Kristian Kersting

Orcid: 0000-0002-2873-9152

Affiliations:
  • TU Darmstadt, Computer Science Department, Germany
  • TU Darmstadt, Centre for Cognitive Science, Germany
  • TU Dortmund, Department of Computer Science, Germany
  • University of Bonn, Faculty of Agriculture, Germany
  • Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, Germany
  • Massachusetts Institute of Technology, Computer Science and Artificial Intelligence Laboratory (CSAIL), Cambridge, MA, USA
  • University of Freiburg, Machine Learning Laborator, Germany (PhD 2005)


According to our database1, Kristian Kersting authored at least 436 papers between 2000 and 2025.

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Bibliography

2025
APT: Alarm Prediction Transformer.
Expert Syst. Appl., 2025

2024
Effective Risk Detection for Natural Gas Pipelines Using Low-Resolution Satellite Images.
Remote. Sens., January, 2024

Structural causal models reveal confounder bias in linear program modelling.
Mach. Learn., 2024

Does CLIP Know My Face?
J. Artif. Intell. Res., 2024

Diagnostic Reasoning in Natural Language: Computational Model and Application.
CoRR, 2024

χSPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains.
CoRR, 2024

Problem Solving Through Human-AI Preference-Based Cooperation.
CoRR, 2024

T-FREE: Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.
CoRR, 2024

OCALM: Object-Centric Assessment with Language Models.
CoRR, 2024

Towards Probabilistic Clearance, Explanation and Optimization.
CoRR, 2024

Neural Concept Binder.
CoRR, 2024

EXPIL: Explanatory Predicate Invention for Learning in Games.
CoRR, 2024

LLavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment.
CoRR, 2024

HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning.
CoRR, 2024

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Program.
CoRR, 2024

Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models.
CoRR, 2024

Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning.
CoRR, 2024

ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming.
CoRR, 2024

Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits.
CoRR, 2024

United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once.
CoRR, 2024

DeiSAM: Segment Anything with Deictic Prompting.
CoRR, 2024

Right on Time: Revising Time Series Models by Constraining their Explanations.
CoRR, 2024

Exploring the Adversarial Capabilities of Large Language Models.
CoRR, 2024

Amplifying Exploration in Monte-Carlo Tree Search by Focusing on the Unknown.
CoRR, 2024

Pix2Code: Learning to Compose Neural Visual Concepts as Programs.
CoRR, 2024

Where is the Truth? The Risk of Getting Confounded in a Continual World.
CoRR, 2024

BOWLL: A Deceptively Simple Open World Lifelong Learner.
CoRR, 2024

Checkmating One, by Using Many: Combining Mixture of Experts with MCTS to Improve in Chess.
CoRR, 2024

Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help You.
CoRR, 2024

Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents.
CoRR, 2024

OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments.
Proceedings of the 1st Reinforcement Learning Conference, 2024

"Do Not Disturb My Circles!" Identifying the Type of Counterfactual at Hand (Short Paper).
Proceedings of the Robust Argumentation Machines - First International Conference, 2024

Divergent Token Metrics: Measuring degradation to prune away LLM components - and optimize quantization.
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Exploiting Cultural Biases via Homoglyphs inText-to-Image Synthesis (Abstract Reprint).
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Learning to Intervene on Concept Bottlenecks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Mechanistic Design and Scaling of Hybrid Architectures.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Be Careful What You Smooth For: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Learning Large DAGs is Harder than you Think: Many Losses are Minimal for the Wrong DAG.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Adaptive Rational Activations to Boost Deep Reinforcement Learning.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

T-FREE: Subword Tokenizer-Free Generative LLMs via Sparse Representations for Memory-Efficient Embeddings.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

Defending Our Privacy with Backdoors.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers.
Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

LEDITS++: Limitless Image Editing Using Text-to-Image Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Deep Classifier Mimicry without Data Access.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Modeling Multiple Adverse Pregnancy Outcomes: Learning from Diverse Data Sources.
Proceedings of the Artificial Intelligence in Medicine - 22nd International Conference, 2024

2023
Explanatory Interactive Machine Learning.
Bus. Inf. Syst. Eng., December, 2023

αILP: thinking visual scenes as differentiable logic programs.
Mach. Learn., May, 2023

A typology for exploring the mitigation of shortcut behaviour.
Nat. Mac. Intell., March, 2023

Learning with privileged and sensitive information: a gradient-boosting approach.
Frontiers Artif. Intell., February, 2023

