We presented at the European Conference on Artificial Intelligence (ECAI) our work “An Online Incremental Learning Approach for Configuring Multi-arm Bandits Algorithms”. Our approach employs Bayesian optimisation in an on-line way to dynamically adapt the hyper-parameters of multi-arm bandit algorithms. We are interested in uncertain and dynamic environments, such as on-line web server optimisation. Our paper can be found here, and our source code is on Github.

Photo from DCC-UFMG.

Our previous PhD student, Washington L. S. Ramos, was awarded the best thesis award in the workshop of thesis and dissertations of the SIBGRAPI conference. SIBGRAPI is an international conference in Computer Graphics and Computer Vision, organised by the Brazilian Computing Society. It is a key target in Brazil, where Washington is based.

Washington developed reinforcement learning techniques to automatically accelerate unedited videos based on textual data. His agents are able to dynamically decide the relevance of the current video frame, and decide the current video acceleration at execution time, generating a video with varying speed-up rates according to the relevance of the content.

A demonstration video is available at https://youtu.be/u6ODTv7-9C4, and his thesis extended abstract is available here.