As an interdisciplinary topic, the study of fake news encourages a concerted effort of experts in computer and information science, political science, journalism, social science, psychology, and economics. A comprehensive framework to systematically understand and detect fake news is necessary to attract and unite researchers in related areas to conduct research on fake news. This tutorial aims to clearly present 1 fake news research, its challenges, and research directions; 2 a comparison between fake news and other related concepts e.
We present fake news detection from various perspectives, which involve news content and information in social networks, and broadly adopt techniques in data mining, machine learning, natural language processing, information retrieval and social search. Facing the upcoming U. Zeroth-order ZO optimization is increasingly embraced for solving big data and machine learning problems when explicit expressions of the gradients are difficult or infeasible to obtain.
It achieves gradient-free optimization by approximating the full gradient via efficient gradient estimators. This tutorial aims to provide a comprehensive introduction to recent advances in ZO optimization methods in both theory and applications. On the theory side, we will cover convergence rate and iteration complexity analysis of ZO algorithms and make comparisons to their first-order counterparts.
On the application side, we will highlight one appealing application of ZO optimization to studying the robustness of deep neural networks - practical and efficient adversarial attacks that generate adversarial examples from a black-box machine learning model. We will also summarize potential research directions regarding ZO optimization, big data challenges and some open-ended data mining and machine learning problems. Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infras- tructure components guides capacity planning; and work- force scheduling in warehouses and factories requires fore- casts of the future workload.
Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources and result in a set of challenges that need to be ad- dressed such as the following.
How can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? What are the implications for building forecasting systems that can handle large data volumes? The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in three related fields: 1 classical modeling of time series, 2 modern methods including ten- sor analysis and deep learning for forecasting. Furthermore, we discuss the practical aspects of building a large scale forecasting system, including data integration, feature gen- eration, backtest framework, error tracking and analysis, etc.
While our focus is on providing an intuitive overview of the methods and practical issues which we will illustrate via case studies and interactive materials with Jupyter notebooks. Search and recommender systems, involving various offline and online components, are becoming increasingly complex. The two systems share many fundamental components such as language understanding for query or documents, retrieval and ranking for documents or items, and language generation for interacting with users.
Natural language text data, such as queries, user profiles, and documents, are the data mostly present in both systems. Thus, building powerful search and recommender systems inevitably requires processing and understanding natural language effectively and efficiently. Recent rapid growth of deep learning technologies has presented both opportunities and challenges in this area. This tutorial offers an overview of deep learning based natural language processing for search and recommender systems from an industry perspective. We focus on how deep natural language processing powers search and recommender systems in practice.
The tutorial first introduces deep learning based natural language processing technologies, including language understanding and language generation. Then it details how those technologies can be applied to common tasks in search and recommender systems, including query and document understanding, retrieval and ranking, and language generation. Applications in LinkedIn production systems are presented as concrete examples where practical challenges are discussed. The tutorial concludes with discussion of future trend in both systems. Spatio-temporal societal event forecasting, which has traditionally been prohibitively challenging, is now becoming possible and experiencing rapid growth thanks to the big data from Open Source Indicators OSI such as social media, news sources, blogs, economic indicators, and other meta-data sources.
Spatio-temporal societal event forecasting and their precursor discovery benefit the society by providing insight into events such as political crises, humanitarian crises, mass violence, riots, mass migrations, disease outbreaks, economic instability, resource shortages, natural disasters, and others. In contrast to traditional event detection that identifies ongoing events, event forecasting focuses on predicting future events yet to happen. Also different from traditional spatio-temporal predictions on numerical indices, spatio-temporal event forecasting needs to leverage the heterogeneous information from OSI to discover the predictive indicators and mappings to future societal events.
While studying large scale societal events, policy makers and practitioners aim to identify precursors to such events to help understand causative attributes and ensure accountability. The resulting problems typically require the predictive modeling techniques that can jointly handle semantic, temporal, and spatial information, and require a design of efficient and interpretable algorithms that scale to high-dimensional large real-world datasets.
In this tutorial, we will present a comprehensive review of the state-of-the-art methods for spatio-temporal societal event forecasting. First, we will categorize the inputs OSI and the predicted societal events commonly researched in the literature.
