arXiv subjects

Keyu Nie

Publications and source records attributed to Keyu Nie.

6 recordsLinked to original sources

arXiv2026

Causal-Informed Hybrid Online Adaptive Optimization for Ad Load Personalization in Large-Scale Social Networks

Personalizing ad load in large-scale social networks requires balancing user experience and conversions under operational constraints. Traditional primal-dual methods enforce constraints reliably but adapt slowly in dynamic environments, while Bayesian Optimization (BO) enables exploration but suffers from slow convergence. We propose a hybrid online adaptive optimization framework CTRCBO ( Cohort-Based Trust Region Contextual Bayesian Optimization), combining primal-dual with BO, enhanced by trust-region updates and Gaussian Process Regression (GPR) surrogates for both objectives and constraints. Our approach leverages a upstream Causal ML model to inform the surrogate, improving decision quality and enabling efficient exploration-exploitation and online tuning. We evaluate our method on a billion-user social network, demonstrating faster convergence, robust constraint satisfaction, and improved personalization metrics, including real-world online AB test results.

cs.SI↗

arXiv2022

Clustering-based Imputation for Dropout Buyers in Large-scale Online Experimentation

In online experimentation, appropriate metrics (e.g., purchase) provide strong evidence to support hypotheses and enhance the decision-making process. However, incomplete metrics are frequently occurred in the online experimentation, making the available data to be much fewer than the planned online experiments (e.g., A/B testing). In this work, we introduce the concept of dropout buyers and categorize users with incomplete metric values into two groups: visitors and dropout buyers. For the analysis of incomplete metrics, we propose a clustering-based imputation method using $k$-nearest neighbors. Our proposed imputation method considers both the experiment-specific features and users' activities along their shopping paths, allowing different imputation values for different users. To facilitate efficient imputation of large-scale data sets in online experimentation, the proposed method uses a combination of stratification and clustering. The performance of the proposed method is compared to several conventional methods in both simulation studies and a real online experiment at eBay.

cs.LG↗

arXiv2022

Ensure A/B Test Quality at Scale with Automated Randomization Validation and Sample Ratio Mismatch Detection

eBay's experimentation platform runs hundreds of A/B tests on any given day. The platform integrates with the tracking infrastructure and customer experience servers, provides the sampling service for experiments, and has the responsibility to monitor the progress of each A/B test. There are many challenges especially when it is required to ensure experiment quality at the large scale. We discuss two automated test quality monitoring processes and methodologies, namely randomization validation using population stability index (PSI) and sample ratio mismatch (a.k.a. sample delta) detection using sequential analysis. The automated processes assist the experimentation platform to run high quality and trustworthy tests not only effectively on a large scale, but also efficiently by minimizing false positive monitoring alarms to experimenters.

stat.AP↗

arXiv2020

Moving Metric Detection and Alerting System at eBay

At eBay, there are thousands of product health metrics for different domain teams to monitor. We built a two-phase alerting system to notify users with actionable alerts based on anomaly detection and alert retrieval. In the first phase, we developed an efficient anomaly detection algorithm, called Moving Metric Detector (MMD), to identify potential alerts among metrics with distribution agnostic criteria. In the second alert retrieval phase, we built additional logic with feedbacks to select valid actionable alerts with point-wise ranking model and business rules. Compared with other trend and seasonality decomposition methods, our decomposer is faster and better to detect anomalies in unsupervised cases. Our two-phase approach dramatically improves alert precision and avoids alert spamming in eBay production.

cs.CY↗

arXiv2019

Dealing With Ratio Metrics in A/B Testing at the Presence of Intra-User Correlation and Segments

We study ratio metrics in A/B testing at the presence of correlation among observations coming from the same user and provides practical guidance especially when two metrics contradict each other. We propose new estimating methods to quantitatively measure the intra-user correlation (within segments). With the accurately estimated correlation, a uniformly minimum-variance unbiased estimator of the population mean, called correlation-adjusted mean, is proposed to account for such correlation structure. It is proved theoretically and numerically better than the other two unbiased estimators, naive mean and normalized mean (averaging within users first and then across users). The correlation-adjusted mean method is unbiased and has reduced variance so it gains additional power. Several simulation studies are designed to show the estimation accuracy of the correlation structure, effectiveness in reducing variance, and capability of obtaining more power. An application to the eBay data is conducted to conclude this paper.

stat.AP↗

arXiv2019

Efficient Multivariate Bandit Algorithm with Path Planning

In this paper, we solve the arms exponential exploding issue in multivariate Multi-Armed Bandit (Multivariate-MAB) problem when the arm dimension hierarchy is considered. We propose a framework called path planning (TS-PP) which utilizes decision graph/trees to model arm reward success rate with m-way dimension interaction, and adopts Thompson sampling (TS) for heuristic search of arm selection. Naturally, it is quite straightforward to combat the curse of dimensionality using a serial processes that operates sequentially by focusing on one dimension per each process. For our best acknowledge, we are the first to solve Multivariate-MAB problem using graph path planning strategy and deploying alike Monte-Carlo tree search ideas. Our proposed method utilizing tree models has advantages comparing with traditional models such as general linear regression. Simulation studies validate our claim by achieving faster convergence speed, better efficient optimal arm allocation and lower cumulative regret.

cs.LG↗