Monthly Archives: November 2015

Talk by Artem Grotov

You are all cordially invited to the next AMLab colloquium on Tuesday, November 24 at 16:00 in C3.163, where Artem Grotov will give a talk titled “Self-learning search engines”.

Abstract: Search engines like Google, Yahoo and Bing provide users with high quality search results. They do so by taking into account thousands of factors that affect the order of the results shown on the search engine result page. These factors include textual query-website similarity, popularity of the website, the time since the website was last updated, click through rates and many others. It comes at a great cost – the search engine engineers have to create huge annotated datasets in order to learn how to combine these factors. Smaller companies cannot afford to go through this process, this is why we, as users often have poor search experience outside of large commercial search engines. For example the quality of search inside university websites, corporate websites and online stores is often very poor. In this talk I will discuss a potential solution to this problem. This solution is to have the search engine learn how to combine numerous search criteria by observing how users interact with the search engine. This way it is possible to provide excellent user experience without the cost of annotating large amounts of data. Additionally the search engine adapts to real users and not to the people who annotated the data and does so in real time.

Talk by Antti Hyttinen

You are all cordially invited to the next AMLab colloquium next Tuesday, November 17 at 16:00 in C3.163, where Antti Hyttinen from the Helsinki Institute for Information Technology and the University of Helsinki will give a talk titled “Constraint Satisfaction Approach to Causal Inference”.

Abstract: A causal model and the accompanied causal graph explain how a system behaves when interventions are applied to it. In constraint-based causal discovery, statistical independence and dependence relations testable in the observed data are used to reason about the properties of the causal  graph. The presentation will outline how constraint-based causal discovery can be achieved using constraint satisfaction. This includes encodings of the d-separation property in propositional logic and solving the constraint satisfaction problem using SAT-solvers, Weighted Partial MaxSAT-solvers and Answer Set Programming solvers. I will also outline how the framework can be used for other problems, such as causal effect estimation.