Bayesian Optimization
Author : By Deepa Korani (dmk333@cornell.edu)
Steward : Fenqgi You
Introduction
Bayesian Optimization is a sequential model-based approach to solving problems. In particular, it prescribes a prior belief over the possible objective functions, and then sequentially refine the model as data are observed via Bayesian posterior updating. [1]
Bayesian Optimization is useful in machine learning. Since Machine Learning consists of black box optimization problem where the objective function is a black box function[2], and the analytical expression for the function is unknown. Bayesian optimization can be useful here. They attempt to find the global optimum in a minimum number of steps.
Bayesian Optimization has shown tremendous solutions for a wide variety of design problems. Certain application include; robotics, environmental monitoring, combinatorial optimization, adaptive Monte Carlo, reinforcement learning. [3]
Theory, Methodology and or Algorithmic Discussion
Bayesian Optimization incorporates the prior belief about
Applications
Bayesian Optimization has shown a wide variety of interest in areas of data science and Chemical Engineering:
- A/B Testing - Though the idea of A/B testing dates back to the early days of advertising in the form of so-called focus groups, the advent of the internet and smartphones has given web and app developers a new forum for implementing these tests at unprecedented scales. By redirecting small fractions of user traffic to experimental designs of an ad, app, game, or website, the developers can utilize noisy feedback to optimize any observable metric with respect to the product’s configuration. In fact, depending on the particular phase of a product’s life, new subscriptions may be more valuable than revenue or user retention, or vice versa; the click-through rate might be the relevant objective to optimize for an ad, whereas for a game it may be some measure of user engagement. The crucial problem is how to optimally query these subsets of users in order to find the best product with high probability within a predetermined query budget, or how to redirect traffic sequentially in order to optimize a cumulative metric while incurring the least opportunity cost.
- Recommender Systems - Generally Recommender systems are used by online content providers to recommend products to their subscribers. Recommending the right product to subscribers is important in order to improvise the revenue in case for e-commerce sits, or consumption sites.
- Natural Language Processing and Text- Bayesian Optimization has been applied to improve text extraction. [4]
- Environmental Monitoring and Sensor Networks - Sensor Networks allow to monitor important environment conditions; such as temperature, concentration of pollutants in the atmosphere, soil and ocean. These networks make noise measurements that are interpolated to produce a global model of interest. In some cases, these sensors are expensive to activate but one can answer important questions like what is the hottest or coldest spot in a building by activating a relatively small number of sensors. Bayesian optimization was used for this task and the similar one of finding the location of greatest highway traffic congestion [5]