See a map of all the places I've given a talk!
Talk, UiB Logic and AI Seminar, Bergen, Norway
This talk introduces the formalism of structural causal models, it explains the idea of abstractions between causal models, provides the definition of alpha-abstractions, and describes a solution for abstraction learning.
Talk, UiB ML Seminar, Bergen, Norway
Multi-armed bandits are a standard formalism to represent simple yet realistic decision-making problems in which a policy-maker has to find an optimal balance between choosing well-known options or exploring new alternatives. Traditionally, such decision-making problems are encoded using a single model; however, in reality, a decision-maker may have multiple related models of the same problem at different level of resolution, each one providing information about the value and the effects of the available choices. In this talk we will recall the standard multi-armed bandits framework, extend it to a causal settings, and explain how multiple models can be related via causal abstractions. Finally, we will discuss a few theoretical results about transporting information across the models via abstraction using basic algorithms inspired by reinforcement learning.
Talk, TMF-UiB Career Program, Solstrand, Norway
This talk provides a conceptual introduction to machine learning for a multidisciplinary audience.
Talk, UiB AI, Bergen, Norway
This talk reviews the basic notions of causality and causal abstraction, defines the problem of causal abstraction learning, and suggests a solution based on genetic algorithms.
Talk, UiB AI, Bergen, Norway
This talk will touch on two aspects of human reasoning that are crucial for AI: causal reasoning and multi-level reasoning. We will discuss the role and the relevance of causal models in machine learning systems, as well as the ubiquity of multi-level reasoning across applications. We will then review some recent approaches based on causal abstraction, aimed at integrating causal reasoning and multi-level reasoning. We will conclude by suggesting possible developments and deployment of these methods.
Talk, Lead AI, Bergen, Norway
In this talk we give a quick overview of the Machine Learning group at UiB and of its research interests and directions.
Talk, HVL AI Seminar Series, Bergen, Norway
In this presentation we review the definition of structural causal models and we introduce the problem of relating these models via an abstraction map. We formalize the problem of learning such a causal abstraction map as a minimizer of an abstraction error expressed in terms of interventional consistency, and we discuss some of the challenges involved in this optimization problem. We then present an approach based on a relaxation and parametrization of the problem, leading to a solution based on differentiable programming. The solution approach is evaluated both on synthetic and real-world data.
Talk, UiB Machine Learning Seminar Series, Bergen, Norway
In this presentation we review the definition of structural causal models and we introduce the problem of relating these models via an abstraction map. We formalize the problem of learning such a causal abstraction map as a minimizer of an abstraction error expressed in terms of interventional consistency, and we discuss some of the challenges involved in this optimization problem. We then present an approach based on a relaxation and parametrization of the problem, leading to a solution based on differentiable programming. The solution approach is evaluated both on synthetic and real-world data.
Talk, Seminar at the University of Kyoto, Kyoto, Japan
In this talk we discuss how rigorous relations between causal models may be defined and quantitatively evaluated. We will start with a quick introduction to the popular formalism of structural causal models. Next, we will review alternative proposals for expressing relations of abstractions between these models. We will then focus on one particular framework, and show how a notion of abstraction error can be introduced in this setup. Finally, we will discuss some of the limitations of this measure, and how alternative measures of error may be developed in order to capture different aspects of abstraction and fit different aims. We will conclude with a few considerations about possible future developments of this theory of abstraction.
Talk, IJCAI 2023, Macau, China
In this presentation we quickly review the idea of defining an abstraction between structural causal models and we present the standard measure of abstraction error proposed in the literature. We then consider some potential limitations when using this single measure to assess or learn abstractions. To overcome this limit, we propose an extension of the original definition of abstraction approximation, we derive new measures of abstraction error, and we discuss theoretical and applied properties of these new measures.
Talk, Battery Modelling Group Weekly Seminar (online), Pittsburgh, US
Modelling complex systems and processes, such as battery manufacturing, is a significant scientific and technical challenge. Mathematics, statistics and machine learning provide useful tools to tackle this problem. In this talk, we will focus on the recent formalism of structural causal models (SCM) and casual abstractions (CA). We will first offer a high-level introduction to SCMs and CA, discussing in particular their importance and relevance for modelling. We will then make a reference to our original methodology for learning CAs. Finally, we will showcase our preliminary results on the problem of modelling one stage of the lithium-ion battery manufacturing process, demonstrating the potential for integrating data collected by different research groups.
Talk, Trial Lecture at UiB, Bergen, Norway
In this talk we will introduce one of the most important formalisms to represent causal systems in computer science. We will start with a brief review of causality, highlighting the meaning of causal queries and the limitations of standard statistics and machine learning in answering them. To address these shortcomings, we will present the formalism of structural causal models (SCMs). We will then show how these models can be used to rigorously answer different types of causal questions, including observational, interventional and counterfactual questions. Finally, we will conclude by discussing how this formalization gives rise to a rich theory of causality, and how the ideas underlying causality have strong and promising intersections with artificial intelligence and machine learning.
