#158,351

Most Influential Person

German computer scientist

Kristian Kersting is head of the Artificial Intelligence and Machine Learning Lab and professor of artificial intelligence and machine learning at the Technische Universität Darmstadt’s department of computer science. He earned a Ph.D. in computer science from the University of Freiburg before completing post-doctoral studies at Katholeike Universiteit Leuven and the Massachusetts Institute of Technology.

His research has focused on deep probabilistic learning, artificial intelligence, statistical relational artificial intelligence, and probabilistic programming. He is a researcher at ATHENE, which is the largest national research facility devoted to IT security in all of Europe. He formerly led a research team at the Fraunhofer Institute for Intelligent Analysis and Information Systems.

He has held several faculty positions as well, as junior professor at the University of Bonn, associate professor at the Technical University of Dortmund, before becoming professor of machine learning and artificial intelligence at his current role.

Kersting was awarded the prestigious Fraunhofer Attract research grant in 2008, which bestows 2.5 million euros over five years to cover research expenses. He is a Fellow of the European Laboratory for Learning and Intelligent Systems and the European Association for Artificial Intelligence. Most recently, he was honored with Germany’s AI Award, Deustcher Kl-Preis, which awarded him 100,000 euros.

**Featured in Top Influential Computer Scientists Today**

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.

- Probabilistic Inductive Logic Programming (279)
- Most likely heteroscedastic Gaussian process regression (269)
- Bayesian Logic Programs (267)
- Lifted Probabilistic Inference with Counting Formulas (210)
- Bayesian Logic Programming: Theory and Tool (177)
- Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (173)
- Probabilistic logic learning (170)
- Counting Belief Propagation (168)
- Towards Combining Inductive Logic Programming with Bayesian Networks (158)
- Probabilistic Inductive Logic Programming - Theory and Applications (139)
- Gradient-based boosting for statistical relational learning: The relational dependency network case (128)
- Probabilistic inductive logic programming (125)
- Adaptive Bayesian Logic Programs (121)
- Bellman goes relational (114)
- Logical Hidden Markov Models (112)
- Propagation kernels: efficient graph kernels from propagated information (112)
- Lifted Probabilistic Inference (111)
- Predicting player churn in the wild (109)
- Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. (108)
- Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions (95)
- Robust 3D scan point classification using associative Markov networks (94)
- How players lose interest in playing a game: An empirical study based on distributions of total playing times (93)
- nFOIL: Integrating Naïve Bayes and FOIL (91)
- Learning Markov Logic Networks via Functional Gradient Boosting (89)
- Basic Principles of Learning Bayesian Logic Programs (88)
- Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness (84)
- Learning predictive terrain models for legged robot locomotion (82)
- Metro Maps of Plant Disease Dynamics—Automated Mining of Differences Using Hyperspectral Images (76)
- Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants (70)
- Exploiting symmetries for scaling loopy belief propagation and relational training (70)
- Non-parametric policy gradients: a unified treatment of propositional and relational domains (69)
- TildeCRF: Conditional Random Fields for Logical Sequences (68)
- Integrating Naïve Bayes and FOIL (67)
- TUDataset: A collection of benchmark datasets for learning with graphs (67)
- Symbolic Dynamic Programming for First-order POMDPs (66)
- Predicting Purchase Decisions in Mobile Free-to-Play Games (64)
- Convex Non-negative Matrix Factorization in the Wild (64)
- Parameter Learning in Probabilistic Databases: A Least Squares Approach (64)
- Descriptive matrix factorization for sustainability Adopting the principle of opposites (62)
- Efficient Graph Kernels by Randomization (61)
- Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains (59)
- Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach (57)
- Multi-Relational Learning with Gaussian Processes (56)
- Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders (54)