AlphaZe∗∗: AlphaZero-like baselines for imperfect information games are surprisingly strong.
Frontiers Artif. Intell., February, 2023

Causal Parrots: Large Language Models May Talk Causality But Are Not Causal.
Trans. Mach. Learn. Res., 2023

Not All Causal Inference is the Same.
Trans. Mach. Learn. Res., 2023

Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis.
J. Artif. Intell. Res., 2023

Scalable Neural-Probabilistic Answer Set Programming.
J. Artif. Intell. Res., 2023

From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?
CoRR, 2023

SPARE: A Single-Pass Neural Model for Relational Databases.
CoRR, 2023

Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data.
CoRR, 2023

Distilling Adversarial Prompts from Safety Benchmarks: Report for the Adversarial Nibbler Challenge.
CoRR, 2023

Learning by Self-Explaining.
CoRR, 2023

Balancing Transparency and Risk: The Security and Privacy Risks of Open-Source Machine Learning Models.
CoRR, 2023

Self Expanding Neural Networks.
CoRR, 2023

Learning Differentiable Logic Programs for Abstract Visual Reasoning.
CoRR, 2023

V-LoL: A Diagnostic Dataset for Visual Logical Learning.
CoRR, 2023

Masked Autoencoders are Efficient Continual Federated Learners.
CoRR, 2023

Mitigating Inappropriateness in Image Generation: Can there be Value in Reflecting the World's Ugliness?
CoRR, 2023

Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman.
CoRR, 2023

Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers.
CoRR, 2023

Image Classifiers Leak Sensitive Attributes About Their Classes.
CoRR, 2023

Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness.
CoRR, 2023

SEGA: Instructing Diffusion using Semantic Dimensions.
CoRR, 2023

AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.
CoRR, 2023

ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models.
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, 2023

Balancing Transparency and Risk: An Overview of the Security and Privacy Risks of Open-Source Machine Learning Models.
Proceedings of the Bridging the Gap Between AI and Reality, 2023

Probabilistic circuits that know what they don't know.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Boosting Object Representation Learning via Motion and Object Continuity.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Do Not Marginalize Mechanisms, Rather Consolidate!
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

SEGA: Instructing Text-to-Image Models using Semantic Guidance.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Characteristic Circuits.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes.
Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, 2023

Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs.
Proceedings of the 25th IEEE International Conference on Intelligent Transportation Systems, 2023

ILLUME: Rationalizing Vision-Language Models through Human Interactions.
Proceedings of the International Conference on Machine Learning, 2023

One Explanation Does Not Fit XIL.
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Vision Relation Transformer for Unbiased Scene Graph Generation.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Revision Transformers: Instructing Language Models to Change Their Values.
Proceedings of the ECAI 2023 - 26th European Conference on Artificial Intelligence, September 30 - October 4, 2023, Kraków, Poland, 2023

Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Active Feature Acquisition via Human Interaction in Relational domains.
Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD), 2023

Speaking Multiple Languages Affects the Moral Bias of Language Models.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

Never Ending Reasoning and Learning: Opportunities and Challenges.
Proceedings of the AAAI Bridge Program on Continual Causality, 2023

2022
User-Level Label Leakage from Gradients in Federated Learning.
Proc. Priv. Enhancing Technol., 2022

Large pre-trained language models contain human-like biases of what is right and wrong to do.
Nat. Mach. Intell., 2022

Relational tree ensembles and feature rankings.
Knowl. Based Syst., 2022

Conditional sum-product networks: Modular probabilistic circuits via gate functions.
Int. J. Approx. Reason., 2022

Declarative Learning-Based Programming as an Interface to AI Systems.
Frontiers Artif. Intell., 2022

Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161).
Dagstuhl Reports, 2022

Pearl Causal Hierarchy on Image Data: Intricacies & Challenges.
CoRR, 2022

The Stable Artist: Steering Semantics in Diffusion Latent Space.
CoRR, 2022

Differentiable Meta logical Programming.
CoRR, 2022

Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models.
CoRR, 2022

Revision Transformers: Getting RiT of No-Nos.
CoRR, 2022

The Biased Artist: Exploiting Cultural Biases via Homoglyphs in Text-Guided Image Generation Models.
CoRR, 2022

CLIPping Privacy: Identity Inference Attacks on Multi-Modal Machine Learning Models.
CoRR, 2022

LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems.
CoRR, 2022

Transformer-Boosted Anomaly Detection with Fuzzy Hashes.
CoRR, 2022

ILLUME: Rationalizing Vision-Language Models by Interacting with their Jabber.
CoRR, 2022

HANF: Hyperparameter And Neural Architecture Search in Federated Learning.
CoRR, 2022

Can Foundation Models Talk Causality?
CoRR, 2022

Attributions Beyond Neural Networks: The Linear Program Case.
CoRR, 2022

Towards a Solution to Bongard Problems: A Causal Approach.
CoRR, 2022

Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation.
CoRR, 2022

Machines Explaining Linear Programs.
CoRR, 2022

Gradient-based Counterfactual Explanations using Tractable Probabilistic Models.
CoRR, 2022

Finding Structure and Causality in Linear Programs.
CoRR, 2022

Do Multilingual Language Models Capture Differing Moral Norms?
CoRR, 2022

A Typology to Explore and Guide Explanatory Interactive Machine Learning.
CoRR, 2022

Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement.
CoRR, 2022

Predictive Whittle networks for time series.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Explaining Deep Tractable Probabilistic Models: The sum-product network case.
Proceedings of the International Conference on Probabilistic Graphical Models, 2022

Adaptable Adapters.
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

Neural-Probabilistic Answer Set Programming.
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022

Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming.
Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning, 2022

Neuro-Symbolic Verification of Deep Neural Networks.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

To Trust or Not To Trust Prediction Scores for Membership Inference Attacks.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Unsupervised Multi-sensor Anomaly Localization with Explainable AI.
Proceedings of the Artificial Intelligence Applications and Innovations, 2022

Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks.
Proceedings of the International Conference on Machine Learning, 2022

CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Interactively Providing Explanations for Transformer Language Models.
Proceedings of the HHAI 2022: Augmenting Human Intellect, 2022

Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Relational Active Feature Elicitation for DDDAS.
Proceedings of the Dynamic Data Driven Applications Systems - 4th International Conference, 2022

Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs.
Proceedings of the Artificial Intelligence in Medicine, 2022

2021
Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation.
Frontiers Artif. Intell., 2021

ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition.
Frontiers Robotics AI, 2021

Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data.
Frontiers Robotics AI, 2021

Structure learning for relational logistic regression: an ensemble approach.
Data Min. Knowl. Discov., 2021

Do Not Trust Prediction Scores for Membership Inference Attacks.
CoRR, 2021

The Causal Loss: Driving Correlation to Imply Causation.
CoRR, 2021

On the Tractability of Neural Causal Inference.
CoRR, 2021

Explaining Deep Tractable Probabilistic Models: The sum-product network case.
CoRR, 2021

Neuro-Symbolic Forward Reasoning.
CoRR, 2021

Inferring Offensiveness In Images From Natural Language Supervision.
CoRR, 2021

SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming.
CoRR, 2021

Structural Causal Interpretation Theorem.
CoRR, 2021

Interactively Generating Explanations for Transformer Language Models.
CoRR, 2021

Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits.
CoRR, 2021

DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection.
CoRR, 2021

Relating Graph Neural Networks to Structural Causal Models.
CoRR, 2021

Generative Adversarial Neural Cellular Automata.
CoRR, 2021

RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting.
CoRR, 2021

Intriguing Parameters of Structural Causal Models.
CoRR, 2021

User Label Leakage from Gradients in Federated Learning.
CoRR, 2021

Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation.
CoRR, 2021

Language Models have a Moral Dimension.
CoRR, 2021

Recurrent Rational Networks.
CoRR, 2021

Leveraging probabilistic circuits for nonparametric multi-output regression.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Elevating Perceptual Sample Quality in PCs through Differentiable Sampling.
Proceedings of the NeurIPS 2021 Workshop on Pre-Registration in Machine Learning, 2021

Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.
Proceedings of the Inductive Logic Programming - 30th International Conference, 2021

Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention.
Proceedings of the Fifteenth International AAAI Conference on Web and Social Media, 2021

Whittle Networks: A Deep Likelihood Model for Time Series.
Proceedings of the 38th International Conference on Machine Learning, 2021

Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Improving AlphaZero Using Monte-Carlo Graph Search.
Proceedings of the Thirty-First International Conference on Automated Planning and Scheduling, 2021

Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
DeepDB: Learn from Data, not from Queries!
Proc. VLDB Endow., 2020

