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Found 6 papers, 1 papers with code
Effect Size Estimation for Duration Recommendation in Online Experiments: Leveraging Hierarchical Models and Objective Utility Approaches
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency.
Estimating the Value of Evidence-Based Decision Making
Business/policy decisions are often based on evidence from randomized experiments and observational studies.
Experimentation Platforms Meet Reinforcement Learning: Bayesian Sequential Decision-Making for Continuous Monitoring
With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible.
A Bayesian Model for Online Activity Sample Sizes
Given observations on the number of unique users participating in an initial period, we present a simple but novel Bayesian method for predicting the number of additional individuals who will participate during a subsequent period.
Nearly Isometric Embedding by Relaxation
Many manifold learning algorithms aim to create embeddings with low or no distortion (i. e. isometric).
megaman: Manifold Learning with Millions of points
Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data.
