Kristian Kersting
170 publications
31 venues
H Index 39
Affiliation
TU Darmstadt, Computer Science Department, GermanyTU 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
Links
DBLPGoogle Scholar
Twitter- https://ml-research.github.io/people/kkersting/
- http://www.ml.informatik.tu-darmstadt.de/#bio
- http://www-ai.cs.uni-dortmund.de/PERSONAL/kersting.html
- http://people.csail.mit.edu/kersting/
- https://en.wikipedia.org/wiki/Kristian_Kersting
- https://orcid.org/0000-0002-2873-9152
- https://www.wikidata.org/entity/Q72740096
- https://id.loc.gov/authorities/names/no2007046979
- https://d-nb.info/gnd/173912001
| Name | Venue | Year | citations |
|---|---|---|---|
Structural causal models reveal confounder bias in linear program modelling.
|
MLJ | 2024 | 0 |
| Probabilistic circuits that know what they don't know.
|
UAI | 2023 | 0 |
| ILLUME: Rationalizing Vision-Language Models through Human Interactions.
|
ICML | 2023 | 0 |
| Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference.
|
UAI | 2023 | 0 |
| Boosting Object Representation Learning via Motion and Object Continuity.
|
ECML/PKDD | 2023 | 0 |
| Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models.
|
CVPR | 2023 | 0 |
| Revision Transformers: Instructing Language Models to Change Their Values.
|
ECAI | 2023 | 0 |
| Do Not Marginalize Mechanisms, Rather Consolidate!
|
NIPS/NeurIPS | 2023 | 0 |
| MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation.
|
NIPS/NeurIPS | 2023 | 0 |
| Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction.
|
NIPS/NeurIPS | 2023 | 0 |
| ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation.
|
NIPS/NeurIPS | 2023 | 0 |
| SEGA: Instructing Text-to-Image Models using Semantic Guidance.
|
NIPS/NeurIPS | 2023 | 0 |
| Characteristic Circuits.
|
NIPS/NeurIPS | 2023 | 0 |
| Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis.
|
ICCV | 2023 | 0 |
| Vision Relation Transformer for Unbiased Scene Graph Generation.
|
ICCV | 2023 | 0 |
| αILP: thinking visual scenes as differentiable logic programs.
|
MLJ | 2023 | 0 |
| Scalable Neural-Probabilistic Answer Set Programming.
|
JAIR | 2023 | 0 |
| Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis.
|
JAIR | 2023 | 0 |
| Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming.
|
KR | 2022 | 0 |
| Neural-Probabilistic Answer Set Programming.
|
KR | 2022 | 0 |
| Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks.
|
ICML | 2022 | 5 |
| Neuro-Symbolic Verification of Deep Neural Networks.
|
IJCAI | 2022 | 3 |
| Predictive Whittle networks for time series.
|
UAI | 2022 | 1 |
| To Trust or Not To Trust Prediction Scores for Membership Inference Attacks.
|
IJCAI | 2022 | 0 |
| Explaining Deep Tractable Probabilistic Models: The sum-product network case.
|
PGM | 2022 | 0 |
| Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations.
|
CVPR | 2022 | 0 |
| CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability.
|
ICLR | 2022 | 0 |
| Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.
|
ILP | 2021 | 0 |
| Whittle Networks: A Deep Likelihood Model for Time Series.
|
ICML | 2021 | 8 |
| Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models.
|
NIPS/NeurIPS | 2021 | 14 |
| Leveraging probabilistic circuits for nonparametric multi-output regression.
|
UAI | 2021 | 6 |
| Improving AlphaZero Using Monte-Carlo Graph Search.
|
ICAPS | 2021 | 1 |
| Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions.
|
AAAI | 2021 | 18 |
| Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting With Their Explanations.
|
CVPR | 2021 | 0 |
| Structure learning for relational logistic regression: an ensemble approach.
|
DMKD | 2021 | 0 |
| Discriminative Non-Parametric Learning of Arithmetic Circuits.
|
PGM | 2020 | 1 |
| Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits.
|
ICML | 2020 | 56 |
| Learning attribute grammars for movement primitive sequencing.
