Online Policy Learning and Inference by Matrix Completion (2404.17398v2)

Abstract: Is it possible to make online decisions when personalized covariates are unavailable? We take a collaborative-filtering approach for decision-making based on collective preferences. By assuming low-dimensional latent features, we formulate the covariate-free decision-making problem as a matrix completion bandit. We propose a policy learning procedure that combines an $\varepsilon$-greedy policy for decision-making with an online gradient descent algorithm for bandit parameter estimation. Our novel two-phase design balances policy learning accuracy and regret performance. For policy inference, we develop an online debiasing method based on inverse propensity weighting and establish its asymptotic normality. Our methods are applied to data from the San Francisco parking pricing project, revealing intriguing discoveries and outperforming the benchmark policy.

• The paper presents a new framework that integrates matrix completion with bandit algorithms to optimize online policy learning in sparse feature environments.
• It utilizes ε-greedy exploration and online gradient descent to effectively manage the trade-offs between reducing regret and achieving accurate policy inference.
• Empirical results from simulations and real-world data confirm the framework’s capability to adjust for biases using inverse propensity weighting.

Matrix Completion Bandits for Personalized Online Decision Making

Introduction to Matrix Completion Bandits (MCB)

Matrix Completion Bandit (MCB) problems are conceived to optimize decision-making processes in scenarios where features are sparse and orthogonal to historical data, commonly seen in personalized service areas like e-commerce or healthcare. This paper discusses an approach to formulating such problems within the matrix completion framework in conjunction with collaborative filtering, aimed at effectively balancing the dual objectives of exploration and exploitation.

Policy Learning and Algorithm Convergence

The main algorithmic advancements discussed include the introduction of $\varepsilon$-greedy and online gradient descent mechanisms to foster learning and decision-making under the MCB model. Specifically, the paper provides a detailed analysis on how variations in the schedule of exploration probabilities and step sizes influence learning accuracy and regret:

• Convergence and Accuracy: The analysis indicates that maintaining a higher (or faster-decaying) exploration probability within a sensibly chosen schedule can effectively reduce regret, though it might compromise the accuracy of the learned policy.
• Regret vs. Policy Accuracy: A critical examination is provided on the trade-offs between immediate performance (regret) and long-term benefits (policy accuracy). The paper finds that faster-decaying exploration probabilities yield smaller regret but at the expense of precise policy learning.

Practical Implications and Theoretical Contributions

From a practical standpoint, various simulations and a real-world dataset (San Francisco parking pricing project) validate the effectiveness of the proposed methods. The theoretical contributions extend to a comprehensive framework for evaluating policy inference, where online debiasing techniques, specifically inverse propensity weighting (IPW), play a central role in adjusting for non-uniform exploration of actions.

Online Inference Framework

A significant portion of the paper is devoted to establishing a robust framework for online policy inference. This enhances the practical utility of the MCB approach by allowing real-time adjustments and improvements to decision-making strategies based on incoming data. The paper skillfully discusses how the inherent biases of gradient descent methods in dynamic environments can be mitigated through IPW to achieve asymptotical normality of estimators, facilitating the construction of confidence intervals and hypothesis testing in online settings.

Future Directions

Looking ahead, this work opens several avenues for further exploration:

1. Complexity and Scalability: Further research could focus on optimizing the computational complexity and scalability of the proposed methods, particularly in environments with extremely large datasets or higher-dimensional matrices.
2. Broader Applicability: Extending these methods to non-matrix structured data or different types of decision problems could broaden the applicability of the MCB approach.
3. Refinement of Inference Techniques: Enhancements to the online debiasing approach could potentially improve the accuracy and reliability of policy inference under varying operational conditions.

Conclusion

The exploration of matrix completion bandits in this paper provides a robust framework for addressing the complex challenge of learning optimal policies in environments with sparse, orthogonal features. The proposed methods hold promise for significantly improving decision-making processes in personalized applications, supported by both theoretical insights and empirical validations.