equipment is utilized efficiently with minimum operating cost. To address this problem, we have implemented truck assignment approach which integrates machine learning, linear/integer programming and simulation. Our truck assignment approach takes into consideration the number of trucks and their sizes, shovels and dump locations as well as stochastic activity times during the operations. Machine learning is used to predict probability distributions of equipment activity duration. We have validated the approach using data collected from two open pit mines. Our experimental results show that our approach offers increase of 10% in efficiency. Presented results demonstrate that machine learning can bring significant value to mining industry.
Inferring the Strength of Social Ties: A Community-Driven Approach
Authors: Polina Rozenshtein, Nikolaj Tatti, Aristides Gionis
Venue: KDD
Year: 2017

Online social networks are growing and becoming denser.The social connections of a given person may have very high variability: from close friends and relatives to acquaintances to people who hardly know. Inferring the strength of social ties is an important ingredient for modeling the interaction of users in a network and understanding their behavior. Furthermore, the problem has applications in computational social science, viral marketing, and people recommendation. In this paper we study the problem of inferring the strength of social ties in a given network. Our work is motivated by a recent approach by Sintos et. al [24], which leverages the Strong Triadic Closure} STC principle, a hypothesis rooted in social psychology. To guide our inference process, in addition to the network structure, we also consider as input a collection of tight communities. Those are sets of vertices that we expect to be connected via strong ties. Such communities appear in different situations, e.g., when being part of a community implies a strong connection to one of the existing members. We consider two related problem formalizations that reflect the assumptions of our setting: small number of STC violations and strong-tie connectivity in the input communities. We show that both problem formulations are NP-hard. We also show that one problem formulation is hard to approximate, while for the second we develop an algorithm with approximation guarantee. We validate the proposed method on real-world datasets by comparing with baselines that optimize STC violations and community connectivity separately.
More than the Sum of its Parts: Building Domino Data Lab
Authors: Eduardo Ariño de la Rubia
Venue: KDD
Year: 2017

Industry has always leveraged cutting edge quantitative research techniques. From finance and insurance, to marketing and manufacturing, efficiencies and advantages have been seized through measurement, prediction, and the generation of insights' but never at this scale. Organizations which previously may have employed one or two data scientists are now scaling the work to dozens if not hundreds of practitioners. Where previously only a handful of organizations could boast that they were leveraging machine learning and statistical models, now it's a rarity to find an untouched industry or player. Organizations are now faced with the challenges of empowering, scaling, and measuring this workforce to sustain the transformation to the prediction economy. In this talk, I will discuss how and why we built the Domino Data Lab platform. I will talk about the challenges we faced technologically, organizationally and culturally when bringing a system of record to data science.
"The Leicester City Fairytale?": Utilizing New Soccer Analytics Tools to Compare Performance in the 15/16 & 16/17 EPL Seasons
Authors: Héctor Ruiz, Paul Power, Xinyu Wei, Patrick Lucey
Venue: KDD
Year: 2017

The last two years have been somewhat of a rollercoaster for English Premier League (EPL) team Leicester City. In the 2015/16 season, against all odds and logic, they won the league to much fan-fare. Fast-forward nine months later, and they are battling relegation. What could describe this fluctuating form? As soccer is a very complex and strategic game, common statistics (e.g., passes, shots, possession) do not really tell the full story on how a team succeeds and fails. However, using machine learning tools and a plethora of data, it is now possible to obtain some insights into how a team performs. To showcase the utility of these new tools (i.e., expected goal value, expected save value, strategy-plots and passing quality measures), we first analyze the EPL 2015/16 season which a specific emphasis on the champions Leicester City, and then compare it to the current one. Finally, we show how these features can be used to predict future performance.
Matching Restaurant Menus to Crowdsourced Food Data: A Scalable Machine Learning Approach
Authors: Hesam Salehian, Patrick D. Howell, Chul Lee
Venue: KDD
Year: 2017

We study the problem of how to match a formally structured restaurant menu item to a large database of less structured food items that has been collected via crowd-sourcing. At first glance, this problem scenario looks like a typical text matching problem that might possibly be solved with existing text similarity learning approaches. However, due to the unique nature of our scenario and the need for scalability, our problem imposes certain restrictions on possible machine learning approaches that we can employ. We propose a novel, practical, and scalable machine learning solution architecture, consisting of two major steps. First we use a query generation approach, based on a Markov Decision Process algorithm, to reduce the time complexity of searching for matching candidates. That is then followed by a re-ranking step, using deep learning techniques, to meet our required matching quality goals. It is important to note that our proposed solution architecture has already been deployed in a real application system serving tens of millions of users, and shows great potential for practical cases of user-entered text to structured text matching, especially when scalability is crucial.
Similarity Forests
Authors: Saket Sathe, Charu C. Aggarwal
Venue: KDD
Year: 2017