Making deep neural networks right for the right scientific reasons by interacting with their explanations.
Nat. Mach. Intell., 2020

Matrix- and Tensor Factorization for Game Content Recommendation.
Künstliche Intell., 2020

Rethinking Computer Science Through AI.
Künstliche Intell., 2020

Learning attribute grammars for movement primitive sequencing.
Int. J. Robotics Res., 2020

The Moral Choice Machine.
Frontiers Artif. Intell., 2020

Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data.
Frontiers Artif. Intell., 2020

Towards Understanding and Arguing with Classifiers: Recent Progress.
Datenbank-Spektrum, 2020

SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091).
Dagstuhl Reports, 2020

Monte-Carlo Graph Search for AlphaZero.
CoRR, 2020

TUDataset: A collection of benchmark datasets for learning with graphs.
CoRR, 2020

Fitted Q-Learning for Relational Domains.
CoRR, 2020

Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations.
CoRR, 2020

Modelling Multivariate Ranking Functions with Min-Sum Networks.
Proceedings of the Scalable Uncertainty Management - 14th International Conference, 2020

Residual Sum-Product Networks.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

Discriminative Non-Parametric Learning of Arithmetic Circuits.
Proceedings of the International Conference on Probabilistic Graphical Models, 2020

On Hybrid and Systems AI.
Proceedings of the Conference "Lernen, 2020

Independence and D-separation in Abstract Argumentation.
Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning, 2020

Estimating the Importance of Relational Features by Using Gradient Boosting.
Proceedings of the Foundations of Intelligent Systems - 25th International Symposium, 2020

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits.
Proceedings of the 37th International Conference on Machine Learning, 2020

Alfie: An Interactive Robot with Moral Compass.
Proceedings of the ICMI '20: International Conference on Multimodal Interaction, 2020

Structured Object-Aware Physics Prediction for Video Modeling and Planning.
Proceedings of the 8th International Conference on Learning Representations, 2020

Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks.
Proceedings of the 8th International Conference on Learning Representations, 2020

Künstliche Intelligenz - Die dritte Welle.
Proceedings of the 50. Jahrestagung der Gesellschaft für Informatik, INFORMATIK 2020 - Back to the Future, Karlsruhe, Germany, 28. September, 2020

CryptoSPN: Privacy-Preserving Sum-Product Network Inference.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

CryptoSPN: Expanding PPML beyond Neural Networks.
Proceedings of the PPMLP'20: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, 2020

2019
Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range.
Remote. Sens., 2019

Editorial: Statistical Relational Artificial Intelligence.
Frontiers Robotics AI, 2019

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning.
Frontiers Big Data, 2019

A unifying view of explicit and implicit feature maps of graph kernels.
Data Min. Knowl. Discov., 2019

Logic and Learning (Dagstuhl Seminar 19361).
Dagstuhl Reports, 2019

BERT has a Moral Compass: Improvements of ethical and moral values of machines.
CoRR, 2019

Random Sum-Product Forests with Residual Links.
CoRR, 2019

Neural-Symbolic Argumentation Mining: an Argument in Favour of Deep Learning and Reasoning.
CoRR, 2019

Was ist eine Professur fuer Kuenstliche Intelligenz?
CoRR, 2019

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks.
CoRR, 2019

Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach.
Auton. Robots, 2019

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Gaussian Lifted Marginal Filtering.
Proceedings of the KI 2019: Advances in Artificial Intelligence, 2019

Neural Networks for Relational Data.
Proceedings of the Inductive Logic Programming - 29th International Conference, 2019

Faster Attend-Infer-Repeat with Tractable Probabilistic Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs.
Proceedings of the International Conference on Field-Programmable Technology, 2019

Explanatory Interactive Machine Learning.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices.
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 2019

Automatic Bayesian Density Analysis.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
From Big Data to Big Artificial Intelligence? - Algorithmic Challenges and Opportunities of Big Data.
Künstliche Intell., 2018

Making AI Smarter.
Künstliche Intell., 2018

Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines.
Frontiers Big Data, 2018

Model-based Approximate Query Processing.
CoRR, 2018

Probabilistic Deep Learning using Random Sum-Product Networks.
CoRR, 2018

"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users.
CoRR, 2018

Neural Conditional Gradients.
CoRR, 2018

Lifted Filtering via Exchangeable Decomposition.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Systems AI: A Declarative Learning Based Programming Perspective.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Efficient Symbolic Integration for Probabilistic Inference.
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives.
Proceedings of the 2018 IEEE International Conference on Robotics and Automation, 2018

Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators.
Proceedings of the 36th IEEE International Conference on Computer Design, 2018

Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

Core Dependency Networks.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Symbolic Dynamic Programming.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Statistical Relational Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Gaussian Process.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction.
Trans. GIS, 2017

Semantic Interpretation of Multi-Modal Human-Behaviour Data - Making Sense of Events, Activities, Processes.
Künstliche Intell., 2017

Sum-Product Networks for Hybrid Domains.
CoRR, 2017

Coresets for Dependency Networks.
CoRR, 2017

A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels.
CoRR, 2017

Global Weisfeiler-Lehman Graph Kernels.
CoRR, 2017

Relational linear programming.
Artif. Intell., 2017

Graph Enhanced Memory Networks for Sentiment Analysis.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach.
Proceedings of the Inductive Logic Programming - 27th International Conference, 2017

Stochastic Online Anomaly Analysis for Streaming Time Series.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs.
Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

Interactive Data Analytics for the Humanities.
Proceedings of the Computational Linguistics and Intelligent Text Processing, 2017

Modeling heart procedures from EHRs: An application of exponential families.
Proceedings of the 2017 IEEE International Conference on Bioinformatics and Biomedicine, 2017

Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

Lifted Inference for Convex Quadratic Programs.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

The Symbolic Interior Point Method.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Statistical Relational Artificial Intelligence: Logic, Probability, and Computation
Synthesis Lectures on Artificial Intelligence and Machine Learning, Morgan & Claypool Publishers, ISBN: 978-3-031-01574-8, 2016

Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants.
Proceedings of the Computational Sustainability, 2016

Propagation kernels: efficient graph kernels from propagated information.
Mach. Learn., 2016

Collective Attention on the Web.
Found. Trends Web Sci., 2016

How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?
Entropy, 2016

Lifted Convex Quadratic Programming.
CoRR, 2016

Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage.
CoRR, 2016

Scaling Lifted Probabilistic Inference and Learning Via Graph Databases.
Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Learning Through Advice-Seeking via Transfer.
Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

Learning Using Unselected Features (LUFe).
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Faster Kernels for Graphs with Continuous Attributes via Hashing.
Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction.
Proceedings of the Solving Large Scale Learning Tasks. Challenges and Algorithms, 2016

Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

RELOOP: A Python-Embedded Declarative Language for Relational Optimization.
Proceedings of the Declarative Learning Based Programming, 2016

2015
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases.
Mach. Learn., 2015

Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data.
Mach. Learn., 2015

Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization.
Künstliche Intell., 2015

pyGPs: a Python library for Gaussian process regression and classification.
J. Mach. Learn. Res., 2015

Maximum Entropy Models of Shortest Path and Outbreak Distributions in Networks.
CoRR, 2015

Automated interpretation of 3D laserscanned point clouds for plant organ segmentation.
BMC Bioinform., 2015

Reports of the AAAI 2014 Conference Workshops.
AI Mag., 2015

LTE Connectivity and Vehicular Traffic Prediction Based on Machine Learning Approaches.
Proceedings of the IEEE 82nd Vehicular Technology Conference, 2015

Equitable Partitions of Concave Free Energies.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Parameterizing the Distance Distribution of Undirected Networks.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

How Viral Are Viral Videos?
Proceedings of the Ninth International Conference on Web and Social Media, 2015

Transfer Learning via Relational Type Matching.
Proceedings of the 2015 IEEE International Conference on Data Mining, 2015

Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study.
Proceedings of the Artificial Intelligence in Medicine, 2015

Predicting Purchase Decisions in Mobile Free-to-Play Games.
Proceedings of the Eleventh AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2015

2014
Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine
Springer Briefs in Computer Science, Springer, ISBN: 978-3-319-13644-8, 2014

Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain.
Int. J. Mach. Learn. Cybern., 2014

Künstliche Intelligenz für Computerspiele - Historische Entwicklung und aktuelle Trends.
Inform. Spektrum, 2014

Propagation Kernels.
CoRR, 2014

Relational Linear Programs.
CoRR, 2014

Strong Regularities in Growth and Decline of Popularity of Social Media Services.
CoRR, 2014

High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline.
CoRR, 2014

Collective attention to social media evolves according to diffusion models.
Proceedings of the 23rd International World Wide Web Conference, 2014

