A Contextual Bandit Bake-off

Contextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally efficient methods, the practical behavior of these algorithms is still poorly understood. We leverage the availability of large numbers of supervised learning datasets to empirically evaluate contextual bandit algorithms, focusing on practical methods that learn by relying on optimization oracles from supervised learning. We find that a recent method (Foster et al., 2018) using optimism under uncertainty works the best overall. A surprisingly close second is a simple greedy baseline that only explores implicitly through the diversity of contexts, followed by a variant of Online Cover (Agarwal et al., 2014) which tends to be more conservative but robust to problem specification by design. Along the way, we also evaluate various components of contextual bandit algorithm design such as loss estimators. Overall, this is a thorough study and review of contextual bandit methodology

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

A Contextual Bandit Bake-off

International audienceContextual bandit algorithms are essential for solving many real-world interactive machine learning problems. Despite multiple recent successes on statistically and computationally efficient methods, the practical behavior of these algorithms is still poorly understood. We leverage the availability of large numbers of supervised learning datasets to compare and empirically optimize contextual bandit algorithms, focusing on practical methods that learn by relying on optimization oracles from supervised learning. We find that a recent method (Foster et al., 2018) using optimism under uncertainty works the best overall. A surprisingly close second is a simple greedy baseline that only explores implicitly through the diversity of contexts, followed by a variant of Online Cover (Agarwal et al., 2014) which tends to be more conservative but robust to problem specification by design. Along the way, we also evaluate and improve several internal components of contextual bandit algorithm design. Overall, this is a thorough study and review of contextual bandit methodology

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