|
IJRR | 2020 | 7 |
| Independence and D-separation in Abstract Argumentation.
|
KR | 2020 | 0 |
| CryptoSPN: Privacy-Preserving Sum-Product Network Inference.
|
ECAI | 2020 | 8 |
| Residual Sum-Product Networks.
|
PGM | 2020 | 4 |
| Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures.
|
PGM | 2020 | 0 |
| Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks.
|
ICLR | 2020 | 0 |
| Structured Object-Aware Physics Prediction for Video Modeling and Planning.
|
ICLR | 2020 | 0 |
| Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs.
|
AAAI | 2019 | 13 |
| Neural Networks for Relational Data.
|
ILP | 2019 | 6 |
| Faster Attend-Infer-Repeat with Tractable Probabilistic Models.
|
ICML | 2019 | 40 |
| Explanatory Interactive Machine Learning.
|
AIES | 2019 | 106 |
| Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning.
|
UAI | 2019 | 69 |
| Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices.
|
AIES | 2019 | 36 |
| Automatic Bayesian Density Analysis.
|
AAAI | 2019 | 0 |
| A unifying view of explicit and implicit feature maps of graph kernels.
|
DMKD | 2019 | 0 |
| Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach.
|
Autonomous Robots | 2019 | 0 |
| Systems AI: A Declarative Learning Based Programming Perspective.
|
IJCAI | 2018 | 11 |
| Lifted Filtering via Exchangeable Decomposition.
|
IJCAI | 2018 | 8 |
| Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives.
|
ICRA | 2018 | 4 |
| Efficient Symbolic Integration for Probabilistic Inference.
|
IJCAI | 2018 | 24 |
| Structure Learning for Relational Logistic Regression: An Ensemble Approach.
|
KR | 2018 | 9 |
| Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains.
|
AAAI | 2018 | 79 |
| Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks.
|
AAAI | 2018 | 23 |
| Core Dependency Networks.
|
AAAI | 2018 | 15 |
| Lifted Inference for Convex Quadratic Programs.
|
AAAI | 2017 | 4 |
| Graph Enhanced Memory Networks for Sentiment Analysis.
|
ECML/PKDD | 2017 | 1 |
| Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs.
|
ICDM | 2017 | 64 |
| Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach.
|
ILP | 2017 | 17 |
| Relational linear programming.
|
Artificial Intelligence | 2017 | 17 |
| Stochastic Online Anomaly Analysis for Streaming Time Series.
|
IJCAI | 2017 | 18 |
| Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions.
|
AAAI | 2017 | 30 |
| The Symbolic Interior Point Method.
|
AAAI | 2017 | 0 |
| Faster Kernels for Graphs with Continuous Attributes via Hashing.
|
ICDM | 2016 | 2 |
| Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach.
|
AAAI | 2016 | 15 |
| Scaling Lifted Probabilistic Inference and Learning Via Graph Databases.
|
SDM | 2016 | 24 |
| Learning Through Advice-Seeking via Transfer.
|
ILP | 2016 | 1 |
| Learning Using Unselected Features (LUFe).
|
IJCAI | 2016 | 6 |
| Propagation kernels: efficient graph kernels from propagated information.
|
MLJ | 2016 | 171 |
| Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases.
|
MLJ | 2015 | 34 |
| Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data.
|
MLJ | 2015 | 0 |
| Predicting Purchase Decisions in Mobile Free-to-Play Games.
|
AIIDE | 2015 | 81 |
| Equitable Partitions of Concave Free Energies.
|
UAI | 2015 | 7 |
| pyGPs: a Python library for Gaussian process regression and classification.
|
JMLR | 2015 | 21 |
| Parameterizing the Distance Distribution of Undirected Networks.
|
UAI | 2015 | 5 |
| Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation.
|
IJCAI | 2015 | 7 |
| Transfer Learning via Relational Type Matching.
|
ICDM | 2015 | 32 |
| Efficient Lifting of MAP LP Relaxations Using k-Locality.
|
AISTATS | 2014 | 21 |
| Mind the Nuisance: Gaussian Process Classification using Privileged Noise.
