LinUCB | Contextual bandit algorithm called LinUCB / Linear Upper | Reinforcement Learning library

 by   thunfischtoast Java Version: 1.0 License: GPL-3.0

kandi X-RAY | LinUCB Summary

kandi X-RAY | LinUCB Summary

LinUCB is a Java library typically used in Artificial Intelligence, Reinforcement Learning applications. LinUCB has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. However LinUCB has 1 bugs. You can download it from GitHub.

Contextual bandit algorithm called LinUCB / Linear Upper Confidence Bounds as proposed by Li, Langford and Schapire. We implemented the two version, one with disjoint and and one with hybrid linear models, as mentioned in the paper. See src/de/thunfischtoast/ for basic usage example as inspired by .

            kandi-support Support

              LinUCB has a low active ecosystem.
              It has 25 star(s) with 10 fork(s). There are 1 watchers for this library.
              It had no major release in the last 12 months.
              There are 1 open issues and 0 have been closed. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of LinUCB is 1.0

            kandi-Quality Quality

              LinUCB has 1 bugs (0 blocker, 1 critical, 0 major, 0 minor) and 24 code smells.

            kandi-Security Security

              LinUCB has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              LinUCB code analysis shows 0 unresolved vulnerabilities.
              There are 2 security hotspots that need review.

            kandi-License License

              LinUCB is licensed under the GPL-3.0 License. This license is Strong Copyleft.
              Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

            kandi-Reuse Reuse

              LinUCB releases are available to install and integrate.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              LinUCB saves you 107 person hours of effort in developing the same functionality from scratch.
              It has 272 lines of code, 12 functions and 4 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed LinUCB and discovered the below as its top functions. This is intended to give you an instant insight into LinUCB implemented functionality, and help decide if they suit your requirements.
            • Returns the viable arm
            • Return the best payoff for each arm
            • Receive a multiple rewards
            • Receive a reward
            Get all kandi verified functions for this library.

            LinUCB Key Features

            No Key Features are available at this moment for LinUCB.

            LinUCB Examples and Code Snippets

            Javadot img1Lines of Code : 8dot img1License : Strong Copyleft (GPL-3.0)
            copy iconCopy
              title={A contextual-bandit approach to personalized news article recommendation},
              author={Li, Lihong and Chu, Wei and Langford, John and Schapire, Robert E},
              booktitle={Proceedings of the 19th international conf  

            Community Discussions


            Contextual-Bandit Approach: Algorithm 1 LinUCB with disjoint linear models
            Asked 2019-Feb-25 at 01:34

            I am trying to implement the algorithm called LinUCB with disjoint linear models from this paper "A Contextual-Bandit Approach to Personalized News Article Recommendation"

            This is the algorithm: Algorithm 1 LinUCB with disjoint linear models

            I am confused about the features vector Xt,a (I highlighted on the algorithm). Is the feature vector related to information (context) of the article(arm) or the user?

            I would appreciate your help. Thank you



            Answered 2018-Jul-13 at 22:53

            The feature vector x_t,a applies to both the user and the arm.

            The vector xt,a summarizes information of both the user ut and arm a, and will be referred to as the context.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install LinUCB

            You can download it from GitHub.
            You can use LinUCB like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the LinUCB component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer For Gradle installation, please refer .


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
          • HTTPS


          • CLI

            gh repo clone thunfischtoast/LinUCB

          • sshUrl


          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Consider Popular Reinforcement Learning Libraries

            Try Top Libraries by thunfischtoast


            by thunfischtoastPython


            by thunfischtoastPython