Then we will review methods for temporal and spatio-temporal societal event forecasting. Next, we will also discuss the foundations of precursor identification with an introduction to various machine learning approaches that aim to discover precursors while forecasting events. Through the tutorial, we expect to illustrate the basic theoretical and algorithmic ideas and discuss specific applications in all the above settings. Finding nearest neighbors is an important topic that has attracted much attention over the years and has applications in many fields, such as market basket analysis, plagiarism and anomaly detection, advertising, community detection, ligand-based virtual screening, etc.
As data are being easier and easier to collect, finding neighbors has become a potential bottleneck in analysis pipelines.
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Performing pairwise comparisons given the massive datasets of today is no longer feasible. The high computational complexity of the task has led researchers to develop approximate methods, which find many but not all of the nearest neighbors. Yet, for some types of data, efficient exact solutions have been found by carefully partitioning or filtering the search space in a way that avoids most unnecessary comparisons. In recent years, there have been several fundamental advances in our ability to efficiently identify appropriate neighbors, especially in non-traditional data, such as graphs or document collections.
In this tutorial, we provide an in-depth overview of recent methods for finding nearest neighbors, focusing on the intuition behind choices made in the design of those algorithms as well as the utility of the methods in real-world applications. For each type of data, we will review the current state-of-the-art approaches used to identify neighbors and discuss how neighbor search methods are used to solve important problems.
Arguably, every entity in this universe is networked in one way or another. With the prevalence of network data collected, such as social media and biological networks, learning from networks has become an essential task in many applications. It is well recognized that network data is intricate and large-scale, and analytic tasks on network data become more and more sophisticated.
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In this tutorial, we systematically review the area of learning from networks, including algorithms, theoretical analysis, and illustrative applications. Starting with a quick recollection of the exciting history of the area, we formulate the core technical problems. Then, we introduce the fundamental approaches, that is, the feature selection based approaches and the network embedding based approaches. Next, we extend our discussion to attributed networks, which are popular in practice.
Last, we cover the latest hot topic, graph neural based approaches. For each group of approaches, we also survey the associated theoretical analysis and real-world application examples. Our tutorial also inspires a series of open problems and challenges that may lead to future breakthroughs. The authors are productive and seasoned researchers active in this area who represent a nice combination of academia and industry.
The tutorial will review recent developments in using techniques from statistical mechanics to understand the properties of modern deep neural networks. Although there have long been connections between statistical mechanics and neural networks, in recent decades connections have withered. In light of recent failings of traditional statistical learning theory and stochastic optimization theory even to qualitatively describe many properties of production quality deep neural network models, researchers have revisited ideas from the statistical mechanics of neural networks.
The tutorial will provide an overview of the area; it will go into detail on how connections with heavy tailed random matrix theory can lead to a practical phenomenological theory for large-scale deep neural networks; and it will describe future directions. Networks or graphs are used to represent and analyze large datasets of objects and their relations. Typical examples of graph applications come from social networks, traffic networks, electric power grids, road systems, the Internet, chemical and biological systems, and more.
Naturally, real-world networks have a temporal component: for instance, interactions between objects have a timestamp and a duration.
In this tutorial we present models and algorithms for mining temporal networks, i. We overview different models used to represent temporal networks. We highlight the main differences between static and temporal networks, and discuss the challenges arising from introducing the temporal dimension in the network representation. We present recent papers addressing the most well-studied problems in the setting of temporal networks, including computation of centrality measures, motif detection and counting, community detection and monitoring, event and anomaly detection, analysis of epidemic processes and influence spreading, network summarization, and structure prediction.
Finally, we discuss some of the current challenges and open problems in the area, and we highlight directions for future work. There is now more data to analyze than ever before. As data volume and variety have increased, so have the ties between machine learning and data integration become stronger.
For machine learning to be effective, one must utilize data from the greatest possible variety of sources; and this is why data integration plays a key role. At the same time machine learning is driving automation in data integration, resulting in overall reduction of integration costs and improved accuracy. This tutorial focuses on three aspects of the synergistic relationship between data integration and machine learning: 1 we survey how state-of-the-art data integration solutions rely on machine learning-based approaches for accurate results and effective human-in-the-loop pipelines, 2 we review how end-to-end machine learning applications rely on data integration to identify accurate, clean, and relevant data for their analytics exercises, and 3 we discuss open research challenges and opportunities that span across data integration and machine learning.
It is widely used in the industry to test changes ranging from simple copy change or UI change to more complex changes like using machine learning models to personalize user experience. In this tutorial we will discuss challenges, best practices, and pitfalls in evaluating experiment results, focusing on both lessons learned and practical guidelines as well as open research questions.
