Junwen Yang

Junwen Yang, Tianyuan Jin, Vincent Y. F. Tan

We introduce a novel extension of the canonical multi-armed bandit problem that incorporates an additional strategic element: abstention. In this enhanced framework, the agent is not only tasked with selecting an arm at each time step, but also has the option to abstain from accepting the stochastic instantaneous reward before observing it. When opting for abstention, the agent either suffers a fixed regret or gains a guaranteed reward. Given this added layer of complexity, we ask whether we can develop efficient algorithms that are both asymptotically and minimax optimal. We answer this question affirmatively by designing and analyzing algorithms whose regrets meet their corresponding information-theoretic lower bounds. Our results offer valuable quantitative insights into the benefits of the abstention option, laying the groundwork for further exploration in other online decision-making problems with such an option. Numerical results further corroborate our theoretical findings.

Junwen Yang, Yeye He, Surajit Chaudhuri

Recent work has made significant progress in helping users to automate single data preparation steps, such as string-transformations and table-manipulation operators (e.g., Join, GroupBy, Pivot, etc.). We in this work propose to automate multiple such steps end-to-end, by synthesizing complex data pipelines with both string transformations and table-manipulation operators. We propose a novel "by-target" paradigm that allows users to easily specify the desired pipeline, which is a significant departure from the traditional by-example paradigm. Using by-target, users would provide input tables (e.g., csv or json files), and point us to a "target table" (e.g., an existing database table or BI dashboard) to demonstrate how the output from the desired pipeline would schematically "look like". While the problem is seemingly underspecified, our unique insight is that implicit table constraints such as FDs and keys can be exploited to significantly constrain the space to make the problem tractable. We develop an Auto-Pipeline system that learns to synthesize pipelines using reinforcement learning and search. Experiments on large numbers of real pipelines crawled from GitHub suggest that Auto-Pipeline can successfully synthesize 60-70% of these complex pipelines with up to 10 steps.