- Mathematical Models of Fads Explain the Temporal Dynamics of Internet Memes (52)
- Automated interpretation of 3D laserscanned point clouds for plant organ segmentation (51)
- Exploration in relational domains for model-based reinforcement learning (49)
- Interpreting Bayesian Logic Programs (49)
- Multi-Agent Inverse Reinforcement Learning (48)
- Logical Hierarchical Hidden Markov Models for Modeling User Activities (48)
- Relational Logistic Regression (48)
- Faster Kernels for Graphs with Continuous Attributes via Hashing (46)
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (46)
- Lifted Linear Programming (45)
- DeepDB: Learn from Data, not from Queries! (44)
- Dimension Reduction via Colour Refinement (44)
- Convex non-negative matrix factorization for massive datasets (40)
- Explanatory Interactive Machine Learning (40)
- Towards Discovering Structural Signatures of Protein Folds Based on Logical Hidden Markov Models (39)
- Learning Relational Navigation Policies (38)
- Modeling Semantic Cognition as Logical Dimensionality Reduction (37)
- Compressing probabilistic Prolog programs (37)
- Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning (35)
- Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter (34)
- Power Iterated Color Refinement (33)
- Stacked Gaussian Process Learning (33)
- Mind the Nuisance: Gaussian Process Classification using Privileged Noise (32)
- Kernel Conditional Quantile Estimation via Reduction Revisited (32)
- Erosion Band Features for Cell Phone Image Based Plant Disease Classification (31)
- SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks (31)
- Hierarchical Convex NMF for Clustering Massive Data (30)
- A Bayesian regression approach to terrain mapping and an application to legged robot locomotion (29)
- Structured Object-Aware Physics Prediction for Video Modeling and Planning (28)
- Social Network Mining with Nonparametric Relational Models (28)
- How is a data-driven approach better than random choice in label space division for multi-label classification? (28)
- Data Mining and Pattern Recognition in Agriculture (27)
- "Say EM" for Selecting Probabilistic Models for Logical Sequences (27)
- Making deep neural networks right for the right scientific reasons by interacting with their explanations (26)
- Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases (26)
- Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance. (26)
- Automatic Bayesian Density Analysis (26)
- Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions (26)
- Probabilistic Inductive Logic Programming (26)
- Relational Sequence Learning (25)
- Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data (25)
- LTE Connectivity and Vehicular Traffic Prediction Based on Machine Learning Approaches (25)
- An inductive logic programming approach to statistical relational learning (25)
- Boosting Relational Sequence Alignments (24)
- Logical Markov Decision Programs (24)
- Informed Lifting for Message-Passing (24)
- Faster Attend-Infer-Repeat with Tractable Probabilistic Models (24)
- Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes (24)
- The Weibull as a Model of Shortest Path Distributions in Random Networks (24)
- Lifted Online Training of Relational Models with Stochastic Gradient Methods (23)
- Statistical Relational Learning of Grammar Rules for 3D Building Reconstruction (23)
- Population Size Extrapolation in Relational Probabilistic Modelling (23)
- Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! (22)
- Gaussian Process (22)
- How Viral Are Viral Videos? (22)
- Probabilistic Deep Learning using Random Sum-Product Networks (22)
- Learning from Imbalanced Data in Relational Domains: A Soft Margin Approach (22)
- Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits (22)
- Self-Taught Decision Theoretic Planning with First Order Decision Diagrams (21)
- Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks (21)
- Fisher Kernels for Logical Sequences (21)
- Transfer Learning via Relational Type Matching (21)
- Scaling Lifted Probabilistic Inference and Learning Via Graph Databases (21)
- Generalized First Order Decision Diagrams for First Order Markov Decision Processes (21)
- Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (20)
- Explicit Versus Implicit Graph Feature Maps: A Computational Phase Transition for Walk Kernels (20)
- Learning to hash logistic regression for fast 3D scan point classification (20)
