Chris H. Wiggins

Kathy Li, Iñigo Urteaga, Amanda Shea et al.

Mobile health (mHealth) apps such as menstrual trackers provide a rich source of self-tracked health observations that can be leveraged for health-relevant research. However, such data streams have questionable reliability since they hinge on user adherence to the app. Therefore, it is crucial for researchers to separate true behavior from self-tracking artifacts. By taking a machine learning approach to modeling self-tracked cycle lengths, we can both make more informed predictions and learn the underlying structure of the observed data. In this work, we propose and evaluate a hierarchical, generative model for predicting next cycle length based on previously-tracked cycle lengths that accounts explicitly for the possibility of users skipping tracking their period. Our model offers several advantages: 1) accounting explicitly for self-tracking artifacts yields better prediction accuracy as likelihood of skipping increases; 2) because it is a generative model, predictions can be updated online as a given cycle evolves, and we can gain interpretable insight into how these predictions change over time; and 3) its hierarchical nature enables modeling of an individual's cycle length history while incorporating population-level information. Our experiments using mHealth cycle length data encompassing over 186,000 menstruators with over 2 million natural menstrual cycles show that our method yields state-of-the-art performance against neural network-based and summary statistic-based baselines, while providing insights on disentangling menstrual patterns from self-tracking artifacts. This work can benefit users, mHealth app developers, and researchers in better understanding cycle patterns and user adherence.

Iñigo Urteaga, Chris H. Wiggins

We extend Bayesian multi-armed bandit (MAB) algorithms beyond their original setting by making use of sequential Monte Carlo (SMC) methods. A MAB is a sequential decision making problem where the goal is to learn a policy that maximizes long term payoff, where only the reward of the executed action is observed. In the stochastic MAB, the reward for each action is generated from an unknown distribution, often assumed to be stationary. To decide which action to take next, a MAB agent must learn the characteristics of the unknown reward distribution, e.g., compute its sufficient statistics. However, closed-form expressions for these statistics are analytically intractable except for simple, stationary cases. We here utilize SMC for estimation of the statistics Bayesian MAB agents compute, and devise flexible policies that can address a rich class of bandit problems: i.e., MABs with nonlinear, stateless- and context-dependent reward distributions that evolve over time. We showcase how non-stationary bandits, where time dynamics are modeled via linear dynamical systems, can be successfully addressed by SMC-based Bayesian bandit agents. We empirically demonstrate good regret performance of the proposed SMC-based bandit policies in several MAB scenarios that have remained elusive, i.e., in non-stationary bandits with nonlinear rewards.

Iñigo Urteaga, Chris H. Wiggins

We here adopt Bayesian nonparametric mixture models to extend multi-armed bandits in general, and Thompson sampling in particular, to scenarios where there is reward model uncertainty. In the stochastic multi-armed bandit, the reward for the played arm is generated from an unknown distribution. Reward uncertainty, i.e., the lack of knowledge about the reward-generating distribution, induces the exploration-exploitation trade-off: a bandit agent needs to simultaneously learn the properties of the reward distribution and sequentially decide which action to take next. In this work, we extend Thompson sampling to scenarios where there is reward model uncertainty by adopting Bayesian nonparametric Gaussian mixture models for flexible reward density estimation. The proposed Bayesian nonparametric mixture model Thompson sampling sequentially learns the reward model that best approximates the true, yet unknown, per-arm reward distribution, achieving successful regret performance. We derive, based on a novel posterior convergence based analysis, an asymptotic regret bound for the proposed method. In addition, we empirically evaluate its performance in diverse and previously elusive bandit environments, e.g., with rewards not in the exponential family, subject to outliers, and with different per-arm reward distributions. We show that the proposed Bayesian nonparametric Thompson sampling outperforms, both in averaged cumulative regret and in regret volatility, state-of-the-art alternatives. The proposed method is valuable in the presence of bandit reward model uncertainty, as it avoids stringent case-by-case model design choices, yet provides important regret savings.

