# Kristian Kersting

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German computer scientist

## Kristian Kersting's AcademicInfluence.com Rankings

Kristian Kerstingcomputer-science Degrees

Computer Science

#1037

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#1075

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Machine Learning

#167

World Rank

#168

Historical Rank

Artificial Intelligence

#170

World Rank

#174

Historical Rank

Algorithms

#208

World Rank

#211

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Computer Science

## Why Is Kristian Kersting Influential?

(Suggest an Edit or Addition)According to Wikipedia, Kristian Kersting is a German computer scientist. He is Professor of Artificial intelligence and Machine Learning at the Department of Computer Science at the Technische Universität Darmstadt, Head of the Artificial Intelligence and Machine Learning Lab and Co-Director of hessian.AI, the Hessian Center of Artificial Intelligence.

## Kristian Kersting's Published Works

### Published Works

- Probabilistic Inductive Logic Programming (2004) (417)
- Most likely heteroscedastic Gaussian process regression (2007) (330)
- TUDataset: A collection of benchmark datasets for learning with graphs (2020) (327)
- Bayesian Logic Programs (2001) (265)
- Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (2016) (249)
- Lifted Probabilistic Inference with Counting Formulas (2008) (218)
- Bayesian Logic Programming: Theory and Tool (2007) (185)
- Propagation kernels: efficient graph kernels from propagated information (2016) (176)
- Probabilistic logic learning (2003) (173)
- Counting Belief Propagation (2009) (170)
- Probabilistic Inductive Logic Programming - Theory and Applications (2008) (158)
- Towards Combining Inductive Logic Programming with Bayesian Networks (2001) (157)
- Gradient-based boosting for statistical relational learning: The relational dependency network case (2011) (148)
- Predicting player churn in the wild (2014) (130)
- Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions (2015) (130)
- Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. (2012) (128)
- Explanatory Interactive Machine Learning (2019) (123)
- Bellman goes relational (2004) (122)
- Adaptive Bayesian Logic Programs (2001) (119)
- Lifted Probabilistic Inference (2012) (116)
- Logical Hidden Markov Models (2011) (113)
- DeepDB: Learn from Data, not from Queries! (2019) (112)
- nFOIL: Integrating Naïve Bayes and FOIL (2005) (101)
- Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness (2008) (101)
- Learning Markov Logic Networks via Functional Gradient Boosting (2011) (100)
- How players lose interest in playing a game: An empirical study based on distributions of total playing times (2012) (98)
- Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020) (98)
- Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants (2016) (98)
- Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images (2015) (97)
- Robust 3D scan point classification using associative Markov networks (2006) (95)
- Basic Principles of Learning Bayesian Logic Programs (2008) (90)
- Learning predictive terrain models for legged robot locomotion (2008) (85)
- Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains (2018) (81)
- Predicting Purchase Decisions in Mobile Free-to-Play Games (2015) (81)
- Symbolic Dynamic Programming for First-order POMDPs (2010) (80)
- Exploiting symmetries for scaling loopy belief propagation and relational training (2013) (79)
- Faster Kernels for Graphs with Continuous Attributes via Hashing (2016) (74)
- Automated interpretation of 3D laserscanned point clouds for plant organ segmentation (2015) (73)
- Non-parametric policy gradients: a unified treatment of propositional and relational domains (2008) (72)
- Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning (2019) (72)
- Integrating Naïve Bayes and FOIL (2007) (70)
- TildeCRF: Conditional Random Fields for Logical Sequences (2006) (70)
- Descriptive matrix factorization for sustainability Adopting the principle of opposites (2012) (69)
- Multi-Agent Inverse Reinforcement Learning (2010) (69)
- Parameter Learning in Probabilistic Databases: A Least Squares Approach (2008) (67)
- Convex Non-negative Matrix Factorization in the Wild (2009) (67)
- Efficient Graph Kernels by Randomization (2012) (65)
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017) (64)
- Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes (2013) (63)
- Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (2011) (62)
- Yes we can: simplex volume maximization for descriptive web-scale matrix factorization (2010) (62)
- Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits (2020) (60)
- Exploration in relational domains for model-based reinforcement learning (2012) (59)