- Larger Residuals, Less Work: Active Document Scheduling for Latent Dirichlet Allocation (20)
- Collective attention to social media evolves according to diffusion models (20)
- Efficient Lifting of MAP LP Relaxations Using k-Locality (20)
- Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering (18)
- Automated identification of sugar beet diseases using smartphones (18)
- Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei. (18)
- Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks (18)
- Towards Learning Stochastic Logic Programs from Proof-Banks (18)
- pyGPs: a Python library for Gaussian process regression and classification (18)
- Boosting relational dependency networks (18)
- Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models (18)
- Learning Preferences with Hidden Common Cause Relations (18)
- Non-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF (18)
- Exploration in Relational Worlds (17)
- Deterministic CUR for Improved Large-Scale Data Analysis: An Empirical Study (17)
- Strong Regularities in Growth and Decline of Popularity of Social Media Services (17)
- Efficient Symbolic Integration for Probabilistic Inference (17)
- Balios - The Engine for Bayesian Logic Programs (17)
- Relational linear programming (16)
- Beyond 2D-grids: a dependence maximization view on image browsing (16)
- Graph Kernels for Object Category Prediction in Task-Dependent Robot Grasping (15)
- Parameter estimation in ProbLog from annotated queries (15)
- Semantics Derived Automatically from Language Corpora Contain Human-like Moral Choices (15)
- Challenges for Relational Reinforcement Learning (15)
- Statistical Relational AI : Logic , Probability and Computation (14)
- Pre-Symptomatic Prediction of Plant Drought Stress Using Dirichlet-Aggregation Regression on Hyperspectral Images (14)
- Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach (14)
- Lifted Message Passing as Reparametrization of Graphical Models (14)
- Relational Restricted Boltzmann Machines: A Probabilistic Logic Learning Approach (14)
- Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants (14)
- Simplex Distributions for Embedding Data Matrices over Time (14)
- Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations (14)
- Topic Models Conditioned on Relations (14)
- Stochastic Online Anomaly Analysis for Streaming Time Series (14)
- Lifting Relational MAP-LPs using Cluster Signatures (13)
- From Big Data to Big Artificial Intelligence? (13)
- Plant disease detection by hyperspectral imaging: from the lab to the field (13)
- Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation (13)
- Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators (13)
- Machine Learning and Artificial Intelligence: Two Fellow Travelers on the Quest for Intelligent Behavior in Machines (13)
- Non-negative matrix factorization for the near real-time interpretation of absorption effects in elemental distribution images acquired by X-ray fluorescence imaging. (12)
- A unifying view of explicit and implicit feature maps of graph kernels (12)
- Lifted Belief Propagation : Pairwise Marginals and Beyond (12)
- Reduce and Re-Lift: Bootstrapped Lifted Likelihood Maximization for MAP (12)
- Semantic and geometric reasoning for robotic grasping: a probabilistic logic approach (12)
- Fisher Kernels for Relational Data (12)
- Relational Sequence Alignments and Logos (12)
- Symbolic Dynamic Programming for Continuous State and Observation POMDPs (12)
- Probabilistic Inductive Querying Using ProbLog (12)
- Relational learning helps in three-way classification of Alzheimer patients from structural magnetic resonance images of the brain (12)
- "Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users (11)
- Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures (11)
- Agriculture's Technological Makeover (11)
- Multi-task Learning with Task Relations (10)
- Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range (10)
- Core Dependency Networks (10)
- Scaled CGEM: A Fast Accelerated EM (10)
- Relational Logistic Regression: The Directed Analog of Markov Logic Networks (9)
- Model-based Approximate Query Processing (9)
- Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data (9)
- Early Prediction of Coronary Artery Calcification Levels Using Machine Learning (9)
- Efficient Sequential Clamping for Lifted Message Passing (9)
- Differentially Private Variational Inference for Non-conjugate Models (9)
