A Gentle Introduction to Contextual Bandits

April 1, 2020
 
 

Contextual bandits are a simplified type of reinforcement learning algorithm that use contextual information about the environment to make decisions in real-time and require reward at every step. They can be used for applications such as news recommendation, ads placement on a website, financial portfolio design to name a few. In this post, we give an overview of contextual bandits and explain how they can be used.

 
 

ASROB 10th anniversary

Dec. 31, 2019
 
 

This year 2019, was the 10th anniversary of ASROB, the robotic student society at the University Carlos III in Madrid. It is exciting to see that the organization is still thriving, that new students are joining it and that many projects are being developed. In this post we review the history of the organization and reflect on the reasons for its success.

 
 

Understanding the Sequential Recommender SLi-Rec

Dec. 21, 2019
 
 

Sequential recommenders are a hot topic in Recommendation systems. In this post we do a short summary of one of the latest contributions of Microsoft Research: SLi-Rec. This algorithm has a time-aware component, being able to encode static and dynamic user behavior, and a content-aware component, being able to incorporate contextual information.

 
 

Understanding XLNet and its implications for NLP

Nov. 19, 2019
 
 

Last year BERT revolutionized NLP and since then there have appeared a large number of improvements over the original implementation: MT-DNN, RoBERTa, AlBERTa. The main feature of these models is their autoencoding nature. On the other hand, a group of autorregressive methods have been proposed like Transformer-XL, GPT-2 or XLNet. In this short post we want to give a short overview of XLNet and its nature and how it compares with BERT.

 
 
 
 

In this post we revisit the revision of the Unreasonable Effectiveness of Data in the hope that it empowers the deep learning community to keep revising old ideas.