|
NIPS/NeurIPS | 2014 | 35 |
| Lifting Relational MAP-LPs Using Cluster Signatures.
|
AAAI | 2014 | 15 |
| Relational Logistic Regression.
|
KR | 2014 | 52 |
| Lifted Message Passing as Reparametrization of Graphical Models.
|
UAI | 2014 | 14 |
| Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach.
|
ICDM | 2014 | 23 |
| Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge.
|
ILP | 2014 | 9 |
| Power Iterated Color Refinement.
|
AAAI | 2014 | 38 |
| Explicit Versus Implicit Graph Feature Maps: A Computational Phase Transition for Walk Kernels.
|
ICDM | 2014 | 28 |
| Exploiting symmetries for scaling loopy belief propagation and relational training.
|
MLJ | 2013 | 78 |
| Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels.
|
ACML | 2013 | 7 |
| Reduce and Re-Lift: Bootstrapped Lifted Likelihood Maximization for MAP.
|
AAAI | 2013 | 12 |
| Accelerating Imitation Learning in Relational Domains via Transfer by Initialization.
|
ILP | 2013 | 6 |
| Guest editor's introduction: special issue of the ECML PKDD 2013 journal track.
|
DMKD | 2013 | 0 |
| Guest editor's introduction: special issue of the ECML PKDD 2013 journal track.
|
MLJ | 2013 | 0 |
| Symbolic Dynamic Programming for Continuous State and Observation POMDPs.
|
NIPS/NeurIPS | 2012 | 12 |
| Lifted Probabilistic Inference.
|
ECAI | 2012 | 115 |
| Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants.
|
UAI | 2012 | 7 |
| Exploration in relational domains for model-based reinforcement learning.
|
JMLR | 2012 | 0 |
| Efficient Graph Kernels by Randomization.
|
ECML/PKDD | 2012 | 64 |
| Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data.
|
AISTATS | 2012 | 9 |
| Lifted Online Training of Relational Models with Stochastic Gradient Methods.
|
ECML/PKDD | 2012 | 24 |
| Pairwise Markov Logic.
|
ILP | 2012 | 5 |
| Pre-Symptomatic Prediction of Plant Drought Stress Using Dirichlet-Aggregation Regression on Hyperspectral Images.
|
AAAI | 2012 | 21 |
| Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study.
|
SDM | 2012 | 23 |
| Simplex Distributions for Embedding Data Matrices over Time.
|
SDM | 2012 | 16 |
| Descriptive matrix factorization for sustainability Adopting the principle of opposites.
|
DMKD | 2012 | 68 |
| Lifted Linear Programming.
|
AISTATS | 2012 | 47 |
| Matrix Factorization as Search.
|
ECML/PKDD | 2012 | 3 |
| Efficient Learning for Hashing Proportional Data.
|
ICDM | 2012 | 4 |
| Gradient-based boosting for statistical relational learning: The relational dependency network case.
|
MLJ | 2012 | 0 |
| Larger Residuals, Less Work: Active Document Scheduling for Latent Dirichlet Allocation.
|
ECML/PKDD | 2011 | 20 |
| Decision-theoretic planning with generalized first-order decision diagrams.
|
Artificial Intelligence | 2011 | 6 |
| Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter.
|
IJCAI | 2011 | 34 |
| More influence means less work: fast latent dirichlet allocation by influence scheduling.
|
CIKM | 2011 | 8 |
| Multi-task Learning with Task Relations.
|
ICDM | 2011 | 14 |
| Learning Markov Logic Networks via Functional Gradient Boosting.
|
ICDM | 2011 | 100 |
| Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach.
|
IJCAI | 2011 | 62 |
| Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation.
|
AAAI | 2011 | 15 |
| Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning.
|
MLJ | 2011 | 0 |
| Self-Taught Decision Theoretic Planning with First Order Decision Diagrams.
|
ICAPS | 2010 | 21 |
| Learning to hash logistic regression for fast 3D scan point classification.
|
IROS | 2010 | 21 |
| Exploration in Relational Worlds.
|
ECML/PKDD | 2010 | 17 |
| Hierarchical Convex NMF for Clustering Massive Data.
|
ACML | 2010 | 31 |
| Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models.