- Multi-Relational Learning with Gaussian Processes (2009) (58)
- Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders (2007) (55)
- Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! (2019) (55)
- Interpreting Bayesian Logic Programs (2000) (53)
- Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation (2021) (53)
- SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks (2019) (52)
- Relational Logistic Regression (2014) (52)
- Dimension Reduction via Colour Refinement (2013) (50)
- Structured Object-Aware Physics Prediction for Video Modeling and Planning (2019) (49)
- Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines (2018) (48)
- Logical Hierarchical Hidden Markov Models for Modeling User Activities (2008) (47)
- Lifted Linear Programming (2012) (47)
- Convex non-negative matrix factorization for massive datasets (2011) (46)
- How is a data-driven approach better than random choice in label space division for multi-label classification? (2016) (45)
- Data Mining and Pattern Recognition in Agriculture (2013) (44)
- Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks (2019) (44)
- Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations (2020) (44)
- Modeling Semantic Cognition as Logical Dimensionality Reduction (2008) (43)
- Stacked Gaussian Process Learning (2009) (42)
- Large pre-trained language models contain human-like biases of what is right and wrong to do (2021) (41)
- Compressing probabilistic Prolog programs (2008) (41)
- Faster Attend-Infer-Repeat with Tractable Probabilistic Models (2019) (41)
- Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices (2019) (40)
- Learning Relational Navigation Policies (2006) (40)
- Gaussian Process (2010) (39)
- Power Iterated Color Refinement (2014) (38)
- Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models (2002) (37)
- Logical Markov Decision Programs and the Convergence of Logical TD(lambda) (2004) (36)
- Erosion Band Features for Cell Phone Image Based Plant Disease Classification (2014) (36)
- Mind the Nuisance: Gaussian Process Classification using Privileged Noise (2014) (35)
- Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (2015) (35)
- Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter (2011) (34)
- Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction (2017) (34)
- Kernel Conditional Quantile Estimation via Reduction Revisited (2009) (34)
- A Bayesian regression approach to terrain mapping and an application to legged robot locomotion (2009) (33)
- Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance. (2016) (33)
- Transfer Learning via Relational Type Matching (2015) (32)
- Population Size Extrapolation in Relational Probabilistic Modelling (2014) (31)
- How Viral Are Viral Videos? (2015) (31)
- Hierarchical Convex NMF for Clustering Massive Data (2010) (31)
- LTE Connectivity and Vehicular Traffic Prediction Based on Machine Learning Approaches (2015) (31)
- Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions (2017) (30)
- Automatic Bayesian Density Analysis (2018) (30)
- Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei. (2017) (29)
- Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping (2013) (29)
- Social Network Mining with Nonparametric Relational Models (2008) (28)
- The Moral Choice Machine (2020) (28)
- Explicit Versus Implicit Graph Feature Maps: A Computational Phase Transition for Walk Kernels (2014) (28)
- Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering (2017) (27)
- Non-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF (2014) (27)
- "Say EM" for Selecting Probabilistic Models for Logical Sequences (2005) (27)
- Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures (2019) (27)
- An inductive logic programming approach to statistical relational learning (2005) (26)
- Probabilistic Deep Learning using Random Sum-Product Networks (2018) (26)
- The Weibull as a Model of Shortest Path Distributions in Random Networks (2013) (25)
- Informed Lifting for Message-Passing (2010) (25)
- Relational Sequence Learning (2008) (25)
- Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes (2010) (25)
- Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data (2015) (25)
- Boosting Relational Sequence Alignments (2008) (25)
- Logical Markov Decision Programs (2003) (24)
- Lifted Online Training of Relational Models with Stochastic Gradient Methods (2012) (24)
- Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions (2021) (24)
- Efficient Symbolic Integration for Probabilistic Inference (2018) (24)
- Scaling Lifted Probabilistic Inference and Learning Via Graph Databases (2016) (24)
- Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks (2018) (23)
- Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range (2019) (23)
- Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study (2012) (23)
- Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach (2014) (23)
- Collective attention to social media evolves according to diffusion models (2014) (23)
- Pre-Symptomatic Prediction of Plant Drought Stress Using Dirichlet-Aggregation Regression on Hyperspectral Images (2012) (22)
- pyGPs: a Python library for Gaussian process regression and classification (2015) (22)
- Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (2015) (22)
- Generalized First Order Decision Diagrams for First Order Markov Decision Processes (2009) (22)
- Beyond 2D-grids: a dependence maximization view on image browsing (2010) (22)
- Fisher Kernels for Logical Sequences (2004) (21)
- Self-Taught Decision Theoretic Planning with First Order Decision Diagrams (2010) (21)
- Efficient Lifting of MAP LP Relaxations Using k-Locality (2014) (21)
- Learning to hash logistic regression for fast 3D scan point classification (2010) (21)
- Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach (2018) (20)
- Boosting relational dependency networks (2010) (20)
- Statistical Relational Learning (2010) (20)
- Strong Regularities in Growth and Decline of Popularity of Social Media Services (2014) (20)
- Plant disease detection by hyperspectral imaging: from the lab to the field (2017) (20)
- From Big Data to Big Artificial Intelligence? (2018) (20)
- Larger Residuals, Less Work: Active Document Scheduling for Latent Dirichlet Allocation (2011) (20)
- Generative Learning (2010) (19)
- A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels (2017) (19)
- Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators (2018) (19)
- Statistical Relational AI : Logic , Probability and Computation (2011) (19)
- Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models (2010) (18)
- A unifying view of explicit and implicit feature maps of graph kernels (2017) (18)
- Stochastic Online Anomaly Analysis for Streaming Time Series (2017) (18)
- Relational linear programming (2017) (18)
- Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash (2021) (18)
- User Label Leakage from Gradients in Federated Learning (2021) (18)
- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach (2017) (17)
- Exploration in Relational Worlds (2010) (17)
- Learning Preferences with Hidden Common Cause Relations (2009) (17)
- Balios - The Engine for Bayesian Logic Programs (2004) (17)
- Relating Graph Neural Networks to Structural Causal Models (2021) (17)
- Core Dependency Networks (2018) (17)
- Non-negative matrix factorization for the near real-time interpretation of absorption effects in elemental distribution images acquired by X-ray fluorescence imaging. (2016) (16)
- Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations (2020) (16)
- Simplex Distributions for Embedding Data Matrices over Time (2012) (16)
- Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach (2016) (15)
- Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models (2021) (15)
- Language Models have a Moral Dimension (2021) (15)
- Towards Learning Stochastic Logic Programs from Proof-Banks (2005) (15)
- Lifting Relational MAP-LPs using Cluster Signatures (2014) (15)
- Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation (2011) (15)
- Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants (2016) (15)
- BERT has a Moral Compass: Improvements of ethical and moral values of machines (2019) (15)
- Multi-task Learning with Task Relations (2011) (15)
- Lifted Message Passing as Reparametrization of Graphical Models (2014) (14)
- Parameter estimation in ProbLog from annotated queries (2010) (14)
- Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain (2014) (14)
- Challenges for Relational Reinforcement Learning (2004) (14)
- Topic Models Conditioned on Relations (2010) (14)
- Sponsored Search (2010) (14)
- "Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users (2018) (13)
- Color Refinement and Its Applications (2021) (13)
- Lifted Belief Propagation : Pairwise Marginals and Beyond (2010) (13)
- CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability (2021) (13)
- Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs (2019) (13)
- Relational Sequence Alignments and Logos (2007) (13)
- Reduce and Re-Lift: Bootstrapped Lifted Likelihood Maximization for MAP (2013) (12)
- Agriculture's Technological Makeover (2012) (12)
- User-Level Label Leakage from Gradients in Federated Learning (2021) (12)
- Symbolic Dynamic Programming for Continuous State and Observation POMDPs (2012) (12)
- Probabilistic Inductive Querying Using ProbLog (2010) (12)
- Simplex Volume Maximization (SiVM): A matrix factorization algorithm with non-negative constrains and low computing demands for the interpretation of full spectral X-ray fluorescence imaging data (2017) (12)
