Trustworthy Experimentation Under Telemetry Loss

Failure to accurately measure the outcomes of an experiment can lead to bias and incorrect conclusions. Online controlled experiments (aka AB tests) are increasingly being used to make decisions to improve websites as well as mobile and desktop applications. We argue that loss of telemetry data (during upload or post-processing) can skew the results of experiments, leading to loss of statistical power and inaccurate or erroneous conclusions. By systematically investigating the causes of telemetry loss, we argue that it is not practical to entirely eliminate it. Consequently, experimentation systems need to be robust to its effects. Furthermore, we note that it is nontrivial to measure the absolute level of telemetry loss in an experimentation system. In this paper, we take a top-down approach towards solving this problem. We motivate the impact of loss qualitatively using experiments in real applications deployed at scale, and formalize the problem by presenting a theoretical breakdown of the bias introduced by loss. Based on this foundation, we present a general framework for quantitatively evaluating the impact of telemetry loss, and present two solutions to measure the absolute levels of loss. This framework is used by well-known applications at Microsoft, with millions of users and billions of sessions. These general principles can be adopted by any application to improve the overall trustworthiness of experimentation and data-driven decision making.Comment: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, October 201

Online Model Evaluation in a Large-Scale Computational Advertising Platform

Online media provides opportunities for marketers through which they can deliver effective brand messages to a wide range of audiences. Advertising technology platforms enable advertisers to reach their target audience by delivering ad impressions to online users in real time. In order to identify the best marketing message for a user and to purchase impressions at the right price, we rely heavily on bid prediction and optimization models. Even though the bid prediction models are well studied in the literature, the equally important subject of model evaluation is usually overlooked. Effective and reliable evaluation of an online bidding model is crucial for making faster model improvements as well as for utilizing the marketing budgets more efficiently. In this paper, we present an experimentation framework for bid prediction models where our focus is on the practical aspects of model evaluation. Specifically, we outline the unique challenges we encounter in our platform due to a variety of factors such as heterogeneous goal definitions, varying budget requirements across different campaigns, high seasonality and the auction-based environment for inventory purchasing. Then, we introduce return on investment (ROI) as a unified model performance (i.e., success) metric and explain its merits over more traditional metrics such as click-through rate (CTR) or conversion rate (CVR). Most importantly, we discuss commonly used evaluation and metric summarization approaches in detail and propose a more accurate method for online evaluation of new experimental models against the baseline. Our meta-analysis-based approach addresses various shortcomings of other methods and yields statistically robust conclusions that allow us to conclude experiments more quickly in a reliable manner. We demonstrate the effectiveness of our evaluation strategy on real campaign data through some experiments.Comment: Accepted to ICDM201

Generalized Team Draft Interleaving

Interleaving is an online evaluation method that compares two ranking functions by mixing their results and interpret- ing the users' click feedback. An important property of an interleaving method is its sensitivity, i.e. the ability to obtain reliable comparison outcomes with few user interac- tions. Several methods have been proposed so far to im- prove interleaving sensitivity, which can be roughly divided into two areas: (a) methods that optimize the credit assign- ment function (how the click feedback is interpreted), and (b) methods that achieve higher sensitivity by controlling the interleaving policy (how often a particular interleaved result page is shown). In this paper, we propose an interleaving framework that generalizes the previously studied interleaving methods in two aspects. First, it achieves a higher sensitivity by per- forming a joint data-driven optimization of the credit as- signment function and the interleaving policy. Second, we formulate the framework to be general w.r.t. the search do- main where the interleaving experiment is deployed, so that it can be applied in domains with grid-based presentation, such as image search. In order to simplify the optimization, we additionally introduce a stratifed estimate of the exper- iment outcome. This stratifcation is also useful on its own, as it reduces the variance of the outcome and thus increases the interleaving sensitivity. We perform an extensive experimental study using large- scale document and image search datasets obtained from a commercial search engine. The experiments show that our proposed framework achieves marked improvements in sensitivity over efective baselines on both datasets

Continuous phase amplification with a Sagnac interferometer

We describe a weak value inspired phase amplification technique in a Sagnac interferometer. We monitor the relative phase between two paths of a slightly misaligned interferometer by measuring the average position of a split-Gaussian mode in the dark port. Although we monitor only the dark port, we show that the signal varies linearly with phase and that we can obtain similar sensitivity to balanced homodyne detection. We derive the source of the amplification both with classical wave optics and as an inverse weak value.Comment: 5 pages, 4 figures, previously submitted for publicatio