Online Active Linear Regression via Thresholding

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional settings. Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality --- significantly reducing both the mean and variance of the squared error.Comment: Published in AAAI 201

An examination of social presence in an online learning environment.

The distance education literature is lacking studies investigating the construct of social presence, the value placed on it by online learners, and whether its existence in text-based environments is necessary for learning, satisfaction, and contributing to course retention. The purpose of this study was to explore learner perceptions and experiences of the learning process within the Web-based online learning medium in terms of social presence. More specifically, it examines the relationship among learners\u27 perceptions of social presence in asynchronous online courses and how it relates to satisfaction with learning, whether course activities perceived as having high levels of social presence also have high levels of satisfaction and quality of learning, and whether perceptions of social presence and satisfaction with learning affects their likelihood of enrolling in future online courses. The research design of this study utilized an online survey administered to 280 students enrolled in online courses of nine disciplines; both undergraduate- and graduate- level at a large urban university. Open-ended questions from the online survey were examined as well to help inform and support the findings from the quantitative data. Data were analyzed using correlations, ANOVAs, and hierarchical regression analysis. The findings demonstrated that students\u27 perceived social presence was statistically, significantly, and positively related to their overall perceived satisfaction with learning in online courses. Furthermore, students\u27 perceived social presence was statistically, significantly, and positively related to their perception of quality of and satisfaction with learning for each of the five course activities examined in this study. The hierarchical regression analysis suggested that perceived social presence contributed substantially more incremental variance to the decision to enroll again in an online course than the satisfaction with learning variable. Overall, the theoretical model including social presence and satisfaction with learning explained 18 percent of the variance in the dependent variable. The potential implications for theory and practice for online course designers and instructors are provided

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. Future of the smart buildings lies in using sensory data for adaptive decision making and control that is currently gloomed by the key challenge of learning a good control policy in a short period of time in an online and continuing fashion. To tackle this challenge, an event-triggered -- as opposed to classic time-triggered -- paradigm, is proposed in which learning and control decisions are made when events occur and enough information is collected. Events are characterized by certain design conditions and they occur when the conditions are met, for instance, when a certain state threshold is reached. By systematically adjusting the time of learning and control decisions, the proposed framework can potentially reduce the variance in learning, and consequently, improve the control process. We formulate the micro-climate control problem based on semi-Markov decision processes that allow for variable-time state transitions and decision making. Using extended policy gradient theorems and temporal difference methods in a reinforcement learning set-up, we propose two learning algorithms for event-triggered control of micro-climate in buildings. We show the efficacy of our proposed approach via designing a smart learning thermostat that simultaneously optimizes energy consumption and occupants' comfort in a test building

Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time

A crucial problem in reinforcement learning is learning the optimal policy. We study this in tabular infinite-horizon discounted Markov decision processes under the online setting. The existing algorithms either fail to achieve regret optimality or have to incur a high memory and computational cost. In addition, existing optimal algorithms all require a long burn-in time in order to achieve optimal sample efficiency, i.e., their optimality is not guaranteed unless sample size surpasses a high threshold. We address both open problems by introducing a model-free algorithm that employs variance reduction and a novel technique that switches the execution policy in a slow-yet-adaptive manner. This is the first regret-optimal model-free algorithm in the discounted setting, with the additional benefit of a low burn-in time

Information and Communication-Based Collaborative Learning and Behavior Modeling Using Machine Learning Algorithm

Rapid growth of smart phone industries has led people to use more technology and thus aided in adoption of information and communication technology (ICT) in educational purposes for enhancing students? performance. This chapter shows that students use social media platform or virtual environment for learning, especially in Open University or online learning system. In such environment, the students? drop rate is extremely high. This work primarily aims at reducing students? dropout or students? fails to finish course within prerequisite time using student behavior styles. For addressing research problems, this research aims in building efficient student behavior learning model for improving the performance of student applying machine learning (ML) models. The behavior extraction and study have been carried utilizing decision tree (DT) ML algorithm. Further, a model has been proposed for provisioning student contextual information to different students utilizing VLE platform interaction (collaborative learning) using DT algorithm which considered bagging. The DT with bagging is an ensemble learning (EL) model that depicts bootstrap aggregating (BA), which is modeled for enhancing accuracies and stabilities of every distinct predictive trees. Bagging aids DT in influencing overfitting problems and minimizes its variance. The proposed method is efficient in extracting learning styles and intrinsic behavior of students