Iñigo Urteaga, Chris H. Wiggins

In many biomedical, science, and engineering problems, one must sequentially decide which action to take next so as to maximize rewards. One general class of algorithms for optimizing interactions with the world, while simultaneously learning how the world operates, is the multi-armed bandit setting and, in particular, the contextual bandit case. In this setting, for each executed action, one observes rewards that are dependent on a given 'context', available at each interaction with the world. The Thompson sampling algorithm has recently been shown to enjoy provable optimality properties for this set of problems, and to perform well in real-world settings. It facilitates generative and interpretable modeling of the problem at hand. Nevertheless, the design and complexity of the model limit its application, since one must both sample from the distributions modeled and calculate their expected rewards. We here show how these limitations can be overcome using variational inference to approximate complex models, applying to the reinforcement learning case advances developed for the inference case in the machine learning community over the past two decades. We consider contextual multi-armed bandit applications where the true reward distribution is unknown and complex, which we approximate with a mixture model whose parameters are inferred via variational inference. We show how the proposed variational Thompson sampling approach is accurate in approximating the true distribution, and attains reduced regrets even with complex reward distributions. The proposed algorithm is valuable for practical scenarios where restrictive modeling assumptions are undesirable.

Iñigo Urteaga, Chris H. Wiggins

Reinforcement learning studies how to balance exploration and exploitation in real-world systems, optimizing interactions with the world while simultaneously learning how the world operates. One general class of algorithms for such learning is the multi-armed bandit setting. Randomized probability matching, based upon the Thompson sampling approach introduced in the 1930s, has recently been shown to perform well and to enjoy provable optimality properties. It permits generative, interpretable modeling in a Bayesian setting, where prior knowledge is incorporated, and the computed posteriors naturally capture the full state of knowledge. In this work, we harness the information contained in the Bayesian posterior and estimate its sufficient statistics via sampling. In several application domains, for example in health and medicine, each interaction with the world can be expensive and invasive, whereas drawing samples from the model is relatively inexpensive. Exploiting this viewpoint, we develop a double sampling technique driven by the uncertainty in the learning process: it favors exploitation when certain about the properties of each arm, exploring otherwise. The proposed algorithm does not make any distributional assumption and it is applicable to complex reward distributions, as long as Bayesian posterior updates are computable. Utilizing the estimated posterior sufficient statistics, double sampling autonomously balances the exploration-exploitation tradeoff to make better informed decisions. We empirically show its reduced cumulative regret when compared to state-of-the-art alternatives in representative bandit settings.

Amy Rebecca Gansell, Jan-Willem van de Meent, Sakellarios Zairis et al.

Thousands of first-millennium BCE ivory carvings have been excavated from Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan Tash) hundreds of miles from their Levantine production contexts. At present, their specific manufacture dates and workshop localities are unknown. Relying on subjective, visual methods, scholars have grappled with their classification and regional attribution for over a century. This study combines visual approaches with machine-learning techniques to offer data-driven perspectives on the classification and attribution of this early Iron Age corpus. The study sample consisted of 162 sculptures of female figures. We have developed an algorithm that clusters the ivories based on a combination of descriptive and anthropometric data. The resulting categories, which are based on purely statistical criteria, show good agreement with conventional art historical classifications, while revealing new perspectives, especially with regard to the contested Syrian/South Syrian/Intermediate tradition. Specifically, we have identified that objects of the Syrian/South Syrian/Intermediate tradition may be more closely related to Phoenician objects than to North Syrian objects; we offer a reconsideration of a subset of Phoenician objects, and we confirm Syrian/South Syrian/Intermediate stylistic subgroups that might distinguish networks of acquisition among the sites of Nimrud, Khorsabad, Arslan Tash and the Levant. We have also identified which features are most significant in our cluster assignments and might thereby be most diagnostic of regional carving traditions. In short, our study both corroborates traditional visual classification methods and demonstrates how machine-learning techniques may be employed to reveal complementary information not accessible through the exclusively visual analysis of an archaeological corpus.

Jan-Willem van de Meent, Jonathan E. Bronson, Frank Wood et al.

We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.