Talk, CLeaR 2023, Tubingen, Germany
In this presentation we review the definition of abstraction between structural causal models and we frame the problem of learning a mapping between them. We discuss the challenges of learning a causal abstraction that minimizes the abstraction error in terms of interventional consistency. We then suggest an approach based on a relaxation and parametrization of the problem, leading to a solution based on differentiable programming. The solution approach is evaluated both on synthetic and real-world data.
Talk, Statistics Department Research Seminar, Warwick, United Kingdom
Causal models can represent an identical system or phenomenon at different levels of abstraction. In this talk, we will focus on structural causal models (SCM) and review two frameworks which have been proposed to express a relation of abstraction between SCMs and to measure the interventional consistency of an abstraction. We will then discuss some current directions of research, including the problem of learning abstractions.
Talk, UAI 2022 Workshop on Causal Representation Learning (online), Eindhoven, Netherlands
In this presentation we first offer a review of definitions of abstractions proposed in the literature, and then we propose a framework to align these definitions and evaluate their properties. We suggest analyzing abstractions on two layers (a structural layer and a distributional layer) and we review some basic properties that may be enforced on maps defined on each layer. We suggest that this framework may contribute to a better understanding of different forms of abstraction, as well as providing a way to tailor application-specific definitions of abstraction.
Talk, Warwick Machine Learning Group Reading Group (online), Warwick, United Kingdom
In this presentation we consider the problem of relating causal models representing the same phenomenon or system at different levels of abstraction. A given system may be represented with more or less details according to the resources or the need of a modeler; switching between descriptions at different levels of abstraction is not trivial, and it raises questions of consistency. In this presentation, we will focus in particular on structural causal models (SCM) and we will express properties of consistency in this context. We will then present two formalisms for defining a relation of abstraction between SCMs: an approach based on the definition of a transformation between the outcomes of models, and an approach based on the definition of a mapping between the structure of models. We will then conclude with some observations and some questions regarding this current direction of research.
Talk, Oslo Metropolitan University (online), Oslo, Norway
These slides analyze the application of reinforcement learning for modelling the problem of penetration testing in computer security. After a conceptual overview of reinforcement learning, we discuss which are the specific challenges in modeling penetration testing as a game that may be solved by a reinforcement learning agent. Finally, we present some of the work done by the research group at the University of Oslo on this topic, including conceptual modelling and preliminary practical implementations of reinforcement learning environments and agents.
Talk, University of Warwick (online), Warwick, United Kingdom
These slides provide a synthetic overview of the problem of relating structural causal models (SCMs) at different levels of abstraction. We define the problem and discuss the desiderata of our solution. We present a few of the existing formalizations and solutions offered in the literature. We then conclude highglighting interesting future direction of research in this area.
Talk, University of Utrecht (online), Utrecht, Netherlands
Structural causal models (SCMs) constitute a rigorous and tested formalism to deal with causality in many fields, including artificial intelligence and machine learning. Systems and phenomena may be modelled as SCMs and then studied using the tools provided by the framework of causality. A given system can, however, be modelled at different levels of abstraction, depending on the aims or the resources of a modeller. The most exemplar case is probably statistical physics, where a thermodynamical system may be represented both as a collection of microscopic particles or as a single body with macroscopic properties. In general, however, switching between models with different granularities presents non-trivial challenges and raises questions of consistency. These slides will first provide a brief introduction to SCMs, and then consider how we can express the problem of relating SCMs representing the same phenomenon at different levels of abstraction. Finally, we will discuss open challenges and present some existing solutions, as well as pointing towards possible future directions of research.
Talk, Keynote at IEEE Big Data CyberHunt Workshop (online), Atlanta, GA, USA
These slides provide an overview on the topic of the security of machine learning systems. We identify the two main attack surfaces inherent in machine learned systems, and we then provide a review of the main attack and defenses, heavily relying on analogical reasoning to illustrate and explain these methods. The presentation ends with remarks on the practical implications of these vulnerabilities and the current directions of research.
Talk, University of Innsbruck (online), Innsbruck, Austria
These slides provides a quick conceptual introduction to neural networks for supervised learning, and review some hypothesis and theories meant to explain the generalization performance of learning. The presentation then focuses on one of these possible interpretative frameworks, information bottleneck, and discusses its possible application to understand the dynamics of unsupervised learning algorithms, such as sparse filtering.
Talk, ECML Workshop on AI for Social Good (SoGood), Ghent, Belgium
This talk briefly summarizes several observations on the political value of adopting machine learning systems in criminal justice presented through the lens of Left Realism.
Talk, ECML Workshop on Uncertainty in Machine Learning (WUML), Ghent, Belgium
This talk provides an overview of the problem of aggregating several probablistic structural causal models and it offers a walkthrough of our algorithm applied to a toy case scenario.