- Stratified gradient boosting for fast training of conditional random fields (9)
- Effectively Creating Weakly Labeled Training Examples via Approximate Domain Knowledge (9)
- Systems AI: A Declarative Learning Based Programming Perspective (9)
- Aggregation and Population Growth : The Relational Logistic Regression and Markov Logic Cases (9)
- 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 (8)
- The Moral Choice Machine (8)
- Learning to transfer optimal navigation policies (8)
- More influence means less work: fast latent dirichlet allocation by influence scheduling (8)
- Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs (7)
- Symbolic Dynamic Programming (7)
- Boosted Statistical Relational Learners (7)
- A Bayesian regression approach to terrain mapping and an application to legged robot locomotion (7)
- Statistical Relational Learning (7)
- Statistical Relational Learning (7)
- Statistical Relational Learning (7)
- High-level Reasoning and Low-level Learning for Grasping: A Probabilistic Logic Pipeline (7)
- Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels (7)
- Heteroscedastic Gaussian Process Regression for Modeling Range Sensors in Mobile Robotics (7)
- Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation (7)
- Towards Argumentation-based Classification (7)
- Lifted Filtering via Exchangeable Decomposition (6)
- An inductive logic programming approach to statistical relational learning: Thesis (6)
- Accelerating Imitation Learning in Relational Domains via Transfer by Initialization (6)
- Equitable Partitions of Concave Free Energies (6)
- Decision-theoretic planning with generalized first-order decision diagrams (6)
- Structure Learning for Relational Logistic Regression: An Ensemble Approach (6)
- A Structural GEM for Learning Logical Hidden Markov Models (6)
- Kernelized map matching (6)
- The Symbolic Interior Point Method (5)
- Unbiased conjugate direction boosting for conditional random fields (5)
- CryptoSPN: Privacy-preserving Sum-Product Network Inference (5)
- BERT has a Moral Compass: Improvements of ethical and moral values of machines (5)
- Color Refinement and Its Applications (5)
- Scaled Conjugate Gradients for Maximum Likelihood: An Empirical Comparison with the EM Algorithm (5)
- Where traffic meets DNA: mobility mining using biological sequence analysis revisited (5)
- Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data (5)
- “Deep Phenotyping” of Early Plant Response to Abiotic Stress Using Non-invasive Approaches in Barley (5)
- Expressivity Analysis for PL-Languages (5)
- Machine Learning and Knowledge Discovery in Databases (5)
- A Machine Learning Pipeline for Three-Way Classification of Alzheimer Patients from Structural Magnetic Resonance Images of the Brain (5)
- Logic and Learning (Dagstuhl Seminar 19361) (5)
- Bayesian Learning of Logical Hidden Markov Models (5)
- Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants (5)
- Pairwise Markov Logic (5)
- Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning (5)
- Global Weisfeiler-Lehman Graph Kernels (5)
- Multi-evidence Lifted Message Passing (4)
- Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007 (4)
- GeoDBLP: Geo-Tagging DBLP for Mining the Sociology of Computer Science (4)
- Learning Using Unselected Features (LUFe) (4)
- Lifted Inference for Convex Quadratic Programs (4)
- Lifted Message Passing for Satisfiability (4)
- Matrix- and Tensor Factorization for Game Content Recommendation (4)
- Traffic Simulations with Empirical Data: How to Replace Missing Traffic Flows? (4)
- Learning Relational Probabilistic Models from Partially Observed Data-Opening the Closed-World Assumption (4)
- Kernelized Map Matching for noisy trajectories (4)
- Logical Hidden Markov Models (Extendes abstract) (4)
- A Deeper Empirical Analysis of CBP Algorithm: Grounding Is the Bottleneck (4)
- Lifted Inference via k-Locality (4)
- Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations (4)
- Reasoning about Large Populations with Lifted Probabilistic Inference (4)
- Efficient Learning for Hashing Proportional Data (4)
- Relational Linear Programs (3)
- Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization (3)
- Guest editorial to the special issue on inductive logic programming, mining and learning in graphs and statistical relational learning (3)
- Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction (3)