Lifted Message Passing as Reparametrization of Graphical Models.
Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Population Size Extrapolation in Relational Probabilistic Modelling.
Proceedings of the Scalable Uncertainty Management - 8th International Conference, 2014

Mind the Nuisance: Gaussian Process Classification using Privileged Noise.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Relational Logistic Regression.
Proceedings of the Principles of Knowledge Representation and Reasoning: Proceedings of the Fourteenth International Conference, 2014

Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge.
Proceedings of the Inductive Logic Programming - 24th International Conference, 2014

Erosion Band Features for Cell Phone Image Based Plant Disease Classification.
Proceedings of the 22nd International Conference on Pattern Recognition, 2014

Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Explicit Versus Implicit Graph Feature Maps: A Computational Phase Transition for Walk Kernels.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Dimension Reduction via Colour Refinement.
Proceedings of the Algorithms - ESA 2014, 2014

Predicting player churn in the wild.
Proceedings of the 2014 IEEE Conference on Computational Intelligence and Games, 2014

Efficient Lifting of MAP LP Relaxations Using k-Locality.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Power Iterated Color Refinement.
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014

Relational Logistic Regression: The Directed Analog of Markov Logic Networks.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Preface.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

Lifting Relational MAP-LPs using Cluster Signatures.
Proceedings of the Statistical Relational Artificial Intelligence, 2014

2013
Guest editor's introduction: special issue of the ECML PKDD 2013 journal track.
Mach. Learn., 2013

Exploiting symmetries for scaling loopy belief propagation and relational training.
Mach. Learn., 2013

Data Mining and Pattern Recognition in Agriculture.
Künstliche Intell., 2013

Can Computers Learn from the Aesthetic Wisdom of the Crowd?
Künstliche Intell., 2013

GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science
CoRR, 2013

Efficient Information Theoretic Clustering on Discrete Lattices.
CoRR, 2013

The AAAI-13 Conference Workshops.
AI Mag., 2013

Accelerating Imitation Learning in Relational Domains via Transfer by Initialization.
Proceedings of the Inductive Logic Programming - 23rd International Conference, 2013

Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes.
Proceedings of the Seventh International Conference on Weblogs and Social Media, 2013

Early Prediction of Coronary Artery Calcification Levels Using Machine Learning.
Proceedings of the Twenty-Fifth Innovative Applications of Artificial Intelligence Conference, 2013

Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels.
Proceedings of the Asian Conference on Machine Learning, 2013

Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

Lifted Inference via k-Locality.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

Reduce and Re-Lift: Bootstrapped Lifted Likelihood Maximization for MAP.
Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, 2013

Preface.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

MapReduce Lifting for Belief Propagation.
Proceedings of the Statistical Relational Artificial Intelligence, 2013

2012
Agriculture's Technological Makeover.
IEEE Pervasive Comput., 2012

Gradient-based boosting for statistical relational learning: The relational dependency network case.
Mach. Learn., 2012

Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Lifted Linear Programming.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Exploration in relational domains for model-based reinforcement learning.
J. Mach. Learn. Res., 2012

Descriptive matrix factorization for sustainability Adopting the principle of opposites.
Data Min. Knowl. Discov., 2012

A Revised Publication Model for ECML PKDD
CoRR, 2012

Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants.
Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012

Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Accelarating Imitation Learning in Relational Domains via Transfer by Initialization.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

From Lifted Inference to Lifted Models.
Proceedings of the 2nd International Workshop on Statistical Relational AI (StaRAI-12), 2012

Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study.
Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Simplex Distributions for Embedding Data Matrices over Time.
Proceedings of the Twelfth SIAM International Conference on Data Mining, 2012

Efficient Graph Kernels by Randomization.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Matrix Factorization as Search.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Lifted Online Training of Relational Models with Stochastic Gradient Methods.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Symbolic Dynamic Programming for Continuous State and Observation POMDPs.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Pairwise Markov Logic.
Proceedings of the Inductive Logic Programming - 22nd International Conference, 2012

A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain.
Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

Efficient Learning for Hashing Proportional Data.
Proceedings of the 12th IEEE International Conference on Data Mining, 2012

Lifted Probabilistic Inference.
Proceedings of the ECAI 2012, 2012

How players lose interest in playing a game: An empirical study based on distributions of total playing times.
Proceedings of the 2012 IEEE Conference on Computational Intelligence and Games, 2012

