OpenRaiser/Intern-Atlas huggingface dataset:
From Prediction to Action: A Closed-Loop Approach for Data-Guided Network Resource Allocation
Authors: Yanan Bao, Huasen Wu, Xin Liu
Venue: KDD
Year: 2016

Machine learning methods have been widely used in modeling and predicting network user experience. In this paper, moving beyond user experience prediction, we propose a closed-loop approach that uses data-generated prediction models to explicitly guide resource allocation for user experience improvement. The closed-loop approach leverages and verifies the causal relation that often exists between certain feature values (e.g., bandwidth) and user experience in computer networks. The approach consists of three components: we train a neural network classifier to predict user experience, utilize the trained neural network classifier as the objective function to allocate network resource, and then evaluate user experience with allocated resource to (in)validate and adjust the original model. Specifically, we propose a dual decomposition algorithm to solve the neural networkbased resource optimization problem, which is complex and non-convex. We further develop an iterative mechanism for classifier optimization. Numerical results show that the dual algorithm reduces the expected number of unsatisfied users by up to 2x compared with the baseline, and the optimized classifier further improves the performance by 50%.
Can You Teach the Elephant to Dance? AKA: Culture Eats Data Science for Breakfast
Authors: Jonathan D. Becher
Venue: KDD
Year: 2016

In the past 20 years, the practical examples of KDD/data mining have become so ubiquitous that it's almost impossible to imagine a new venture that isn't based on data science. Uber, Facebook, 23andMe, Tesla -- they aren't just technology companies; they are data companies. And yet the reality is that these companies are still anomalies. Large, successful companies usually still treat KDD as either an afterthought or as an experiment. It's not core to how they run the business.
Towards Robust and Versatile Causal Discovery for Business Applications
Authors: Giorgos Borboudakis, Ioannis Tsamardinos
Venue: KDD
Year: 2016

Causal discovery algorithms can induce some of the causal relations from the data, commonly in the form of a causal network such as a causal Bayesian network. Arguably however, all such algorithms lack far behind what is necessary for a true business application. We develop an initial version of a new, general causal discovery algorithm called ETIO with many features suitable for business applications. These include (a) ability to accept prior causal knowledge (e.g., taking senior driving courses improves driving skills), (b) admitting the presence of latent confounding factors, (c) admitting the possibility of (a certain type of) selection bias in the data (e.g., clients sampled mostly from a given region), (d) ability to analyze data with missing-by-design (i.e., not planned to measure) values (e.g., if two companies merge and their databases measure different attributes), and (e) ability to analyze data from different interventions (e.g., prior and posterior to an advertisement campaign). ETIO is an instance of the logical approach to integrative causal discovery that has been relatively recently introduced and enables the solution of complex reverse-engineering problems in causal discovery. ETIO is compared against the state-of-the-art and is shown to be more effective in terms of speed, with only a slight degradation in terms of learning accuracy, while incorporating all the features above. The code is available on the mensxmachina.org website.
The Legislative Influence Detector: Finding Text Reuse in State Legislation
Authors: Matthew Burgess, Eugenia Giraudy, Julian Katz-Samuels, Joe Walsh, Derek Willis, Lauren Haynes, Rayid Ghani
Venue: KDD
Year: 2016

State legislatures introduce at least 45,000 bills each year. However, we lack a clear understanding of who is actually writing those bills. As legislators often lack the time and staff to draft each bill, they frequently copy text written by other states or interest groups. However, existing approaches to detect text reuse are slow, biased, and incomplete. Journalists or researchers who want to know where a particular bill originated must perform a largely manual search. Watchdog organizations even hire armies of volunteers to monitor legislation for matches. Given the time-consuming nature of the analysis, journalists and researchers tend to limit their analysis to a subset of topics (e.g. abortion or gun control) or a few interest groups.
Deep Visual-Semantic Hashing for Cross-Modal Retrieval
Authors: Yue Cao, Mingsheng Long, Jianmin Wang, Qiang Yang, Philip S. Yu
Venue: KDD
Year: 2016