- Model-based Approximate Query Processing (2018) (11)
- Fisher Kernels for Relational Data (2006) (11)
- A Typology to Explore and Guide Explanatory Interactive Machine Learning (2022) (11)
- Early Prediction of Coronary Artery Calcification Levels Using Machine Learning (2013) (11)
- Systems AI: A Declarative Learning Based Programming Perspective (2018) (11)
- Scaled CGEM: A Fast Accelerated EM (2003) (10)
- Structure learning for relational logistic regression: an ensemble approach (2018) (10)
- Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks (2022) (10)
- Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data (2019) (10)
- Boosted Statistical Relational Learners (2014) (9)
- Relational Logistic Regression: The Directed Analog of Markov Logic Networks (2014) (9)
- Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge (2014) (9)
- CryptoSPN: Privacy-preserving Sum-Product Network Inference (2020) (9)
- Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data (2012) (9)
- Statistical Relational Artificial Intelligence (2016) (9)
- Aggregation and Population Growth: The Relational Logistic Regression and Markov Logic Cases (2012) (9)
- Stratified gradient boosting for fast training of conditional random fields (2007) (9)
- Efficient Sequential Clamping for Lifted Message Passing (2011) (9)
- Towards Argumentation-based Classification (2017) (8)
- DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection (2021) (8)
- More influence means less work: fast latent dirichlet allocation by influence scheduling (2011) (8)
- Symbolic Dynamic Programming (2010) (8)
- Lifted Filtering via Exchangeable Decomposition (2018) (8)
- Learning to transfer optimal navigation policies (2007) (8)
- Whittle Networks: A Deep Likelihood Model for Time Series (2021) (8)
- Matrix- and Tensor Factorization for Game Content Recommendation (2019) (7)
- Heteroscedastic Gaussian Process Regression for Modeling Range Sensors in Mobile Robotics (2005) (7)
- Sequential Data (2010) (7)
- Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning (2019) (7)
- Fitted Q-Learning for Relational Domains (2020) (7)
- High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline (2014) (7)
- Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels (2013) (7)
- Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression (2021) (7)
- Neuro-Symbolic Forward Reasoning (2021) (7)
- Conditional Sum-Product Networks: Modular Probabilistic Circuits via Gate Functions (2021) (7)
- Machine Learning and Knowledge Discovery in Databases (2013) (7)
- Logic and Learning (Dagstuhl Seminar 19361) (2019) (7)
- Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants (2012) (7)
- Equitable Partitions of Concave Free Energies (2015) (7)
- Learning attribute grammars for movement primitive sequencing (2020) (7)
- Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation (2015) (7)
- Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction (2021) (7)
- Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models (2022) (7)
- Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations (2021) (7)
- DP-CTGAN: Differentially Private Medical Data Generation Using CTGANs (2022) (7)
- Industrial Data Science: Developing a Qualification Concept for Machine Learning in Industrial Production (2020) (6)
- Neural Networks for Relational Data (2019) (6)
- Learning Using Unselected Features (LUFe) (2016) (6)
- The Biased Artist: Exploiting Cultural Biases via Homoglyphs in Text-Guided Image Generation Models (2022) (6)
- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization (2013) (6)
- Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content? (2022) (6)
- A Structural GEM for Learning Logical Hidden Markov Models (2003) (6)
- Editorial: Statistical Relational Artificial Intelligence (2019) (6)
- Kernelized map matching (2010) (6)
- Decision-theoretic planning with generalized first-order decision diagrams (2011) (6)
- The Symbolic Interior Point Method (2016) (5)
- Humane Anthropomorphic Agents : the Quest for the Outcome Measure (2019) (5)
- A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain (2012) (5)
- Pairwise Markov Logic (2012) (5)
- Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives (2018) (5)
- Monte-Carlo Graph Search for AlphaZero (2020) (5)
- Parameterizing the Distance Distribution of Undirected Networks (2015) (5)
- “Deep Phenotyping” of Early Plant Response to Abiotic Stress Using Non-invasive Approaches in Barley (2013) (5)
- An inductive logic programming approach to statistical relational learning: Thesis (2006) (5)
- Generalization Performance (2010) (5)
- Where traffic meets DNA: mobility mining using biological sequence analysis revisited (2011) (5)
- Stream Mining (2010) (5)
- Spam Detection (2010) (5)
