Boosted Off-Policy Learning

We investigate boosted ensemble models for off-policy learning from logged bandit feedback. Toward this goal, we propose a new boosting algorithm that directly optimizes an estimate of the policy's expected reward. We analyze this algorithm and prove that the empirical risk decreases (possibly exponentially fast) with each round of boosting, provided a "weak" learning condition is satisfied. We further show how the base learner reduces to standard supervised learning problems. Experiments indicate that our algorithm can outperform deep off-policy learning and methods that simply regress on the observed rewards, thereby demonstrating the benefits of both boosting and choosing the right learning objective

New Directions in Online Learning: Boosting, Partial Information, and Non-Stationarity

Online learning, where a learning algorithm fits a model on-the-fly with streaming data, has become an important research area in machine learning. Batch learning, where the entire data set has to be available to the learning algorithm, is not always a suitable paradigm for the big data era. It is increasingly common in many practical situations, such as online ads prediction or control of self-driving cars, that data instances naturally arrive in a sequential manner. In these situations, researchers want to update their model in an online fashion. This dissertation pursues several topics at the frontier of online learning research. In Chapter 2 and Chapter 3, the journey starts with online boosting. Online boosting studies how to combine multiple online weak learners to get a stronger learner. Chapter 2 considers online multi-class classification problems. Chapter 3 focuses on the more challenging multi-label ranking problem where there are multiple correct labels and the learner outputs a ranking of labels based on their relevance. In both chapters, an optimal algorithm and an adaptive algorithm are proposed. The optimal algorithms require a minimal number of weak learners to attain the desired accuracy. The adaptive algorithms are practically more useful since they do not require a priori knowledge about the strength of weak learners and are more computationally efficient. The adaptive algorithms are not statistically optimal but they still come with reasonable performance guarantees. The empirical results on real data sets support the theoretical findings and the proposed boosting algorithms outperformed existing competitors on benchmark data sets. Chapter 4 considers the partial information setting, where the learner does not receive the true labels. Partial feedback is common in practice as obtaining complete feedback can be costly. The chapter revisits the boosting algorithms that are presented in Chapter 2 and Chapter 3 and extends them to work with partial information feedback. Despite the learner receiving much less information, comparable performance guarantees can be made. Later in Chapter 5 and Chapter 6, we move on to another interesting area in online learning called restless bandit problems. Unlike the classical (stochastic) multi-armed bandit problems where the reward distributions are unknown but stationary, in restless bandit problems the distributions can change over time. This extra layer of complexity allows us to study more complicated models, but the analysis becomes even more difficult. In restless bandit problems, it is assumed that each arm has a state that evolves according to an unknown Markov process, and the reward distribution depends on the arm's current state. This setting can be thought of as a sub-class of reinforcement learning and the partial observability inherent in this problem makes the analysis very challenging. The well known Thompson Sampling algorithm is analyzed and a Bayesian regret bound for it is derived. Chapter 5 considers the episodic case where the system periodically resets. Chapter 6 extends the analysis to the more challenging non-episodic (i.e., infinite time horizon) case. In both settings, Thompson Sampling algorithms (with slight modifications) enjoy sub-linear regret bounds, and the empirical results on simulated data support this fact. The experiments also suggest the possibility that the algorithm can be used in the frequentist setting even though the theoretical bounds are only shown for the Bayesian regret.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155110/1/yhjung_1.pd

Distributed Online Big Data Classification Using Context Information

Distributed, online data mining systems have emerged as a result of applications requiring analysis of large amounts of correlated and high-dimensional data produced by multiple distributed data sources. We propose a distributed online data classification framework where data is gathered by distributed data sources and processed by a heterogeneous set of distributed learners which learn online, at run-time, how to classify the different data streams either by using their locally available classification functions or by helping each other by classifying each other's data. Importantly, since the data is gathered at different locations, sending the data to another learner to process incurs additional costs such as delays, and hence this will be only beneficial if the benefits obtained from a better classification will exceed the costs. We model the problem of joint classification by the distributed and heterogeneous learners from multiple data sources as a distributed contextual bandit problem where each data is characterized by a specific context. We develop a distributed online learning algorithm for which we can prove sublinear regret. Compared to prior work in distributed online data mining, our work is the first to provide analytic regret results characterizing the performance of the proposed algorithm