Author name cluster

Tue Herlau

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4 papers

2 author rows

AAAI Conference 2022 Conference Paper

Reinforcement Learning of Causal Variables Using Mediation Analysis

Tue Herlau
Rasmus Larsen

We consider the problem of acquiring causal representations and concepts in a reinforcement learning setting. Our approach defines a causal variable as being both manipulable by a policy, and able to predict the outcome. We thereby obtain a parsimonious causal graph in which interventions occur at the level of policies. The approach avoids defining a generative model of the data, prior pre-processing, or learning the transition kernel of the Markov decision process. Instead, causal variables and policies are determined by maximizing a new optimization target inspired by mediation analysis, which differs from the expected return. The maximization is accomplished using a generalization of Bellman’s equation which is shown to converge, and the method finds meaningful causal representations in a simulated environment.

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NeurIPS Conference 2016 Conference Paper

Completely random measures for modelling block-structured sparse networks

Tue Herlau
Mikkel Schmidt
Morten Mørup

Statistical methods for network data often parameterize the edge-probability by attributing latent traits such as block structure to the vertices and assume exchangeability in the sense of the Aldous-Hoover representation theorem. These assumptions are however incompatible with traits found in real-world networks such as a power-law degree-distribution. Recently, Caron & Fox (2014) proposed the use of a different notion of exchangeability after Kallenberg (2005) and obtained a network model which permits edge-inhomogeneity, such as a power-law degree-distribution whilst retaining desirable statistical properties. However, this model does not capture latent vertex traits such as block-structure. In this work we re-introduce the use of block-structure for network models obeying Kallenberg’s notion of exchangeability and thereby obtain a collapsed model which both admits the inference of block-structure and edge inhomogeneity. We derive a simple expression for the likelihood and an efficient sampling method. The obtained model is not significantly more difficult to implement than existing approaches to block-modelling and performs well on real network datasets.

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AILAW Journal 2013 Journal Article

Cross-categorization of legal concepts across boundaries of legal systems: in consideration of inferential links

Fumiko Kano Glückstad
Tue Herlau
Mikkel N. Schmidt
Morten Mørup

Abstract This work contrasts Giovanni Sartor’s view of inferential semantics of legal concepts (Sartor in Artif Intell Law 17: 217–251, 2009 ) with a probabilistic model of theory formation (Kemp et al. in Cognition 114: 165–196, 2010 ). The work further explores possibilities of implementing Kemp’s probabilistic model of theory formation in the context of mapping legal concepts between two individual legal systems. For implementing the legal concept mapping, we propose a cross-categorization approach that combines three mathematical models: the Bayesian Model of Generalization (BMG; Tenenbaum and Griffiths in Behav Brain Sci 4: 629–640, 2001 ), the probabilistic model of theory formation, i. e. , the Infinite Relational Model (IRM) first introduced by Kemp et al. (The twenty-first national conference on artificial intelligence, 2006, Cognition 114: 165–196, 2010 ) and its extended model, i. e. , the normal-IRM (n-IRM) proposed by Herlau et al. (IEEE International Workshop on Machine Learning for Signal Processing, 2012 ). We apply our cross-categorization approach to datasets where legal concepts related to educational systems are respectively defined by the Japanese- and the Danish authorities according to the International Standard Classification of Education. The main contribution of this work is the proposal of a conceptual framework of the cross-categorization approach that, inspired by Sartor (Artif Intell Law 17: 217–251, 2009 ), attempts to explain reasoner’s inferential mechanisms.

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ICML Conference 2013 Conference Paper

Modeling Temporal Evolution and Multiscale Structure in Networks

Tue Herlau
Morten Mørup
Mikkel N. Schmidt

Many real-world networks exhibit both temporal evolution and multiscale structure. We propose a model for temporally correlated multifurcating hierarchies in complex networks which jointly capture both effects. We use the Gibbs fragmentation tree as prior over multifurcating trees and a change-point model to account for the temporal evolution of each vertex. We demonstrate that our model is able to infer time-varying multiscale structure in synthetic as well as three real world time-evolving complex networks. Our modeling of the temporal evolution of hierarchies brings new insights into the changing roles and position of entities and possibilities for better understanding these dynamic complex systems.

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