- Bayesian Learning of Logical Hidden Markov Models (2002) (5)
- Unbiased conjugate direction boosting for conditional random fields (2006) (5)
- Expressivity Analysis for PL-Languages (2006) (5)
- Lifted Inference for Convex Quadratic Programs (2017) (4)
- Rickrolling the Artist: Injecting Invisible Backdoors into Text-Guided Image Generation Models (2022) (4)
- Differentially Private Variational Inference for Non-conjugate Models (4)
- Samuel's Checkers Player (2010) (4)
- Learning Relational Probabilistic Models from Partially Observed Data-Opening the Closed-World Assumption (2013) (4)
- Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage (2016) (4)
- Interactive Data Analytics for the Humanities (2017) (4)
- Residual Sum-Product Networks (2020) (4)
- Efficient Learning for Hashing Proportional Data (2012) (4)
- Traffic Simulations with Empirical Data: How to Replace Missing Traffic Flows? (2016) (4)
- Recurrent Rational Networks (2021) (4)
- Multi-evidence Lifted Message Passing (2011) (4)
- GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science (2013) (4)
- Logical Hidden Markov Models (Extendes abstract) (2002) (4)
- Rethinking Computer Science Through AI (2020) (4)
- Reasoning about Large Populations with Lifted Probabilistic Inference (2007) (4)
- Automatic Synthesis of FPGA-based Accelerators for the Sum-Product Network Inference Problem (2018) (4)
- DeepDB (2020) (4)
- Lifted Message Passing for Satisfiability (2010) (4)
- Can Foundation Models Talk Causality? (2022) (4)
- Adaptable Adapters (2022) (4)
- Cell Phone Image-Based Plant Disease Classification (2016) (4)
- Kernelized Map Matching for noisy trajectories (2010) (4)
- To Trust or Not To Trust Prediction Scores for Membership Inference Attacks (2021) (4)
- Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs (2019) (4)
- A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck (2014) (4)
- Bayesian Logi Programs ? (2000) (4)
- Lifted Inference via k-Locality (2013) (4)
- ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition (2022) (3)
- RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting (2021) (3)
- Revising Probabilistic Prolog Programs (2007) (3)
- Towards Understanding and Arguing with Classifiers: Recent Progress (2020) (3)
- Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning (2011) (3)
- Global Weisfeiler-Lehman Graph Kernels (2017) (3)
- Relational Linear Programs (2014) (3)
- Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013 (2013) (3)
- CLIPping Privacy: Identity Inference Attacks on Multi-Modal Machine Learning Models (2022) (3)
- Representational Power of Probabilistic-Logical Models : From Upgrading to Downgrading ∗ (2003) (3)
- Improving AlphaZero Using Monte-Carlo Graph Search (2021) (3)
- Neuro-Symbolic Verification of Deep Neural Networks (2022) (3)
- The Stable Artist: Steering Semantics in Diffusion Latent Space (2022) (3)
- Efficient Information Theoretic Clustering on Discrete Lattices (2013) (3)
- Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction (2016) (3)
- Estimating the parameters of probabilistic databases from probabilistically weighted queries and proofs [extended abstract] (2008) (3)
- A Revised Publication Model for ECML PKDD (2012) (3)
- MapReduce Lifting for Belief Propagation (2013) (3)
- Integrating research into teaching (2005) (3)
- Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation (2020) (3)
- Matrix Factorization as Search (2012) (3)
- ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries (2009) (3)
- Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization (2015) (3)
- Predicting Player Chum In the Wild (2014) (3)
- Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007 (2008) (3)
- Maximum Entropy Models of Shortest Path and Outbreak Distributions in Networks (2015) (3)
- RELOOP: A Python-Embedded Declarative Language for Relational Optimization (2016) (3)
- Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm (2002) (3)
- Semantic Interpretation of Multi-Modal Human-Behaviour Data (2017) (2)
- CryptoSPN: Expanding PPML beyond Neural Networks (2020) (2)
- Revision Transformers: Getting RiT of No-Nos (2022) (2)
- Machine Learning and Knowledge Discovery in Databases (2013) (2)
- Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study (2015) (2)
- Reports of the AAAI 2014 Conference Workshops (2015) (2)
- DAFNe: A One-Stage Anchor-Free Approach for Oriented Object Detection (2021) (2)
- Relational Sequence Alignment (2006) (2)
- Combining video and sequential statistical relational techniques to monitor card games (2010) (2)
- Early Prediction of Coronary Artery Calcication Levels Using Statistical Relational Learning (2012) (2)
- Genetic Grouping (2010) (2)
- Genetic Clustering (2010) (2)
- Collective Attention on the Web (2016) (2)
- Intriguing Parameters of Structural Causal Models (2021) (2)