Random forests are among the most successful methods used in data mining because of their extraordinary accuracy and effectiveness. However, their use is primarily limited to multidimensional data because they sample features from the original data set. In this paper, we propose a method for extending random forests to work with any arbitrary set of data objects, as long as similarities can be computed among the data objects. Furthermore, since it is understood that similarity computation between all O(n2) pairs of n objects might be expensive, our method computes only a very small fraction of the O(n2) pairwise similarities between objects to construct the forests. Our results show that the proposed similarity forest approach is very efficient and accurate on a wide variety of data sets. Therefore, this paper significantly extends the applicability of random forest methods to arbitrary data domains. Furthermore, the approach even outperforms traditional random forests on multidimensional data. We show that similarity forests are robust to the noisy similarity values that are ubiquitous in real-world applications. In many practical settings, the similarity values between objects are incompletely specified because of the difficulty in collecting such values. Similarity forests can be used in such cases with straightforward modifications.
Detecting Network Effects: Randomizing Over Randomized Experiments
Authors: Martin Saveski, Jean Pouget-Abadie, Guillaume Saint-Jacques, Weitao Duan, Souvik Ghosh, Ya Xu, Edoardo M. Airoldi
Venue: KDD
Year: 2017

Randomized experiments, or A/B tests, are the standard approach for evaluating the causal effects of new product features, i.e., treatments. The validity of these tests rests on the "stable unit treatment value assumption" (SUTVA), which implies that the treatment only affects the behavior of treated users, and does not affect the behavior of their connections. Violations of SUTVA, common in features that exhibit network effects, result in inaccurate estimates of the causal effect of treatment. In this paper, we leverage a new experimental design for testing whether SUTVA holds, without making any assumptions on how treatment effects may spill over between the treatment and the control group. To achieve this, we simultaneously run both a completely randomized and a cluster-based randomized experiment, and then we compare the difference of the resulting estimates. We present a statistical test for measuring the significance of this difference and offer theoretical bounds on the Type I error rate. We provide practical guidelines for implementing our methodology on large-scale experimentation platforms. Importantly, the proposed methodology can be applied to settings in which a network is not necessarily observed but, if available, can be used in the analysis. Finally, we deploy this design to LinkedIn's experimentation platform and apply it to two online experiments, highlighting the presence of network effects and bias in standard A/B testing approaches in a real-world setting.
Online Ranking with Constraints: A Primal-Dual Algorithm and Applications to Web Traffic-Shaping
Authors: Parikshit Shah, Akshay Soni, Troy Chevalier
Venue: KDD
Year: 2017

We study the online constrained ranking problem motivated by an application to web-traffic shaping: an online stream of sessions arrive in which, within each session, we are asked to rank items. The challenge involves optimizing the ranking in each session so that local vs. global objectives are controlled: within each session one wishes to maximize a reward (local) while satisfying certain constraints over the entire set of sessions (global). A typical application of this setup is that of page optimization in a web portal. We wish to rank items so that not only is user engagement maximized in each session, but also other business constraints (such as the number of views/clicks delivered to various publishing partners) are satisfied. We describe an online algorithm for performing this optimization. A novel element of our approach is the use of linear programming duality and connections to the celebrated Hungarian algorithm. This framework enables us to determine a set of shadow prices for each traffic-shaping constraint that can then be used directly in the final ranking function to assign near-optimal rankings. The (dual) linear program can be solved off-line periodically to determine the prices. At serving time these prices are used as weights to compute weighted rank-scores for the items, and the simplicity of the approach facilitates scalability to web applications. We provide rigorous theoretical guarantees for the performance of our online algorithm and validate our approach using numerical experiments on real web-traffic data from a prominent internet portal.
A Practical Exploration System for Search Advertising
Authors: Parikshit Shah, Ming Yang, Sachidanand Alle, Adwait Ratnaparkhi, Ben Shahshahani, Rohit Chandra
Venue: KDD
Year: 2017

In this paper, we describe an exploration system that was implemented by the search-advertising team of a prominent web-portal to address the cold ads problem. The cold ads problem refers to the situation where, when new ads are injected into the system by advertisers, the system is unable to assign an accurate quality to the ad (in our case, the click probability). As a consequence, the advertiser may suffer from low impression volumes for these cold ads, and the overall system may perform sub-optimally if the click probabilities for new ads are not learnt rapidly. We designed a new exploration system that was adapted to search advertising and the serving constraints of the system. In this paper, we define the problem, discuss the design details of the exploration system, new evaluation criteria, and present the performance metrics that were observed by us.
The Fake vs Real Goods Problem: Microscopy and Machine Learning to the Rescue
Authors: Ashlesh Sharma, Vidyuth Srinivasan, Vishal Kanchan, Lakshminarayanan Subramanian
Venue: KDD
Year: 2017

