807 research outputs found
Beyond A/B Testing: Sequential Randomization for Developing Interventions in Scaled Digital Learning Environments
Randomized experiments ensure robust causal inference that are critical to
effective learning analytics research and practice. However, traditional
randomized experiments, like A/B tests, are limiting in large scale digital
learning environments. While traditional experiments can accurately compare two
treatment options, they are less able to inform how to adapt interventions to
continually meet learners' diverse needs. In this work, we introduce a trial
design for developing adaptive interventions in scaled digital learning
environments -- the sequential randomized trial (SRT). With the goal of
improving learner experience and developing interventions that benefit all
learners at all times, SRTs inform how to sequence, time, and personalize
interventions. In this paper, we provide an overview of SRTs, and we illustrate
the advantages they hold compared to traditional experiments. We describe a
novel SRT run in a large scale data science MOOC. The trial results
contextualize how learner engagement can be addressed through inclusive
culturally targeted reminder emails. We also provide practical advice for
researchers who aim to run their own SRTs to develop adaptive interventions in
scaled digital learning environments
Dynamic Estimation of Rater Reliability using Multi-Armed Bandits
One of the critical success factors for supervised machine learning is the quality of target values, or predictions, associated with training instances. Predictions can be discrete labels (such as a binary variable specifying whether a blog post is positive or negative) or continuous ratings (for instance, how boring a video is on a 10-point scale). In some areas, predictions are readily available, while in others, the eort of human workers has to be involved. For instance, in the task of emotion recognition from speech, a large corpus of speech recordings is usually available, and humans denote which emotions are present in which recordings
Performative Prediction: Past and Future
Predictions in the social world generally influence the target of prediction,
a phenomenon known as performativity. Self-fulfilling and self-negating
predictions are examples of performativity. Of fundamental importance to
economics, finance, and the social sciences, the notion has been absent from
the development of machine learning. In machine learning applications,
performativity often surfaces as distribution shift. A predictive model
deployed on a digital platform, for example, influences consumption and thereby
changes the data-generating distribution. We survey the recently founded area
of performative prediction that provides a definition and conceptual framework
to study performativity in machine learning. A consequence of performative
prediction is a natural equilibrium notion that gives rise to new optimization
challenges. Another consequence is a distinction between learning and steering,
two mechanisms at play in performative prediction. The notion of steering is in
turn intimately related to questions of power in digital markets. We review the
notion of performative power that gives an answer to the question how much a
platform can steer participants through its predictions. We end on a discussion
of future directions, such as the role that performativity plays in contesting
algorithmic systems
On Connections Between Machine Learning And Information Elicitation, Choice Modeling, And Theoretical Computer Science
Machine learning, which has its origins at the intersection of computer science and statistics, is now a rapidly growing area of research that is being integrated into almost every discipline in science and business such as economics, marketing and information retrieval. As a consequence of this integration, it is necessary to understand how machine learning interacts with these disciplines and to understand fundamental questions that arise at the resulting interfaces. The goal of my thesis research is to study these interdisciplinary questions at the interface of machine learning and other disciplines including mechanism design/information elicitation, preference/choice modeling, and theoretical computer science
On Experimentation in Software-Intensive Systems
Context: Delivering software that has value to customers is a primary concern of every software company. Prevalent in web-facing companies, controlled experiments are used to validate and deliver value in incremental deployments. At the same that web-facing companies are aiming to automate and reduce the cost of each experiment iteration, embedded systems companies are starting to adopt experimentation practices and leverage their activities on the automation developments made in the online domain. Objective: This thesis has two main objectives. The first objective is to analyze how software companies can run and optimize their systems through automated experiments. This objective is investigated from the perspectives of the software architecture, the algorithms for the experiment execution and the experimentation process. The second objective is to analyze how non web-facing companies can adopt experimentation as part of their development process to validate and deliver value to their customers continuously. This objective is investigated from the perspectives of the software development process and focuses on the experimentation aspects that are distinct from web-facing companies. Method: To achieve these objectives, we conducted research in close collaboration with industry and used a combination of different empirical research methods: case studies, literature reviews, simulations, and empirical evaluations. Results: This thesis provides six main results. First, it proposes an architecture framework for automated experimentation that can be used with different types of experimental designs in both embedded systems and web-facing systems. Second, it proposes a new experimentation process to capture the details of a trustworthy experimentation process that can be used as the basis for an automated experimentation process. Third, it identifies the restrictions and pitfalls of different multi-armed bandit algorithms for automating experiments in industry. This thesis also proposes a set of guidelines to help practitioners select a technique that minimizes the occurrence of these pitfalls. Fourth, it proposes statistical models to analyze optimization algorithms that can be used in automated experimentation. Fifth, it identifies the key challenges faced by embedded systems companies when adopting controlled experimentation, and we propose a set of strategies to address these challenges. Sixth, it identifies experimentation techniques and proposes a new continuous experimentation model for mission-critical and business-to-business. Conclusion: The results presented in this thesis indicate that the trustworthiness in the experimentation process and the selection of algorithms still need to be addressed before automated experimentation can be used at scale in industry. The embedded systems industry faces challenges in adopting experimentation as part of its development process. In part, this is due to the low number of users and devices that can be used in experiments and the diversity of the required experimental designs for each new situation. This limitation increases both the complexity of the experimentation process and the number of techniques used to address this constraint
- …