- Can Computers Learn from the Aesthetic Wisdom of the Crowd? (2013) (2)
- Stacked Generalization (2010) (2)
- Machine Learning Assisted Pattern Matching: Insight into Oxide Electronic Device Performance by Phase Determination in 4D-STEM Datasets (2020) (2)
- Enabling Virtual Met Masts for wind energy applications through machine learning-methods (2022) (2)
- Estimating the Importance of Relational Features by Using Gradient Boosting (2020) (2)
- From Lifted Inference to Lifted Models (2012) (2)
- SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming (2021) (2)
- Structure Learning with Hidden Data in Relational Domains (2012) (2)
- Neural Conditional Gradients (2018) (2)
- Interactively Providing Explanations for Transformer Language Models (2021) (2)
- Interactively Generating Explanations for Transformer Language Models (2021) (2)
- SEGA: Instructing Diffusion using Semantic Dimensions (2023) (2)
- Tearing Apart NOTEARS: Controlling the Graph Prediction via Variance Manipulation (2022) (2)
- 07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis (2007) (1)
- Tools for Finding Inconsistencies in Real-world Logic-based Systems (2012) (1)
- SRL without Tears: An ILP Perspective (2008) (1)
- Boosting in the Presence of Missing Data (2014) (1)
- Bellman goes Relational ( extended abstract ) 1 (2008) (1)
- Solution Concept (2010) (1)
- Stratified Cross Validation (2010) (1)
- Coreset based Dependency Networks (2017) (1)
- Spike-Timing-Dependent Plasticity (2010) (1)
- Human-inthe-loop Learning for Probabilistic Programming (2018) (1)
- Early Identification of Plant Stress in Hyperspectral Images (2015) (1)
- Reports of the AAAI 2010 Conference Workshops (2010) (1)
- On the Tractability of Neural Causal Inference (2021) (1)
- Inducing Probabilistic Context-Free Grammars for the Sequencing of Robot Movement Primitives (2018) (1)
- Lifted Convex Quadratic Programming (2016) (1)
- Growth Function (2010) (1)
- Random Sum-Product Forests with Residual Links (2019) (1)
- Coinciding Walk Kernels (2013) (1)
- Graph Enhanced Memory Networks for Sentiment Analysis (2017) (1)
- Stratified conjugate gradient boosting for fast training of conditional random fields (2007) (1)
- PADÉ ACTIVATION UNITS: END-TO-END LEARNING (2019) (1)
- Stratified Gradient Boosting for Fast Training of CRFs ( Extended Abstract ) (2007) (1)
- A Taxonomy for Inference in Causal Model Families (2021) (1)
- On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving (2011) (1)
- Shannon's Information (2010) (1)
- Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text (2013) (1)
- Mining and Learning with Graphs, MLG 2007, Firence, Italy, August 1-3, 2007, Proceedings (2007) (1)
- Bellman goes Relational (extended abstract) (2004) (1)
- Discriminative Non-Parametric Learning of Arithmetic Circuits (2020) (1)
- SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091) (2020) (1)
- Modeling heart procedures from EHRs: An application of exponential families (2017) (1)
- Unsupervised Multi-sensor Anomaly Localization with Explainable AI (2022) (1)
- Gradient-based Counterfactual Explanations using Tractable Probabilistic Models (2022) (1)
- Graph-based Approximate Counting for Relational Probabilistic Models (2015) (1)
- Learning Through Advice-Seeking via Transfer (2016) (1)
- The Causal Loss: Driving Correlation to Imply Causation (2021) (1)
- Machine learning and knowledge discovery in databases, European Conference, ECML PKDD 2013, Proceedings, Part III (2013) (1)
- Imitation Learning in Relational Domains Using Functional Gradient Boosting (1)
- Exploiting Domain Knowledge as Causal Independencies in Modeling Gestational Diabetes (2022) (1)
- Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions (2015) (1)
- Guest editor’s introduction: special issue of the ECML PKDD 2013 journal track (2013) (1)
- Do Multilingual Language Models Capture Differing Moral Norms? (2022) (1)
- AtMan: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation (2023) (1)
- Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement (2022) (1)
- Speaking Multiple Languages Affects the Moral Bias of Language Models (2022) (1)
- Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness (2023) (1)
- Hyperspectral imaging in the UV-range allows for differentiation of sugar beet diseases based on changes of secondary plant metabolites. (2022) (1)
- Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020) (1)
- Blind Source Separation in SPED datasets: Machine learning assisted phase and orientation determination in multilayer oxide electronic thin film devices (2020) (1)
- Inferring Offensiveness In Images From Natural Language Supervision (2021) (1)
- A Typology to Explore the Mitigation of Shortcut Behavior (2022) (1)