Pre-Symptomatic Prediction of Plant Drought Stress Using Dirichlet-Aggregation Regression on Hyperspectral Images.
Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012

2011
Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning.
Mach. Learn., 2011

Convex non-negative matrix factorization for massive datasets.
Knowl. Inf. Syst., 2011

Perception beyond the Here and Now.
Computer, 2011

Decision-theoretic planning with generalized first-order decision diagrams.
Artif. Intell., 2011

Larger Residuals, Less Work: Active Document Scheduling for Latent Dirichlet Allocation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Biological Sequence Analysis meets Mobility Mining.
Proceedings of the Report of the symposium "Lernen, 2011

O Scientist, Where Art Thou? Affiliation Propagation for Geo-Referencing Scientific Publications.
Proceedings of the Report of the symposium "Lernen, 2011

On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving.
Proceedings of the Report of the symposium "Lernen, 2011

Efficient Sequential Clamping for Lifted Message Passing.
Proceedings of the KI 2011: Advances in Artificial Intelligence, 2011

Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach.
Proceedings of the IJCAI 2011, 2011

Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter.
Proceedings of the IJCAI 2011, 2011

Multi-task Learning with Task Relations.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Learning Markov Logic Networks via Functional Gradient Boosting.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Where traffic meets DNA: mobility mining using biological sequence analysis revisited.
Proceedings of the 19th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, 2011

Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learning.
Proceedings of the Recent Advances in Reinforcement Learning - 9th European Workshop, 2011

More influence means less work: fast latent dirichlet allocation by influence scheduling.
Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011

Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation.
Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011

2010
Symbolic Dynamic Programming.
Proceedings of the Encyclopedia of Machine Learning, 2010

Statistical Relational Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Gaussian Process.
Proceedings of the Encyclopedia of Machine Learning, 2010

Hierarchical Convex NMF for Clustering Massive Data.
Proceedings of the 2nd Asian Conference on Machine Learning, 2010

Reports of the AAAI 2010 Conference Workshops.
AI Mag., 2010

Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Topic Models Conditioned on Relations.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Exploration in Relational Worlds.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Beyond 2D-grids: a dependence maximization view on image browsing.
Proceedings of the 11th ACM SIGMM International Conference on Multimedia Information Retrieval, 2010

Convex NMF on Non-Convex Massiv Data.
Proceedings of the LWA 2010, 2010

Kernelized Map Matching for noisy trajectories.
Proceedings of the LWA 2010, 2010

Lifted Conditioning for Pairwise Marginals and Beyond.
Proceedings of the LWA 2010, 2010

Learning to hash logistic regression for fast 3D scan point classification.
Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010

Multi-Agent Inverse Reinforcement Learning.
Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010

Kernelized map matching.
Proceedings of the 18th ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, 2010

Yes we can: simplex volume maximization for descriptive web-scale matrix factorization.
Proceedings of the 19th ACM Conference on Information and Knowledge Management, 2010

Self-Taught Decision Theoretic Planning with First Order Decision Diagrams.
Proceedings of the 20th International Conference on Automated Planning and Scheduling, 2010

Symbolic Dynamic Programming for First-order POMDPs.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

Informed Lifting for Message-Passing.
Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010

Lifted Message Passing for Satisfiability.
Proceedings of the Statistical Relational Artificial Intelligence, 2010

Probabilistic Inductive Querying Using ProbLog.
Proceedings of the Inductive Databases and Constraint-Based Data Mining., 2010

2009
A Bayesian regression approach to terrain mapping and an application to legged robot locomotion.
J. Field Robotics, 2009

Counting Belief Propagation.
Proceedings of the UAI 2009, 2009

Learning Preferences with Hidden Common Cause Relations.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries.
Proceedings of the Inductive Logic Programming, 19th International Conference, 2009

Multi-Relational Learning with Gaussian Processes.
Proceedings of the IJCAI 2009, 2009

Generalized First Order Decision Diagrams for First Order Markov Decision Processes.
Proceedings of the IJCAI 2009, 2009

Convex Non-negative Matrix Factorization in the Wild.
Proceedings of the ICDM 2009, 2009

Kernel Conditional Quantile Estimation via Reduction Revisited.
Proceedings of the ICDM 2009, 2009

Stacked Gaussian Process Learning.
Proceedings of the ICDM 2009, 2009

2008
Compressing probabilistic Prolog programs.
Mach. Learn., 2008

Preface.
Ann. Math. Artif. Intell., 2008

Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Parameter Learning in Probabilistic Databases: A Least Squares Approach.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Towards Engaging Games.
Proceedings of the LWA 2008, 2008