Talk, Robotics and Intelligent Systems (ROBIN) group lunch seminar, University of Oslo, Oslo, Norway
This short presentation introduces the method of information bottleneck by describing its formulation and by illustrating its application in analyzing the behaviour of deep neural networks. The presentation ends discussing the problem of using a similar information-theoretic method to study the behaviour of unsupervised learning algorithms, focusing in particular on the analysis of the sparse filtering algorithm.
Talk, Oslo Analytics Scientific Advisory Board Presentation, Oslo, Norway
These slides present the research project of modelling hacking in the form of capture-the-flag (CTF) games as problems solvable by agents trained by reinforcement learning (RL). The main assumptions and challengese are presented, along with some preliminary results.
Talk, Oslo Machine Learning Meetup, Oslo, Norway
This talk aims at providing an overall understanding of the role of causal modelling, and its relationship to machine learning. We are going to introduce casual models following the popular approach based on structural causal models proposed by Pearl, and show how they can capture the notion of causal relations. We will consider paradigmatic casual problems (causal inference and causal discovery) and discuss how they can be tackled. Finally, we will briefly explore connections between causality and machine learning, touching on topics such as learning with causal assumptions, using counterfactuals to assess fairness, and expressing reinforcement learning problems in causal terms.
Talk, AI Seminar, Oslo Metropolitan University, Oslo, Norway
n this presentation we are going to introduce causal model from the point of view of computer science, following the approach based on structural causal models proposed by Pearl. We will start by showing the place of causality theory and by discussing its relationship with standard statistics. We will then present graphical models (directed acyclic grpahs, Bayesian networks, causal Bayesian networks, and structural causal models) to address causal questions. We will then review some paradigmatic problems that arise in the field of causality, and how they can be solved.
Talk, CyberControl workshop, University of Oslo, Oslo, Norway
This talk provides a short presentations of three perspectives to explore the intersection of cybersecurity and machine learning. It examines an instrumental perspective (in which ML is seen as a tool), a systemic perspective (in which ML is seen as component of a system to defend), and a societal perspective (in which ML is seen as a part of societal processes). Each perspective is reconnected with specific areas of research (cybersecurity, adversarial learning, AI safety).
Talk, Workshop on the Security of Autonomous Systems, Kjeller, Norway
This talk provides an overview of the research in the fields of adversarial machine learning and AI safety. The first part of the talk gives a brief introduction to machine learning from a conceptual point of view; the second and the third part respectively illustrates some representative attacks and defenses for machine learning systems; and, finally, the last part lists safety concerns related to machine learning and artificial intelligence. (This presentation has some overlap with the previous talk “Research Challenges for Applying Machine Learning in Cybersecurity”)
Talk, European Conference on Multi-Agent Systems, University of Bergen, Bergen, Norway
This talk provides an overview of the problem of aggregating several probablistic structural causal models and it offers a walkthrough of our algorithm applied to a toy case scenario.
Talk, AFSecurity Seminar, University of Oslo, Oslo, Norway
This talk provides an overview of some topics at the intersection of cybersecurity and machine learning with the aim of illustrating the possibilities offered by machine learning and surveying recent promising lines of research at the border between the two disciplines. The first part of the talk gives a brief introduction to machine learning from a conceptual point of view. The second part then explores research topics in three main domains: applications of machine learning to security; security aspects of machine learning; and, finally, safety concerns related to machine learning.
Talk, Machine Learning and Optimization Seminar, University of Manchester, Manchester, UK
This talk is meant to be a simple introduction to Principe’s framework for Information Theoretic Learning. We will first review a standard information theoretic measure, going through its derivation, its properties and its limitation. We will then derive a more general form of this information theoretic measure, and we will use it to compute statistical estimators. Finally, we will define an information theoretic loss function that can be used for learning.
Talk, PhD Overview, University of Manchester, Manchester, UK
This talk provides an overview of the problem of disentagling emotional speech features and it offers an analysis of several approaches to tackle this problem along with preliminary results.
Talk, Machine Learning and Optimization Seminar, University of Manchester, Manchester, UK
Sparse filtering is an algorithm for unsupervised learning proposed in 2011. The authors introduced this algorithm as a paradigm of feature distribution learning, contrasting it with more traditional data distribution learning. In this seminar, we will explore the ideas behind sparse filtering following the original paper published in 2011. We will first discuss the general idea of feature distribution learning; then, we will present the specific algorithm for sparse filtering; finally, we will conclude with a discussion of the algorithm and a summary of further developments since the publication of the original paper.
Talk, Seminar on Scientific Methods II, University of Manchester, Manchester, UK
This talk provides a simple conceptual introduction to the topic of deep learning. It coarsely traces the development of neural network models, and it tries to clarify ideas, architectures, and the relationship between them.
Talk, Philosophy of Religion Graduate Seminar, University of Oxford, Oxford, UK
This talk provides a brief introduction on the life and the time of Unamuno, and then it discusses its philosophy of religion as presented in his main work, The Tragic Sense of Life.