**Abstract**: Neural networks on graphs have gained renewed interest in the machine learning community. Recent results have shown that end-to-end trainable neural network models that operate directly on graphs can challenge well-established classical approaches, such as kernel-based methods or methods that rely on graph embeddings (e.g. DeepWalk). In this talk, I will motivate such an approach from an analogy to traditional convolutional neural networks and introduce our recent variant of graph convolutional networks (GCNs) that achieves promising results on a number of semi-supervised node classification tasks. If time permits, I will further introduce two extensions of this basic framework, namely: graph auto-encoders and relational GCNs. While graph auto-encoders provide a novel way of approaching problems like link prediction or recommendation, relational GCNs allow for efficient modeling of directed relational graphs, such as knowledge bases.

**Abstract**: In this talk I want to give a summary over thoughts and experiments we performed over the last couple of weeks in trying to develop a distributed Variational Inference algorithm. Although, theoretically, we can see advantages to the proposed model, as well as cannot immediately see theoretical reasons why it should not work, the experiments demonstrate that learning in the proposed algorithm is unstable and fails catastrophically in the tested settings. I would like to show our intuition and would be glad to discuss and collect your ideas.

**Abstract**: In sequential decision making under uncertainty, an agent attempts to find some function that maps from states to actions, such that a reward signal is maximized, taking both immediate and future reward into account. Under the graph-based perspective, we view the problem of optimal sequential decision making as doing inference in a graphical model.

In this talk I will present some of the research related to this perspective and connect it to recent work in Deep Learning such as Value Iteration Networks and Graph Convolutional Networks.

**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].

**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.

]]>**Abstract:**

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.

]]>**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.

**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.

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.

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**Abstract**:

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.

**Biography**:

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.