Kristian Kersting

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Technische Universität Darmstadt ∙ TU Dortmund

Professor in Computer Science Department and Centre for Cognitive Science at TU Darmstadt

Fitted Q-Learning for Relational Domains

We consider the problem of Approximate Dynamic Programming in relational...
06/10/2020 ∙ by Srijita Das, et al. ∙ 0 ∙ share

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Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits

Probabilistic circuits (PCs) are a promising avenue for probabilistic mo...
04/13/2020 ∙ by Robert Peharz, et al. ∙ 10 ∙ share

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CryptoSPN: Privacy-preserving Sum-Product Network Inference

AI algorithms, and machine learning (ML) techniques in particular, are i...
02/03/2020 ∙ by Amos Treiber, et al. ∙ 4 ∙ share

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Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations

Deep neural networks have shown excellent performances in many real-worl...
01/15/2020 ∙ by Patrick Schramowski, et al. ∙ 24 ∙ share

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BERT has a Moral Compass: Improvements of ethical and moral values of machines

Allowing machines to choose whether to kill humans would be devastating ...
12/11/2019 ∙ by Patrick Schramowski, et al. ∙ 0 ∙ share

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Structured Object-Aware Physics Prediction for Video Modeling and Planning

When humans observe a physical system, they can easily locate objects, u...
10/06/2019 ∙ by Jannik Kossen, et al. ∙ 0 ∙ share

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DeepDB: Learn from Data, not from Queries!

The typical approach for learned DBMS components is to capture the behav...
09/02/2019 ∙ by Benjamin Hilprecht, et al. ∙ 0 ∙ share

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Neural Networks for Relational Data

While deep networks have been enormously successful over the last decade...
08/28/2019 ∙ by Navdeep Kaur, et al. ∙ 0 ∙ share

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Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data

Deep neural networks have been successfully applied in learning the boar...
08/19/2019 ∙ by Johannes Czech, et al. ∙ 7 ∙ share

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Random Sum-Product Forests with Residual Links

Tractable yet expressive density estimators are a key building block of ...
08/08/2019 ∙ by Fabrizio Ventola, et al. ∙ 0 ∙ share

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Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks

The performance of deep network learning strongly depends on the choice ...
07/15/2019 ∙ by Alejandro Molina, et al. ∙ 8 ∙ share

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Declarative Learning-Based Programming as an Interface to AI Systems

Data-driven approaches are becoming more common as problem-solving techn...
06/18/2019 ∙ by Parisa Kordjamshidi, et al. ∙ 2 ∙ share

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Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argum...
05/22/2019 ∙ by Andrea Galassi, et al. ∙ 0 ∙ share

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Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures

Bayesian networks are a central tool in machine learning and artificial ...
05/21/2019 ∙ by Xiaoting Shao, et al. ∙ 0 ∙ share

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Was ist eine Professur fuer Kuenstliche Intelligenz?

The Federal Government of Germany aims to boost the research in the fiel...
02/17/2019 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

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SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks

We introduce SPFlow, an open-source Python library providing a simple in...
01/11/2019 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

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Model-based Approximate Query Processing

Interactive visualizations are arguably the most important tool to explo...
11/15/2018 ∙ by Moritz Kulessa, et al. ∙ 0 ∙ share

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Structure Learning for Relational Logistic Regression: An Ensemble Approach

We consider the problem of learning Relational Logistic Regression (RLR)...
08/06/2018 ∙ by Nandini Ramanan, et al. ∙ 14 ∙ share

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Automatic Bayesian Density Analysis

Making sense of a dataset in an automatic and unsupervised fashion is a ...
07/24/2018 ∙ by Antonio Vergari, et al. ∙ 0 ∙ share

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Probabilistic Deep Learning using Random Sum-Product Networks

Probabilistic deep learning currently receives an increased interest, as...
06/05/2018 ∙ by Robert Peharz, et al. ∙ 0 ∙ share

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"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users

Although interactive learning puts the user into the loop, the learner r...
05/22/2018 ∙ by Stefano Teso, et al. ∙ 0 ∙ share

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Neural Conditional Gradients

The move from hand-designed to learned optimizers in machine learning ha...
03/12/2018 ∙ by Patrick Schramowski, et al. ∙ 0 ∙ share

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Lifted Filtering via Exchangeable Decomposition

We present a model for recursive Bayesian filtering based on lifted mult...
01/31/2018 ∙ by Stefan Lüdtke, et al. ∙ 0 ∙ share

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Sum-Product Networks for Hybrid Domains

While all kinds of mixed data -from personal data, over panel and scient...
10/09/2017 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

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Coresets for Dependency Networks

Many applications infer the structure of a probabilistic graphical model...
10/09/2017 ∙ by Alejandro Molina, et al. ∙ 0 ∙ share

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Global Weisfeiler-Lehman Graph Kernels

Most state-of-the-art graph kernels only take local graph properties int...
03/07/2017 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

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A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels

Non-linear kernel methods can be approximated by fast linear ones using ...
03/02/2017 ∙ by Nils M. Kriege, et al. ∙ 0 ∙ share

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Faster Kernels for Graphs with Continuous Attributes via Hashing

While state-of-the-art kernels for graphs with discrete labels scale wel...
10/01/2016 ∙ by Christopher Morris, et al. ∙ 0 ∙ share

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Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage

Migration crisis, climate change or tax havens: Global challenges need g...
06/16/2016 ∙ by Elena Erdmann, et al. ∙ 0 ∙ share

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Lifted Convex Quadratic Programming

Symmetry is the essential element of lifted inference that has recently ...
06/14/2016 ∙ by Martin Mladenov, et al. ∙ 0 ∙ share

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How is a data-driven approach better than random choice in label space division for multi-label classification?

We propose using five data-driven community detection approaches from so...
06/07/2016 ∙ by Piotr Szymański, et al. ∙ 0 ∙ share

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The Symbolic Interior Point Method

A recent trend in probabilistic inference emphasizes the codification of...
05/26/2016 ∙ by Martin Mladenov, et al. ∙ 0 ∙ share

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Propagation Kernels

We introduce propagation kernels, a general graph-kernel framework for e...
10/13/2014 ∙ by Marion Neumann, et al. ∙ 0 ∙ share

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Relational Linear Programs

We propose relational linear programming, a simple framework for combing...
10/12/2014 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

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Mind the Nuisance: Gaussian Process Classification using Privileged Noise

The learning with privileged information setting has recently attracted ...
07/01/2014 ∙ by Daniel Hernández-Lobato, et al. ∙ 0 ∙ share

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Efficient Information Theoretic Clustering on Discrete Lattices

We consider the problem of clustering data that reside on discrete, low ...
10/26/2013 ∙ by Christian Bauckhage, et al. ∙ 0 ∙ share

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Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants

Understanding the adaptation process of plants to drought stress is esse...
10/16/2012 ∙ by Mirwaes Wahabzada, et al. ∙ 0 ∙ share

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'Say EM' for Selecting Probabilistic Models for Logical Sequences

Many real world sequences such as protein secondary structures or shell ...
07/04/2012 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

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Counting Belief Propagation

A major benefit of graphical models is that most knowledge is captured i...
05/09/2012 ∙ by Kristian Kersting, et al. ∙ 0 ∙ share

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Kristian Kersting

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