Such event sequences are the basis of many practical applications, neural spiking train study, earth quack prediction, crime analysis, infectious disease diffusion forecasting, condition-based preventative maintenance, information retrieval and behavior-based network analysis and services, etc. Temporal point process TPP is a principled mathematical tool for the modeling and learning of asynchronous event sequences, which captures the instantaneous happening rate of the events and the temporal dependency between historical and current events. TPP provides us with an interpretable model to describe the generative mechanism of event sequences, which is beneficial for event prediction and causality analysis.
Recently, it has been shown that TPP has potentials to many machine learning and data science applications and can be combined with other cutting-edge machine learning techniques like deep learning, reinforcement learning, adversarial learning, and so on. Transportation, particularly the mobile ride-sharing domain has a number of traditionally challenging dynamic decision problems that have long threads of research literature and readily stand to benefit tremendously from artificial intelligence AI. Some core examples include online ride order dispatching, which matches available drivers to trip requesting passengers on a ride-sharing platform in real-time; route planning, which plans the best route between the origin and destination of a trip; and traffic signals control, which dynamically and adaptively adjusts the traffic signals within a region to achieve low delays.
All of these problems have a common characteristic that a sequence of decisions is to be made while we care about some cumulative objectives over a certain horizon. Reinforcement learning RL is a machine learning paradigm that trains an agent to learn to take optimal actions as measured by the total cumulative reward achieved in an environment through interactions with it and getting feedback signals. It is thus a class of optimization methods for solving sequential decision-making problems.
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If you have a question that requires immediate attention, please feel free to contact us. Thank you! Lecture-Style Tutorials. Deep Bayesian Mining, Learning and Understanding This tutorial addresses the advances in deep Bayesian learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few.
Gold Panning from the Mess: Rare Category Exploration, Exposition, Representation and Interpretation In contrast to the massive volume of data, it is often the rare categories that are of great importance in many high impact domains, ranging from financial fraud detection in online transaction networks to emerging trend detection in social networks, from spam image detection in social media to rare disease diagnosis in the medical decision support system. Data Mining Methods for Drug Discovery and Development In silico modeling of medicine refers to the direct use of computational methods in support of drug discovery and development.
Mining and model understanding on medical data Medical research and patient caretaking are increasingly benefiting from advances in machine learning. Constructing and Mining Heterogeneous Information Networks from Massive Text Real-world data exists largely in the form of unstructured texts. Incompleteness in Networks: Biases, Skewed Results, and Some Solutions Network analysis is often conducted on incomplete samples of much larger fully observed networks which are supposed to represent some phenomena of interest.
Optimize the Wisdom of the Crowd: Inference, Learning, and Teaching The increasing need for labeled data has brought the booming growth of crowdsourcing in a wide range of high-impact real-world applications, such as collaborative knowledge e. Interpretable knowledge Discovery Reinforced by Visual Methods This tutorial will cover the state-of-the-art research, development, and applications in the KDD area of interpretable knowledge discovery reinforced by visual methods to stimulate and facilitate future work. In this tutorial, we will cover five important aspects related to the effective mining of user interests: 1 The information sources that are used for extracting user interests 2 Various types of user interest profiles that have been proposed in the literature 3 Techniques that have been adopted or proposed for mining user interests 4 The scalability and resource requirements of the state of the art methods 5 The evaluation methodologies that are adopted in the literature for validating the appropriateness of the mined user interest profiles.
Fairness-Aware Machine Learning: Practical Challenges and Lessons Learned Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learned models and data-driven systems, and the potential for such systems to discriminate against certain population groups, due to biases in algorithmic decision-making systems. Explainable AI in Industry Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences.
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Advances in Cost-sensitive Multiclass and Multilabel Classification Classification is an important problem for data mining and knowledge discovery. Mining and other resource companies must decide whether to outsource or own innovation and technology. If they choose the latter, they will undoubtedly seek suitable acquisition targets.
The industry might also attract potential new entrants from the technology sector, adding a new layer of competition and shaking up the mining transactions market. Back to main page. Request for proposal. Save what resonates, curate a library of information, and share content with your network of contacts. You've been a member since. Skip to content. Please note that your account has not been verified - unverified account will be deleted 48 hours after initial registration.
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