- Predictive Whittle networks for time series (2022) (1)
- HANF: Hyperparameter And Neural Architecture Search in Federated Learning (2022) (1)
- Towards a Solution to Bongard Problems: A Causal Approach (2022) (1)
- Online Proceedings of the Workshop on Statistical Relational Learning (SRL-06) (2006) (1)
- O Scientist, Where Art Thou? Affiliation Propagation for Geo-Referencing Scientific Publications (2011) (0)
- Lifted Inference for Hybrid Relational Models (2021) (0)
- Semantic Interpretation of Multi-Modal Human-Behaviour Data (2017) (0)
- Statistical Relational Learning ( 1 ) : Introduction to Probabilistic Inductive Logic Programming ( PILP ) (2017) (0)
- Combining AI and AM - Improving Approximate Matching through Transformer Networks (2022) (0)
- Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data (2021) (0)
- Finding Structure and Causality in Linear Programs (2022) (0)
- On the Trade-Off Between Iterative Classification and Collective Classification : First Experimental Results (0)
- Approximate Counting for Fast Inference and Learning in Probabilistic Programming (2018) (0)
- Gradient-based boosting for statistical relational learning: The relational dependency network case (2011) (0)
- Algebraic Equivalence of Linear Structural Equation Models (2017) (0)
- Sum-Product-Attention Networks: Leveraging Self-Attention in Probabilistic Circuits (2021) (0)
- Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach (2018) (0)
- Structural Causal Interpretation Theorem (2021) (0)
- Gaussian Process Models for Colored Graphs (2008) (0)
- Liftability Theory of Variational Inference (2021) (0)
- Starting Clause (2010) (0)
- Genetic Neural Networks (2010) (0)
- Symbolic Regression (2010) (0)
- Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming (2022) (0)
- cient Graph Kernels by Randomization (2012) (0)
- Propagation Kernels (2014) (0)
- Propagation kernels: efficient graph kernels from propagated information (2015) (0)
- Representation Matters: The Game of Chess Poses a Challenge to Vision Transformers (2023) (0)
- Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data (2015) (0)
- Solving Semantic Ambiguity (2010) (0)
- Sequential Inductive Transfer (2010) (0)
- Making AI Smarter (2018) (0)
- Structured Data Clustering (2010) (0)
- Preface (2013) (0)
- Convex NMF on Non-Convex Massiv Data (2010) (0)
- Exploiting symmetries for scaling loopy belief propagation and relational training (2013) (0)
- CryptoSPN: Expanding PPML beyond Neural Networks (Contributed Talk) (2020) (0)
- Grammar Learning (2010) (0)
- Squared Error Loss (2010) (0)
- Active Feature Acquisition via Human Interaction in Relational domains (2023) (0)
- Generality And Logic (2010) (0)
- Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention (2021) (0)
- $$\alpha$$ILP: thinking visual scenes as differentiable logic programs (2023) (0)
- Speedup Learning for Planning (2010) (0)
- Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (2015) (0)
- LogicRank: Logic Induced Reranking for Generative Text-to-Image Systems (2022) (0)
- Differentiable Meta logical Programming (2022) (0)
- Predictive Whittle Networks for Time Series Supplementary Material (2022) (0)
- Shattering Coefficient (2010) (0)
- Declarative Learning-Based Programming as an Interface to AI Systems (2019) (0)
- On Hybrid and Systems AI (2020) (0)
- Lifted Conditioning for Pairwise Marginals and Beyond (2010) (0)
- Symmetrization Lemma (2010) (0)
- Interactively Generating Explanations for Transformer-based Language Models (2021) (0)
- Meta-Learning Runge-Kutta (2019) (0)
- Alfie: An Interactive Robot with Moral Compass (2020) (0)
- Growing Set (2010) (0)
- Activity Context-Aware System Architectures (2013) (0)
- Genetic Attribute Construction (2010) (0)
- KOGWIS2018: Computational Approaches to Cognitive Science (2018) (0)
- Guest editor’s introduction: special issue of the ECML PKDD 2013 journal track (2013) (0)
- Transfer Learning Across Relational and Uncertain Domains : A Language-Bias Approach (2015) (0)
- Using Point Estimates of Local Smoothness (2008) (0)
- Explaining Deep Tractable Probabilistic Models: The sum-product network case (2021) (0)
- Lifted Parameter Learning in Relational Models (2012) (0)
- Biological Sequence Analysis meets Mobility Mining (2011) (0)
- Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain (2013) (0)
- Towards Understanding and Arguing with Classifiers: Recent Progress (2020) (0)
- Adaptive Rational Activations to Boost Deep Reinforcement Learning (2021) (0)
- Genetic Feature Selection (2010) (0)
- Neural-Probabilistic Answer Set Programming (2022) (0)
- FEATHERS: Federated Architecture and Hyperparameter Search (2022) (0)
- Does CLIP Know My Face? (2022) (0)
- XAI Establishes a Common Ground Between Machine Learning and Causality (2022) (0)
- Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161) (2022) (0)
- Can Computers Learn from the Aesthetic Wisdom of the Crowd? (2012) (0)
- ILLUME: Rationalizing Vision-Language Models by Interacting with their Jabber (2022) (0)
- Probabilistic Circuits That Know What They Don't Know (2023) (0)
- Recent Advancements in Tractable Probabilistic Inference (2022) (0)
- Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization (2015) (0)
- Boosting Undirected Relational Models (2014) (0)
- Image Classifiers Leak Sensitive Attributes About Their Classes (2023) (0)
- Deep Rational Reinforcement Learning (2021) (0)
- Attributions Beyond Neural Networks: The Linear Program Case (2022) (0)
- Simple Bayes (2010) (0)
- Genetics-Based Machine Learning (2010) (0)
- Model-based ApproximateQuery Processing (2018) (0)
- Structural Credit Assignment (2010) (0)
- ILLUME: Rationalizing Vision-Language Models through Human Interactions (2022) (0)
- Transformer-Boosted Anomaly Detection with Fuzzy Hashes (2022) (0)
- Can Linear Programs Have Adversarial Examples? A Causal Perspective (2021) (0)
- Causal Explanations of Structural Causal Models (2021) (0)
- Boosting Statistical Relational Learning in Action (2014) (0)
- A typology for exploring the mitigation of shortcut behaviour (2022) (0)
- Boosting (Bi-)Directed Relational Models (2014) (0)
- Proceedings, Part II, of the European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 8189 (2013) (0)
- Sum-Product Networks for Hybrid Domains (2017) (0)
- DeepVizdom: Deep Interactive Data Exploration (2018) (0)
- One Explanation Does Not Fit XIL (2023) (0)
- String Matching Algorithm (2010) (0)
- Learning the functional connectivity in neuronal cultures (2006) (0)
- Automated interpretation of 3D laserscanned point clouds for plant organ segmentation (2015) (0)
- Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis (2022) (0)
- Global Weisfeiler-Lehman Kernels (2018) (0)
- Modelling Multivariate Ranking Functions with Min-Sum Networks (2020) (0)
- Boosting Object Representation Learning via Motion and Object Continuity (2022) (0)
- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits (2021) (0)
- Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis (2022) (0)
- Machines Explaining Linear Programs (2022) (0)
- Generative Adversarial Neural Cellular Automata (2021) (0)
- Data Mining and Pattern Recognition in Agriculture (2013) (0)
- Independence and D-separation in Abstract Argumentation (2020) (0)
- From Big Data to Big Artificial Intelligence? (2018) (0)
- Matrix- and Tensor Factorization for Game Content Recommendation (2019) (0)
- General-to-Specific Search (2010) (0)
- The AAAI-13 Conference Workshops (2013) (0)
- Making AI Smarter (2018) (0)
- Descriptive matrix factorization for sustainability Adopting the principle of opposites (2011) (0)
- Accelarating Imitation Learning in Relational Domains via Transfer by Initialization Report Title (2014) (0)
- F INDING S TRUCTURE AND C AUSALITY IN L INEAR P ROGRAMS (2022) (0)
- String Kernel (2010) (0)
- Relational tree ensembles and feature rankings (2022) (0)
- Towards Coreset Learning in Probabilistic Circuits (2022) (0)
- Sum-Product-Attention Networks: Leveraging Self-Attention in Energy-Based Probabilistic Circuits (2022) (0)
- Relational Gaussian Processes for Learning Preference Relations (2009) (0)
- Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learning (2011) (0)
- Coresets for Dependency Networks (2017) (0)
- Model Revision from Temporal Logic Properties in Computational Systems Biology (2019) (0)
- BAYESIAN LEARNING OF LOGICAL HIDDEN (2007) (0)
- Non-Negative Matrix Factorization for the near real time interpretation of absorption effects in elemental distribution images acquired by XRF imaging (2015) (0)
- Do Not Trust Prediction Scores for Membership Inference Attacks (2021) (0)
- Distributed Relational State Representations for Complex Stochastic Processes (Extended Abstract) (2007) (0)
- Squared Error (2010) (0)
- Pearl Causal Hierarchy on Image Data: Intricacies & Challenges (2022) (0)
- Semantic Mapping (2010) (0)
- DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets (2019) (0)
- Statistical Relational Artificial Intelligence, Papers from the 2010 AAAI Workshop, Atlanta, Georgia, USA, July 12, 2010 (2010) (0)
- Statistical Physics Of Learning (2010) (0)
- Chapter 10 Probabilistic Inductive Querying Using ProbLog (2010) (0)
- Generalized Delta Rule (2010) (0)
- Synonyms Relational Dynamic Programming , Dynamic Programming for Relational Domains , Relational Value Iteration Definition (2009) (0)

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## Other Resources About Kristian Kersting

## What Schools Are Affiliated With Kristian Kersting?

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