Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

Linear Regression Analysis

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to achieve more effective and responsive disaster management, and has undergone considerable progress in the last decade. However, to the best of our knowledge, there is currently no work that both summarizes recent progress and suggests future directions for this emerging research area. To remedy this situation, we provide a systematic treatment of the recent developments in data-driven disaster management. Specifically, we first present a general overview of the requirements and system architectures of disaster management systems and then summarize state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management. We also discuss and categorize general data-mining and machine-learning techniques in disaster management. Finally, we recommend several research directions for further investigations.

https://dl.acm.org/doi/pdf/10.1145/3017678

Data-Driven Techniques in Disaster Information Management

Contextual multi-armed bandit problems have gained increasing popularity and attention in recent years due to their capability of leveraging contextual information to deliver online personalized recommendation services (eg, online advertising and news article selection) To predict the reward of each arm given a particular context, existing relevant research studies for contextual multi-armed bandit problems often assume the existence of a fixed yet unknown reward mapping function However, this assumption rarely holds in practice, since real-world problems often involve underlying processes that are dynamically evolving over time In this paper, we study the time varying contextual multi-armed problem where the reward mapping function changes over time In particular, we propose a dynamical context drift model based on particle learning In the proposed model, the drift on the reward mapping function is explicitly modeled as a set of random walk particles, where good fitted particles are selected to learn the mapping dynamically Taking advantage of the fully adaptive inference strategy of particle learning, our model is able to effectively capture the context change and learn the latent parameters In addition, those learnt parameters can be naturally integrated into existing multi-arm selection strategies such as LinUCB and Thompson sampling Empirical studies on two real-world applications, including online personalized advertising and news recommendation, demonstrate the effectiveness of our proposed approach The experimental results also show that our algorithm can dynamically track the changing reward over time and consequently improve the click-through rate

https://dl.acm.org/doi/pdf/10.1145/2939672.2939878

Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit

Personalized recommendation services have gained increasing popularity and attention in recent years as most useful information can be accessed online in real-time. Most online recommender systems try to address the information needs of users by virtue of both user and content information. Despite extensive recent advances, the problem of personalized recommendation remains challenging for at least two reasons. First, the user and item repositories undergo frequent changes, which makes traditional recommendation algorithms ineffective. Second, the so-called cold-start problem is difficult to address, as the information for learning a recommendation model is limited for new items or new users. Both challenges are formed by the dilemma of exploration and exploitation. In this paper, we formulate personalized recommendation as a contextual bandit problem to solve the exploration/exploitation dilemma. Specifically in our work, we propose a parameter-free bandit strategy, which employs a principled resampling approach called online bootstrap, to derive the distribution of estimated models in an online manner. Under the paradigm of probability matching, the proposed algorithm randomly samples a model from the derived distribution for every recommendation. Extensive empirical experiments on two real-world collections of web data (including online advertising and news recommendation) demonstrate the effectiveness of the proposed algorithm in terms of the click-through rate. The experimental results also show that this proposed algorithm is robust in the cold-start situation, in which there is no sufficient data or knowledge to tune the hyper-parameters.

Personalized Recommendation via Parameter-Free Contextual Bandits

Techniques to efficiently discover, collect, organize, search, and disseminate real-time disaster information have become national priorities for efficient crisis management and disaster recovery tasks. We have developed techniques to facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We have designed and implemented two parallel systems: a web-based prototype of a Business Continuity Information Network system and an All-Hazard Disaster Situation Browser system that run on mobile devices. Data mining and information retrieval techniques help impacted communities better understand the current disaster situation and how the community is recovering. Specifically, information extraction integrates the input data from different sources; report summarization techniques generate brief reviews from a large collection of reports at different granularities; probabilistic models support dynamically generating query forms and information dashboard based on user feedback; and community generation and user recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. User studies with more than 200 participants from EOC personnel and companies demonstrate that our systems are very useful to gain insights about the disaster situation and for making decisions.

Data Mining Meets the Needs of Disaster Information Management

Online interactive recommender systems strive to promptly suggest to consumers appropriate items (e.g., movies, news articles) according to the current context including both the consumer and item content information. However, such context information is often unavailable in practice for the recommendation, where only the users' interaction data on items can be utilized. Moreover, the lack of interaction records, especially for new users and items, worsens the performance of recommendation further. To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving the balance between exploitation and exploration, are adopted in the online interactive recommendation settings, by assuming independent items (i.e., arms). Nonetheless, the assumption rarely holds in reality, since the real-world items tend to be correlated with each other (e.g., two articles with similar topics). In this paper, we study online interactive collaborative filtering problems by considering the dependencies among items. We explicitly formulate the item dependencies as the clusters on arms, where the arms within a single cluster share the similar latent topics. In light of the topic modeling techniques, we come up with a generative model to generate the items from their underlying topics. Furthermore, an efficient online algorithm based on particle learning is developed for inferring both latent parameters and states of our model. Additionally, our inferred model can be naturally integrated with existing multi-armed selection strategies in the online interactive collaborating setting. Empirical studies on two real-world applications, online recommendations of movies and news, demonstrate both the effectiveness and efficiency of the proposed approach.

Chunqiu Zeng

Papers

Data-Driven Techniques in Disaster Information Management

Online Context-Aware Recommendation with Time Varying Multi-Armed Bandit

Personalized Recommendation via Parameter-Free Contextual Bandits

Data Mining Meets the Needs of Disaster Information Management

Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms