Talk by Sara Magliacane

You are all cordially invited to the AMLab seminar on Tuesday October 24 at 16:00 in C3.163, where Sara Magliacane will give a talk titled “Joint Causal Inference from Observational and Experimental Datasets”. Afterwards there are the usual drinks and snacks!

Abstract: Joint Causal Inference (JCI) is a recently proposed causal discovery framework that aims to discover causal relations based on multiple observational and experimental datasets, also in the presence of latent variables. Compared with current methods for causal inference, JCI allows to jointly learn both the causal structure and intervention targets by pooling data from different experimental conditions in a systematic way. This systematic pooling also improves the statistical power of the independence tests used to recover the causal relations, while the introduction of context variables can improve the identifiability of causal relations. In this talk I will introduce JCI and show two possible implementations using three recent causal discovery methods from literature, Ancestral Causal Inference [Magliacane et al. 2016], [Hyttinen et al. 2014] and Greedy Fast Causal Inference [Ogarrio et al. 2016]. Moreover, I will show the benefits of JCI in an evaluation on synthetic data and in an application to the flow cytometry dataset from [Sachs et al. 2005].

Talk by Peter O’Connor

You are all cordially invited to the AMLab seminar on Tuesday October 10 at 16:00 in C3.163, where Peter O’Connor will give a talk titled “Towards Event-Based online Learning”. Afterwards there are the usual drinks and snacks!

Abstract: The world that our brains experience is quite different from the world that most of our ML models experience. Most models in machine learning are now trained by randomly sampling data from some training set, updating the model, then repeating. When temporal data is considered, it is usually split into short sequences, where each sequence is considered to be a sample from some underlying distribution of sequences, which we wish to learn. Humans on the other hand, learn online – we receive a single, never-ending sequence of inputs. Moreover, these inputs come in asynchronously, and rather than representing the state of the world at a given time, represent that some aspect of the state of the world has changed.

In this talk, I’ll discuss some work we are doing close this gap, and allow us to apply the methods used in deep learning to the more natural online-learning setting.

Talk by Christos Louizos

You are all cordially invited to the AMLab seminar on Tuesday October 3 at 16:00 in C3.163, where Christos Louizos will give a talk titled “Bayesian Uncertainty and Compression for Deep Learning”. Afterwards there are the usual drinks and snacks!

Deep Learning has shown considerable success in a wide range of domains due its rich parametric form and natural scalability to big datasets. Nevertheless, it has limitations that prevent its adoption in specific problems. It has been shown in recent works that they suffer from over-parametrization as they can be significantly pruned without any loss in performance. This fact essentially shows that there is a lot of wasteful computation and resources, which can lead to large speedups if it is avoided. Furthermore, current neural networks suffer from unreliable uncertainty estimates that prevent their usage in domains that involve critical decision making and safety.

In this talk we will show how these two relatively distinct problems can be addressed under a common framework that involves Bayesian inference. In particular, we will show that by adopting a more elaborate version of Gaussian dropout we can obtain deep learning models that can have robust uncertainty on a variety of tasks and architectures, while simultaneously providing compressed networks where most of the parameters and computation has been removed.

Talk by Stephan Bongers

You are all cordially invited to the AMLab seminar on Tuesday September 19 at 16:00 in C3.163, where Stephan Bongers will give a talk titled “Marginalization of Structural Causal Models with feedback”. Afterwards there are the usual drinks and snacks!

Abstract: Structural causal models (SCMs), also known as non-parametric structural equation models (NP-SEMs), are widely used for causal modeling purposes. This talk consists of two parts: part one gives a rigorous treatment of structural causal models, dealing with measure-theoretic complications that arise in the presence of feedback, and part two deals with the marginalizion of SCMs. In part one we deal with recursive models (those without feedback), models where the solutions to the structural equations are unique, and arbitrary non-recursive models, those where the solutions are non-existent or non-unique. We show how we can reason about causality in these models and show how this differs from the recursive causal perspective. In part two, we address the question how we can marginalize an SCM (possibly with feedback), consisting of endogenous and exogenous variables, to a subset of the endogenous variables? Marginalizing an SCM projects the SCM down to an SCM on a subset of the endogenous variables, leading to a more parsimonious but causally equivalent representation of the SCM. We give an abstract defintion of marginalization and propose two approaches how to marginalize SCMs in a constructive way. Those constructive approaches define both a marginalization operation that effectively removes a subset of the endogenous variables from the model and lead to an SCM that has the same causal semantics as the original SCM. We provide several conditions under which the existence of such marginalizations hold.

Talk by Patrick Forré

You are all cordially invited to the AMLab seminar on Tuesday September 5 at 16:00 in C3.163, where Patrick Forré will give a talk titled “Markov Properties for Probabilistic Graphical Models with Latent Confounding and Feedback Loops”. Afterwards there are the usual drinks and snacks!

Abstract: The elegance and simplicity of Bayesian Networks, i.e. probabilistic graphical models for directed acyclic graphs (DAGs), is rooted in the equivalence of several Markov properties like: the recursive factorization property (rFP) which allows for sparse parametrization, the directed global Markov (dGMP) property encoding all conditional independences or the structural equation property (SEP) which expresses the variables in functional relations.
But as soon as we allow the graphical structure to have feedback loops and/or latent confounders the mentioned equivalences break down. In this talk we will introduce a new graphical structure which allows to represent both latent confounding and feedback loops at once, show how to generalize the most important Markov properties to this case and demonstrate how these Markov properties are logically related to each other. Furthermore, we will indicate how this new layer of theory might be used for causal discovery algorithms in the presence of latent confounders, non-linear functional relations and feedback loops.

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.

Talk by Maurice Weiler

You are all cordially invited to the AMLab seminar on Tuesday May 30 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Maurice Weiler will give a talk titled “Learning steerable filters for rotation-equivariant CNNs”. Afterwards there are the usual drinks and snacks.

Abstract: Besides translational invariances, a broad class of images like medical or astronomical data exhibits rotational invariances. While such a priori knowledge was typically exploited by data augmentation, recent research shifts focus to directly implementing rotational equivariance into model architectures. I will present Steerable Filter CNNs which efficiently incorporate rotation equivariance by learning steerable filters. Two approaches, based on orientation-pooling or group-convolutions, are presented and discussed. A common weight initialization scheme is generalized to networks which learn filter banks as a linear combination of a fixed system of atomic filters.

Talk by Joris Mooij

You are all cordially invited to the AMLab seminar on Tuesday May 23 at 16:00 in C3.163 (FNWI, Amsterdam Science Park), where Joris Mooij will give a talk titled “Causal Transfer Learning with Joint Causal Inference”. Afterwards there are the usual drinks and snacks.

Abstract: The gold standard to discover causal relations relies on experimentation. Over the last decades, an intriguing alternative has been proposed: constraint-based causal discovery methods can sometimes infer causal relations from certain statistical patterns in purely observational data. Even though this works nicely on paper, in practice the conclusions of such methods are often unreliable. We introduce Joint Causal Inference (JCI), a novel constraint-based method for causal discovery from multiple data sets that elegantly unifies both approaches. JCI aims to combine the best of two worlds: the reliability offered by experimentation, and the flexibility of not having to perform all theoretically possible experiments. We apply JCI in a causal transfer learning problem and use it to predict how a target variable is distributed (given observations of other variables) in new experiments. We illustrate this with examples where JCI makes the correct predictions, whereas standard feature selection methods make arbitrarily large prediction errors.