Social Network Mining with Nonparametric Relational Models.
Proceedings of the Advances in Social Network Mining and Analysis, 2008

Learning predictive terrain models for legged robot locomotion.
Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008

Probabilistic Inductive Logic Programming.
Proceedings of the Probabilistic Inductive Logic Programming - Theory and Applications, 2008

Logical Hierarchical Hidden Markov Models for Modeling User Activities.
Proceedings of the Inductive Logic Programming, 18th International Conference, 2008

Relational Sequence Learning.
Proceedings of the Probabilistic Inductive Logic Programming - Theory and Applications, 2008

Basic Principles of Learning Bayesian Logic Programs.
Proceedings of the Probabilistic Inductive Logic Programming - Theory and Applications, 2008

SRL without Tears: An ILP Perspective.
Proceedings of the Inductive Logic Programming, 18th International Conference, 2008

Non-parametric policy gradients: a unified treatment of propositional and relational domains.
Proceedings of the Machine Learning, 2008

Boosting Relational Sequence Alignments.
Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Lifted Probabilistic Inference with Counting Formulas.
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008

2007
Integrating Naïve Bayes and FOIL.
J. Mach. Learn. Res., 2007

Learning to transfer optimal navigation policies.
Adv. Robotics, 2007

Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders.
Proceedings of the Robotics: Science and Systems III, 2007

Most likely heteroscedastic Gaussian process regression.
Proceedings of the Machine Learning, 2007

07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis.
Proceedings of the Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04., 2007

2006
Learning Relational Navigation Policies.
Künstliche Intell., 2006

Logical Hidden Markov Models.
J. Artif. Intell. Res., 2006

An inductive logic programming approach to statistical relational learning.
AI Commun., 2006

Revising Probabilistic Prolog Programs.
Proceedings of the Inductive Logic Programming, 16th International Conference, 2006

Relational Sequence Alignments and Logos.
Proceedings of the Inductive Logic Programming, 16th International Conference, 2006

Robust 3D Scan Point Classification using Associative Markov Networks.
Proceedings of the 2006 IEEE International Conference on Robotics and Automation, 2006

TildeCRF: Conditional Random Fields for Logical Sequences.
Proceedings of the Machine Learning: ECML 2006, 2006

Fisher Kernels for Relational Data.
Proceedings of the Machine Learning: ECML 2006, 2006

2005
An Inductive Logic Programming Approach to Statistical Relational Learning
PhD thesis, 2005

"Say EM" for Selecting Probabilistic Models for Logical Sequences.
Proceedings of the UAI '05, 2005

Towards Learning Stochastic Logic Programs from Proof-Banks.
Proceedings of the Proceedings, 2005

nFOIL: Integrating Naïve Bayes and FOIL.
Proceedings of the Proceedings, 2005

2004
Balios - The Engine for Bayesian Logic Programs.
Proceedings of the Knowledge Discovery in Databases: PKDD 2004, 2004

Logical Markov Decision Programs and the Convergence of Logical TD(lambda).
Proceedings of the Inductive Logic Programming, 14th International Conference, 2004

Bellman goes relational.
Proceedings of the Machine Learning, 2004

Fisher Kernels for Logical Sequences.
Proceedings of the Machine Learning: ECML 2004, 2004

Probabilistic Inductive Logic Programming.
Proceedings of the Algorithmic Learning Theory, 15th International Conference, 2004

2003
Probabilistic logic learning.
SIGKDD Explor., 2003

Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models.
Proceedings of the 8th Pacific Symposium on Biocomputing, 2003

Scaled CGEM: A Fast Accelerated EM.
Proceedings of the Machine Learning: ECML 2003, 2003

2002
Analysis of respiratory pressure-volume curves in intensive care medicine using inductive machine learning.
Artif. Intell. Medicine, 2002

Logical Hidden Markov Models (Extendes abstract).
Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002

Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm.
Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002

2001
Towards Combining Inductive Logic Programming with Bayesian Networks.
Proceedings of the Inductive Logic Programming, 11th International Conference, 2001

Adaptive Bayesian Logic Programs.
Proceedings of the Inductive Logic Programming, 11th International Conference, 2001

2000
Bayesian Logic Programs.
Proceedings of the Inductive Logic Programming, 10th International Conference, 2000


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