Due to the storage and retrieval efficiency, hashing has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval. Cross-modal hashing, which enables efficient retrieval of images in response to text queries or vice versa, has received increasing attention recently. Most existing work on cross-modal hashing does not capture the spatial dependency of images and temporal dynamics of text sentences for learning powerful feature representations and cross-modal embeddings that mitigate the heterogeneity of different modalities. This paper presents a new Deep Visual-Semantic Hashing (DVSH) model that generates compact hash codes of images and sentences in an end-to-end deep learning architecture, which capture the intrinsic cross-modal correspondences between visual data and natural language. DVSH is a hybrid deep architecture that constitutes a visual-semantic fusion network for learning joint embedding space of images and text sentences, and two modality-specific hashing networks for learning hash functions to generate compact binary codes. Our architecture effectively unifies joint multimodal embedding and cross-modal hashing, which is based on a novel combination of Convolutional Neural Networks over images, Recurrent Neural Networks over sentences, and a structured max-margin objective that integrates all things together to enable learning of similarity-preserving and high-quality hash codes. Extensive empirical evidence shows that our DVSH approach yields state of the art results in cross-modal retrieval experiments on image-sentences datasets, i.e. standard IAPR TC-12 and large-scale Microsoft COCO.
Identifying Police Officers at Risk of Adverse Events
Authors: Samuel Carton, Jennifer Helsby, Kenneth Joseph, Ayesha Mahmud, Youngsoo Park, Joe Walsh, Crystal Cody, C. P. T. Estella Patterson, Lauren Haynes, Rayid Ghani
Venue: KDD
Year: 2016

Adverse events between police and the public, such as deadly shootings or instances of racial profiling, can cause serious or deadly harm, damage police legitimacy, and result in costly litigation. Evidence suggests these events can be prevented by targeting interventions based on an Early Intervention System (EIS) that flags police officers who are at a high risk for involvement in such adverse events. Today's EIS are not data-driven and typically rely on simple thresholds based entirely on expert intuition. In this paper, we describe our work with the Charlotte-Mecklenburg Police Department (CMPD) to develop a machine learning model to predict which officers are at risk for an adverse event. Our approach significantly outperforms CMPD's existing EIS, increasing true positives by ~12% and decreasing false positives by ~32%. Our work also sheds light on features related to officer characteristics, situational factors, and neighborhood factors that are predictive of adverse events. This work provides a starting point for police departments to take a comprehensive, data-driven approach to improve policing and reduce harm to both officers and members of the public.
Graphons and Machine Learning: Modeling and Estimation of Sparse Massive Networks
Authors: Jennifer T. Chayes
Venue: KDD
Year: 2016

There are numerous examples of sparse massive networks, in particular the Internet, WWW and online social networks. How do we model and learn these networks? In contrast to conventional learning problems, where we have many independent samples, it is often the case for these networks that we can get only one independent sample. How do we use a single snapshot today to learn a model for the network, and therefore be able to predict a similar, but larger network in the future? In the case of relatively small or moderately sized networks, it's appropriate to model the network parametrically, and attempt to learn these parameters. For massive networks, a non-parametric representation is more appropriate. In this talk, we first review the theory of graphons, developed over the last decade to describe limits of dense graphs, and the more the recent theory describing sparse graphs of unbounded average degree, including power-law graphs. We then show how to use these graphons as non-parametric models for sparse networks. Finally, we show how to get consistent estimators of these non-parametric models, and moreover how to do this in a way that protects the privacy of individuals on the network.
Lifelong Machine Learning and Computer Reading the Web
Authors: Zhiyuan Chen, Estevam R. Hruschka Jr., Bing Liu
Venue: KDD
Year: 2016

This tutorial introduces Lifelong Machine Learning (LML) and Machine Reading. The core idea of LML is to learn continuously and accumulate the learned knowledge, and to use the knowledge to help future learning, which is perhaps the hallmark of human learning and human intelligence. By us- ing prior knowledge seamlessly and effortlessly, we humans can learn without a lot of training data, but current machine learning algorithms tend to need a huge amount of training data. LML aims to mimic this human capability. Machine Reading is a research area with the goal of building systems to read natural language text. Among different approaches employed in Machine Reading, this tutorial focuses on projects and approaches that use the idea of LML. Most current machine learning (ML) algorithms learn in isolation. They are designed to address a specific problem using a single dataset. That is, given a dataset, an ML algorithm is executed on the dataset to build a model. Although this type of isolated learning is very useful, it does not have the ability to accumulate past knowledge and to make use of the knowledge for future learning, which we believe are critical for the future of machine learning and data mining. LML aims to design and develop computational systems and algorithms with this capability, i.e., to learn as humans do in a lifelong manner.
Compressing Convolutional Neural Networks in the Frequency Domain
Authors: Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, Yixin Chen
Venue: KDD
Year: 2016

Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers, hindering many applications such as image and speech recognition on mobile phones and other devices. In this paper, we present a novel net- work architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low-frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low-cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back-propagation. To further reduce model size, we allocate fewer hash buckets to high-frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to better compressed performance than several relevant baselines.
Parallel Dual Coordinate Descent Method for Large-scale Linear Classification in Multi-core Environments
Authors: Wei-Lin Chiang, Mu-Chu Lee, Chih-Jen Lin
Venue: KDD
Year: 2016

Dual coordinate descent method is one of the most effective approaches for large-scale linear classification. However, its sequential design makes the parallelization difficult. In this work, we target at the parallelization in a multi-core environment. After pointing out difficulties faced in some existing approaches, we propose a new framework to parallelize the dual coordinate descent method. The key idea is to make the majority of all operations (gradient calculation here) parallelizable. The proposed framework is shown to be theoretically sound. Further, we demonstrate through experiments that the new framework is robust and efficient in a multi-core environment.
Domain Adaptation in the Absence of Source Domain Data
Authors: Boris Chidlovskii, Stéphane Clinchant, Gabriela Csurka
Venue: KDD
Year: 2016

The overwhelming majority of existing domain adaptation methods makes an assumption of freely available source domain data. An equal access to both source and target data makes it possible to measure the discrepancy between their distributions and to build representations common to both target and source domains. In reality, such a simplifying assumption rarely holds, since source data are routinely a subject of legal and contractual constraints between data owners and data customers. When source domain data can not be accessed, decision making procedures are often available for adaptation nevertheless. These procedures are often presented in the form of classification, identification, ranking etc. rules trained on source data and made ready for a direct deployment and later reuse. In other cases, the owner of a source data is allowed to share a few representative examples such as class means. In this paper we address the domain adaptation problem in real world applications, where the reuse of source domain data is limited to classification rules or a few representative examples. We extend the recent techniques of feature corruption and their marginalization, both in supervised and unsupervised settings. We test and compare them on private and publicly available source datasets and show that significant performance gains can be achieved despite the absence of source data and shortage of labeled target data.
Towards Conversational Recommender Systems
Authors: Konstantina Christakopoulou, Filip Radlinski, Katja Hofmann
Venue: KDD
Year: 2016

People often ask others for restaurant recommendations as a way to discover new dining experiences. This makes restaurant recommendation an exciting scenario for recommender systems and has led to substantial research in this area. However, most such systems behave very differently from a human when asked for a recommendation. The goal of this paper is to begin to reduce this gap. In particular, humans can quickly establish preferences when asked to make a recommendation for someone they do not know. We address this cold-start recommendation problem in an online learning setting. We develop a preference elicitation framework to identify which questions to ask a new user to quickly learn their preferences. Taking advantage of latent structure in the recommendation space using a probabilistic latent factor model, our experiments with both synthetic and real world data compare different types of feedback and question selection strategies. We find that our framework can make very effective use of online user feedback, improving personalized recommendations over a static model by 25% after asking only 2 questions. Our results demonstrate dramatic benefits of starting from offline embeddings, and highlight the benefit of bandit-based explore-exploit strategies in this setting.
Efficient Processing of Network Proximity Queries via Chebyshev Acceleration
Authors: Mustafa Coskun, Ananth Grama, Mehmet Koyutürk
Venue: KDD
Year: 2016

Network proximity is at the heart of a large class of network analytics and information retrieval techniques, including node/ edge rankings, network alignment, and randomwalk based proximity queries, among many others. Owing to its importance, significant effort has been devoted to accelerating iterative processes underlying network proximity computations. These techniques rely on numerical properties of power iterations, as well as structural properties of the networks to reduce the run time of iterative algorithms.
The Evolving Meaning of Information Security
Authors: Whitfield Diffie
Venue: KDD
Year: 2016

When you are developing security systems, new penetration techniques seem to appear as responses to new security measures but in general the flow is the other way around: security exists and evolves because of the evolution of threats. Beginning with the rise of radio in the 20th Century attacks on communication networks have shown two forms: those that go for the big kill --- such as the breaking of Enigma --- and those that assemble small seemingly innocuous leaks of information into a comprehensive understanding of the target's behavior. We will analyze the way in which these trends interact with others to create a situation in which what is possible in security and even the meaning of security in communication networks needs reexamination.
Kam1n0: MapReduce-based Assembly Clone Search for Reverse Engineering
Authors: Steven H. H. Ding, Benjamin C. M. Fung, Philippe Charland
Venue: KDD
Year: 2016