Counterfeiting of physical goods is a global problem amounting to nearly 7% of world trade. While there have been a variety of overt technologies like holograms and specialized barcodes and covert technologies like taggants and PUFs, these solutions have had a limited impact on the counterfeit market due to a variety of factors - clonability, cost or adoption barriers. In this paper, we introduce a new mechanism that uses machine learning algorithms on microscopic images of physical objects to distinguish between genuine and counterfeit versions of the same product. The underlying principle of our system stems from the idea that microscopic characteristics in a genuine product or a class of products (corresponding to the same larger product line), exhibit inherent similarities that can be used to distinguish these products from their corresponding counterfeit versions. A key building block for our system is a wide-angle microscopy device compatible with a mobile device that enables a user to easily capture the microscopic image of a large area of a physical object. Based on the captured microscopic images, we show that using machine learning algorithms (ConvNets and bag of words), one can generate a highly accurate classification engine for separating the genuine versions of a product from the counterfeit ones; this property also holds for "super-fake" counterfeits observed in the marketplace that are not easily discernible from the human eye. We describe the design of an end-to-end physical authentication system leveraging mobile devices, portable hardware and a cloud-based object verification ecosystem. We evaluate our system using a large dataset of 3 million images across various objects and materials such as fabrics, leather, pills, electronics, toys and shoes. The classification accuracy is more than 98% and we show how our system works with a cellphone to verify the authenticity of everyday objects.
ReasoNet: Learning to Stop Reading in Machine Comprehension
Authors: Yelong Shen, Po-Sen Huang, Jianfeng Gao, Weizhu Chen
Venue: KDD
Year: 2017

Teaching a computer to read and answer general questions pertaining to a document is a challenging yet unsolved problem. In this paper, we describe a novel neural network architecture called the Reasoning Network (ReasoNet) for machine comprehension tasks. ReasoNets make use of multiple turns to effectively exploit and then reason over the relation among queries, documents, and answers. Different from previous approaches using a fixed number of turns during inference, ReasoNets introduce a termination state to relax this constraint on the reasoning depth. With the use of reinforcement learning, ReasoNets can dynamically determine whether to continue the comprehension process after digesting intermediate results, or to terminate reading when it concludes that existing information is adequate to produce an answer. ReasoNets achieve superior performance in machine comprehension datasets, including unstructured CNN and Daily Mail datasets, the Stanford SQuAD dataset, and a structured Graph Reachability dataset.
On Finding Socially Tenuous Groups for Online Social Networks
Authors: Chih-Ya Shen, Liang-Hao Huang, De-Nian Yang, Hong-Han Shuai, Wang-Chien Lee, Ming-Syan Chen
Venue: KDD
Year: 2017

Existing research on finding social groups mostly focuses on dense subgraphs in social networks. However, finding socially tenuous groups also has many important applications. In this paper, we introduce the notion of k-triangles to measure the tenuity of a group. We then formulate a new research problem, Minimum k-Triangle Disconnected Group (MkTG), to find a socially tenuous group from online social networks. We prove that MkTG is NP-Hard and inapproximable within any ratio in arbitrary graphs but polynomial-time tractable in threshold graphs. Two algorithms, namely TERA and TERA-ADV, are designed to exploit graph-theoretical approaches for solving MkTG on general graphs effectively and efficiently. Experimental results on seven real datasets manifest that the proposed algorithms outperform existing approaches in both efficiency and solution quality.
DenseAlert: Incremental Dense-Subtensor Detection in Tensor Streams
Authors: Kijung Shin, Bryan Hooi, Jisu Kim, Christos Faloutsos
Venue: KDD
Year: 2017

Consider a stream of retweet events - how can we spot fraudulent lock-step behavior in such multi-aspect data (i.e., tensors) evolving over time? Can we detect it in real time, with an accuracy guarantee? Past studies have shown that dense subtensors tend to indicate anomalous or even fraudulent behavior in many tensor data, including social media, Wikipedia, and TCP dumps. Thus, several algorithms have been proposed for detecting dense subtensors rapidly and accurately. However, existing algorithms assume that tensors are static, while many real-world tensors, including those mentioned above, evolve over time. We propose DENSESTREAM, an incremental algorithm that maintains and updates a dense subtensor in a tensor stream (i.e., a sequence of changes in a tensor), and DENSESALERT, an incremental algorithm spotting the sudden appearances of dense subtensors. Our algorithms are: (1) Fast and "any time": updates by our algorithms are up to a million times faster than the fastest batch algorithms, (2) Provably accurate: our algorithms guarantee a lower bound on the density of the subtensor they maintain, and (3) Effective: our DENSESALERT successfully spots anomalies in real-world tensors, especially those overlooked by existing algorithms.