|
ECML/PKDD | 2010 | 18 |
| Informed Lifting for Message-Passing.
|
AAAI | 2010 | 25 |
| Symbolic Dynamic Programming for First-order POMDPs.
|
AAAI | 2010 | 79 |
| Topic Models Conditioned on Relations.
|
ECML/PKDD | 2010 | 14 |
| Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes.
|
ECML/PKDD | 2010 | 25 |
| Yes we can: simplex volume maximization for descriptive web-scale matrix factorization.
|
CIKM | 2010 | 62 |
| Multi-Relational Learning with Gaussian Processes.
|
IJCAI | 2009 | 58 |
| Counting Belief Propagation.
|
UAI | 2009 | 170 |
| ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries.
|
ILP | 2009 | 3 |
| Generalized First Order Decision Diagrams for First Order Markov Decision Processes.
|
IJCAI | 2009 | 22 |
| Learning Preferences with Hidden Common Cause Relations.
|
ECML/PKDD | 2009 | 17 |
| Convex Non-negative Matrix Factorization in the Wild.
|
ICDM | 2009 | 66 |
| Stacked Gaussian Process Learning.
|
ICDM | 2009 | 42 |
| Kernel Conditional Quantile Estimation via Reduction Revisited.
|
ICDM | 2009 | 34 |
| Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness.
|
ECML/PKDD | 2008 | 99 |
| Parameter Learning in Probabilistic Databases: A Least Squares Approach.
|
ECML/PKDD | 2008 | 66 |
| Lifted Probabilistic Inference with Counting Formulas.
|
AAAI | 2008 | 219 |
| SRL without Tears: An ILP Perspective.
|
ILP | 2008 | 1 |
| Logical Hierarchical Hidden Markov Models for Modeling User Activities.
|
ILP | 2008 | 47 |
| Compressing probabilistic Prolog programs.
|
MLJ | 2008 | 41 |
| Learning predictive terrain models for legged robot locomotion.
|
IROS | 2008 | 85 |
| Boosting Relational Sequence Alignments.
|
ICDM | 2008 | 25 |
| Non-parametric policy gradients: a unified treatment of propositional and relational domains.
|
ICML | 2008 | 71 |
| Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders.
|
RSS | 2007 | 55 |
| Integrating Naïve Bayes and FOIL.
|
JMLR | 2007 | 70 |
| Most likely heteroscedastic Gaussian process regression.
|
ICML | 2007 | 328 |
| Learning Relational Navigation Policies.
|
IROS | 2006 | 40 |
| Robust 3D Scan Point Classification using Associative Markov Networks.
|
ICRA | 2006 | 95 |
| Fisher Kernels for Relational Data.
|
ECML/PKDD | 2006 | 11 |
| TildeCRF: Conditional Random Fields for Logical Sequences.
|
ECML/PKDD | 2006 | 70 |
| Revising Probabilistic Prolog Programs.
|
ILP | 2006 | 0 |
| Relational Sequence Alignments and Logos.
|
ILP | 2006 | 0 |
| Logical Hidden Markov Models.
|
JAIR | 2006 | 0 |
| "Say EM" for Selecting Probabilistic Models for Logical Sequences.
|
UAI | 2005 | 27 |
| Towards Learning Stochastic Logic Programs from Proof-Banks.
|
AAAI | 2005 | 15 |
| nFOIL: Integrating Naïve Bayes and FOIL.
|
AAAI | 2005 | 101 |
| Logical Markov Decision Programs and the Convergence of Logical TD(lambda).
|
ILP | 2004 | 36 |
| Fisher Kernels for Logical Sequences.
|
ECML/PKDD | 2004 | 21 |
| Balios - The Engine for Bayesian Logic Programs.
|
ECML/PKDD | 2004 | 17 |
| Bellman goes relational.
|
ICML | 2004 | 121 |
| Scaled CGEM: A Fast Accelerated EM.
|
ECML/PKDD | 2003 | 10 |
| Adaptive Bayesian Logic Programs.
|
ILP | 2001 | 119 |
| Towards Combining Inductive Logic Programming with Bayesian Networks.
|
ILP | 2001 | 158 |