Assembly code analysis is one of the critical processes for detecting and proving software plagiarism and software patent infringements when the source code is unavailable. It is also a common practice to discover exploits and vulnerabilities in existing software. However, it is a manually intensive and time-consuming process even for experienced reverse engineers. An effective and efficient assembly code clone search engine can greatly reduce the effort of this process, since it can identify the cloned parts that have been previously analyzed. The assembly code clone search problem belongs to the field of software engineering. However, it strongly depends on practical nearest neighbor search techniques in data mining and databases. By closely collaborating with reverse engineers and Defence Research and Development Canada (DRDC), we study the concerns and challenges that make existing assembly code clone approaches not practically applicable from the perspective of data mining. We propose a new variant of LSH scheme and incorporate it with graph matching to address these challenges. We implement an integrated assembly clone search engine called Kam1n0. It is the first clone search engine that can efficiently identify the given query assembly function's subgraph clones from a large assembly code repository. Kam1n0 is built upon the Apache Spark computation framework and Cassandra-like key-value distributed storage. A deployed demo system is publicly available. Extensive experimental results suggest that Kam1n0 is accurate, efficient, and scalable for handling large volume of assembly code.
How Machine Learning has Finally Solved Wanamaker's Dilemma
Authors: Oliver Downs
Venue: KDD
Year: 2016

It has become a cliché to talk about Wanamaker's dilemma, his famous quote that "Half the money I spend on advertising is wasted; the trouble is I don't know which half" is well known. So why talk about it today? Well, I'll show you that some of the best targeted consumer marketing still suffers exactly from this problem, and tell you why -- it's not humanly possible to solve it! At Amplero we've finally solved it and made that solution accessible to marketers, using machine learning in combination with the revolution in online experimentation that the advent of the multi-armed bandit has brought about.
Catch Me If You Can: Detecting Pickpocket Suspects from Large-Scale Transit Records
Authors: Bowen Du, Chuanren Liu, Wenjun Zhou, Zhenshan Hou, Hui Xiong
Venue: KDD
Year: 2016

Massive data collected by automated fare collection (AFC) systems provide opportunities for studying both personal traveling behaviors and collective mobility patterns in the urban area. Existing studies on the AFC data have primarily focused on identifying passengers' movement patterns. In this paper, however, we creatively leveraged such data for identifying thieves in the public transit systems. Indeed, stopping pickpockets in the public transit systems has been critical for improving passenger satisfaction and public safety. However, it is challenging to tell thieves from regular passengers in practice. To this end, we developed a suspect detection and surveillance system, which can identify pick-pocket suspects based on their daily transit records. Specifically, we first extracted a number of features from each passenger's daily activities in the transit systems. Then, we took a two-step approach that exploits the strengths of unsupervised outlier detection and supervised classification models to identify thieves, who exhibit abnormal traveling behaviors. Experimental results demonstrated the effective- ness of our method. We also developed a prototype system with a user-friendly interface for the security personnel.
Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality
Authors: Nando de Freitas
Venue: KDD
Year: 2016

Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with several examples from my research team: learning to learn by gradient descent by gradient descent, neural programmers and interpreters, and learning communication.
Mining Reliable Information from Passively and Actively Crowdsourced Data
Authors: Jing Gao, Qi Li, Bo Zhao, Wei Fan, Jiawei Han
Venue: KDD
Year: 2016

Recent years have witnessed an astonishing growth of crowd-contributed data, which has become a powerful information source that covers almost every aspect of our lives. This big treasure trove of information has fundamentally changed the ways in which we learn about our world. Crowdsourcing has attracted considerable attentions with various approaches developed to utilize these enormous crowdsourced data from different perspectives. From the data collection perspective, crowdsourced data can be divided into two types: "passively" crowdsourced data and "actively" crowdsourced data; from task perspective, crowdsourcing research includes information aggregation, budget allocation, worker incentive mechanism, etc. To answer the need of a systematic introduction of the field and comparison of the techniques, we will present an organized picture on crowdsourcing methods in this tutorial. The covered topics will be interested for both advanced researchers and beginners in this field.