- Neural Networks for Relational Data (3)
- ILP, the Blind, and the Elephant: Euclidean Embedding of Co-proven Queries (3)
- Integrating research into teaching (3)
- Automatic Synthesis of FPGA-based Accelerators for the Sum-Product Network Inference Problem (3)
- Learning attribute grammars for movement primitive sequencing (3)
- Inducing Probabilistic Context-Free Grammars for the Sequencing of Movement Primitives (3)
- Representational Power of Probabilistic-Logical Models : From Upgrading to Downgrading ∗ (3)
- Samuel's Checkers Player (3)
- Matrix Factorization as Search (3)
- Interactive Data Analytics for the Humanities (3)
- RELOOP: A Python-Embedded Declarative Language for Relational Optimization (3)
- MapReduce Lifting for Belief Propagation (3)
- Fitted Q-Learning for Relational Domains (3)
- Bayesian Logi Programs ? (3)
- Efficient Information Theoretic Clustering on Discrete Lattices (3)
- Predicting Player Chum In the Wild (3)
- Maximum Entropy Models of Shortest Path and Outbreak Distributions in Networks (3)
- Estimating the parameters of probabilistic databases from probabilistically weighted queries and proofs [extended abstract] (3)
- Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013 (3)
- Early Prediction of Coronary Artery Calcication Levels Using Statistical Relational Learning (2)
- Machine Learning meets Data-Driven Journalism: Boosting International Understanding and Transparency in News Coverage (2)
- Industrial Data Science: Developing a Qualification Concept for Machine Learning in Industrial Production (2)
- Modeling Coronary Artery Calcification Levels from Behavioral Data in a Clinical Study (2)
- Spike-Timing-Dependent Plasticity (2)
- Can Computers Learn from the Aesthetic Wisdom of the Crowd? (2)
- Whittle Networks: A Deep Likelihood Model for Time Series (2)
- A Revised Publication Model for ECML PKDD (2)
- Reports of the AAAI 2014 Conference Workshops (2)
- Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 8188 (2)
- Relational Sequence Alignment (2)
- Revising Probabilistic Prolog Programs (2)
- Bellman goes Relational ( extended abstract ) 1 (2)
- Resource-Efficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs (2)
- From lifted inference to lifted models (2)
- Combining video and sequential statistical relational techniques to monitor card games (2)
- Semantic Interpretation of Multi-Modal Human-Behaviour Data (2)
- Stratified Gradient Boosting for Fast Training of CRFs ( Extended Abstract ) (1)
- Coreset based Dependency Networks (1)
- Coinciding Walk Kernels (1)
- Right for Better Reasons: Training Differentiable Models by Constraining their Influence Functions (1)
- Stratified conjugate gradient boosting for fast training of conditional random fields (1)
- Collective Attention on the Web (1)
- Lifted Convex Quadratic Programming (1)
- Decomposing 3D Scenes into Objects via Unsupervised Volume Segmentation (1)
- Residual Sum-Product Networks (1)
- Discriminative Non-Parametric Learning of Arithmetic Circuits (1)
- Inducing Probabilistic Context-Free Grammars for the Sequencing of Robot Movement Primitives (1)
- Structure Learning with Hidden Data in Relational Domains (1)
- Mining and Learning with Graphs, MLG 2007, Firence, Italy, August 1-3, 2007, Proceedings (1)
- Towards Understanding and Arguing with Classifiers: Recent Progress (1)
- Bellman goes Relational (extended abstract) (1)
- Graph Enhanced Memory Networks for Sentiment Analysis (1)
- Learning Through Advice-Seeking via Transfer (1)
- SRL without Tears: An ILP Perspective (1)
- Using Commonsense Knowledge to Automatically Create (Noisy) Training Examples from Text (1)
- DeepDB (1)
- Guest editor’s introduction: special issue of the ECML PKDD 2013 journal track (1)
- 07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis (1)
- Reports of the AAAI 2010 Conference Workshops (1)
- Human-inthe-loop Learning for Probabilistic Programming (1)
- Neural Conditional Gradients (1)
- Imitation Learning in Relational Domains Using Functional Gradient Boosting (1)
- Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression (1)
- Cell Phone Image-Based Plant Disease Classification (1)
- Modeling heart procedures from EHRs: An application of exponential families (1)
- On Lifted PageRank, Kalman Filter and Towards Lifted Linear Program Solving (1)
- User Label Leakage from Gradients in Federated Learning (1)
- Boosting in the Presence of Missing Data (1)
