Monthly Archives: June 2017

Talk by Arnout Tilgenkamp (Flow traders)

You are all cordially invited to the AMLab seminar next week, on Tuesday July 4 at 16:00 in C3.163, where Arnout Tilgenkamp (Flow traders) will give a talk titled “Machine learning at Flow Traders: Past, Present, Future”. Afterwards there are the usual drinks and snacks!

This will be the last seminar before the summer. We will start again in September.

Abstract: Old-school trading used to be a business with very limited use of statistics. Due to increasing automation and continuous technological advancement in infrastructure, statistics have now found their way into trading. In this presentation we will discuss how we as Flow Traders use machine learning and imagine its use in in the future. We will show you examples of how machine learning methods like neural networks and algorithms like gradient descent can help us capture the information content of financial markets.


Talk by Vaishak Belle (University of Edinburgh)

You are all cordially invited to the AMLab seminar on Tuesday June 13 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Vaishak Belle (University of Edinburgh) will give a talk titled “Open-Universe Probabilistic Models”. Afterwards there are the usual drinks and snacks.

A long-standing goal in AI has been to mimic the natural ability of human beings to infer things about sensory inputs and unforeseen data, usually involving a combination of logical and probabilistic reasoning. The last 10 years of research in statistical relational models have demonstrated how one can successfully borrow syntactic devices from first-order logic to define large graphical models over complex interacting random variables, classes, hierarchies, dependencies and constraints. Statistical relational models continue to be widely used for learning in large-scale knowledge bases, probabilistic configurations, natural language processing, question answering, probabilistic programming and automated planning.

While this progress has been significant, there are some fundamental limitations in the expressivity of these models. Statistical relational models make the finite domain assumption: given a clause such as “friends of smokers are smokers themselves”, the set of friends and those who smoke is assumed to be finite and known. It then makes it difficult to talk about unknown atoms and values (e.g., “All of John’s friends are worth more than a million”), categorical assumptions (e.g., “every animal eats”) and identity uncertainty (“James’ partner wore a red shawl”). Currently, approaches often simply ignore this issue, or deal with it in ad hoc ways.

In this work, we attempt to study this systematically. We begin with first-order probabilistic relational models. But now, we allow quantifiers to range over infinite sets, and although that makes matters undecidable in general, we show when limited to certain classes of statements, probabilistic reasoning becomes computable with attractive properties (e.g., satisfies the additive and equivalence axioms of probability in a first-order setting).

Parts of this work appeared at AAAI-17.

Vaishak Belle is a Chancellor’s Fellow/Lecturer at the School of Informatics, University of Edinburgh, UK. Vaishak’s research is in artificial intelligence, specifically on the theme of unifying logic and probability in different guises. Previously, he was at KU Leuven, the University of Toronto, and the Aachen University of Technology. He has co-authored several articles in AI-related venues, and won the Microsoft best paper award at UAI, the Machine learning journal best student paper award at ECML-PKDD, and the Kurt Goedel silver medal.

Talk by Ted Meeds

You are all cordially invited to the AMLab seminar on Tuesday June 6 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Ted Meeds will give a talk titled “Integrating Cancer Genomics Data using Autoencoders”. Afterwards there are the usual drinks and snacks.

Abstract: Integrating multiple sources of molecular measurements (such as RNA, micro RNA, and methylation data) across pan-cancer cohorts is a promising approach to learn general, non-cohort specific, disease profiles. These profiles provide rich representations of patients that can be used to learn novel subtypes and biomarkers, and are useful for survival prognoses and potentially drug-discovery. However, combining cohorts is challenging in part because the main signal in data is tissue-specific. Special care has to be made to avoid simply “learning the tissue”. In this talk I will describe an approach based on the variational auto-encoder, popular in the deep learning community, to learn an unsupervised latent representation of patients (the disease profile) that explicitly removes tissue/cohort information. Preliminary results indicate that the disease profiles carry little information about tissues and by doing so improves the profiles’ usefulness on other validation tasks, such as predicting cohort-specific survival and DNA mutations.