- Early Identification of Plant Stress in Hyperspectral Images (1)
- Graph-based Approximate Counting for Relational Probabilistic Models (1)
- Machine learning and knowledge discovery in databases, European Conference, ECML PKDD 2013, Proceedings, Part III (1)
- Online Proceedings of the Workshop on Statistical Relational Learning (SRL-06) (1)
- Biological Sequence Analysis meets Mobility Mining (0)
- Gaussian Process Models for Colored Graphs (0)
- Boosting (Bi-)Directed Relational Models (0)
- Independence and D-separation in Abstract Argumentation (0)
- Accelarating Imitation Learning in Relational Domains via Transfer by Initialization Report Title (0)
- Model-based ApproximateQuery Processing (0)
- Improving AlphaZero Using Monte-Carlo Graph Search (0)
- CryptoSPN: Expanding PPML beyond Neural Networks (Contributed Talk) (0)
- Global Weisfeiler-Lehman Kernels (0)
- Language Models have a Moral Dimension (0)
- Boosting Statistical Relational Learning in Action (0)
- Machine Learning and Knowledge Discovery in Databases (0)
- Transfer Learning Across Relational and Uncertain Domains : A Language-Bias Approach (0)
- Approximate Counting for Fast Inference and Learning in Probabilistic Programming (0)
- Convex NMF on Non-Convex Massiv Data (0)
- On the Trade-Off Between Iterative Classification and Collective Classification : First Experimental Results (0)
- Lifted Conditioning for Pairwise Marginals and Beyond (0)
- Activity Context-Aware System Architectures (0)
- Invited Talk: Increasing Representational Power and Scaling Inference in Reinforcement Learning (0)
- Statistical Relational Artificial Intelligence, Papers from the 2010 AAAI Workshop, Atlanta, Georgia, USA, July 12, 2010 (0)
- Declarative Learning-Based Programming as an Interface to AI Systems (0)
- Distributed Relational State Representations for Complex Stochastic Processes (Extended Abstract) (0)
- Meta-Learning Runge-Kutta (0)
- CryptoSPN: Expanding PPML beyond Neural Networks (0)
- Modelling Multivariate Ranking Functions with Min-Sum Networks (0)
- Estimating the Importance of Relational Features by Using Gradient Boosting (0)
- Recurrent Rational Networks (0)
- Boosting Undirected Relational Models (0)
- Rethinking Computer Science Through AI (0)
- Propagation Kernels (0)
- The AAAI-13 Conference Workshops (0)
- Chapter 10 Probabilistic Inductive Querying Using ProbLog (0)
- RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting (0)
- Synonyms Relational Dynamic Programming , Dynamic Programming for Relational Domains , Relational Value Iteration Definition (0)
- Sum-Product Networks for Hybrid Domains (0)
- Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation (0)
- Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention (0)
- Statistical Relational Learning ( 1 ) : Introduction to Probabilistic Inductive Logic Programming ( PILP ) (0)
- Proceedings, Part II, of the European Conference on Machine Learning and Knowledge Discovery in Databases - Volume 8189 (0)
- Semantic Mapping (0)
- Alfie: An Interactive Robot with Moral Compass (0)
- Generative Adversarial Neural Cellular Automata (0)
- Making AI Smarter (0)
- Random Sum-Product Forests with Residual Links (0)
- On Hybrid and Systems AI (0)
- Intriguing Parameters of Structural Causal Models (0)
- Tools for Finding Inconsistencies in Real-world Logic-based Systems (0)
- DeepVizdom: Deep Interactive Data Exploration (0)
- Editorial: Statistical Relational Artificial Intelligence (0)
- O Scientist, Where Art Thou? Affiliation Propagation for Geo-Referencing Scientific Publications (0)
- cient Graph Kernels by Randomization (0)
- Gaussian Lifted Marginal Filtering (0)
- PADÉ ACTIVATION UNITS: END-TO-END LEARNING (0)
- Lifted Parameter Learning in Relational Models (0)
- Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models (0)
- DeepNotebooks: Deep Probabilistic Models Construct Python Notebooks for Reporting Datasets (0)
- Relational Gaussian Processes for Learning Preference Relations (0)
- Guest editor’s introduction: special issue of the ECML PKDD 2013 journal track (0)
- Gaussian Process (0)
- Learning the functional connectivity in neuronal cultures (0)
- Coresets for Dependency Networks (0)
- SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091) (0)
- Algebraic Equivalence of Linear Structural Equation Models (0)
- BAYESIAN LEARNING OF LOGICAL HIDDEN (0)
- Monte-Carlo Graph Search for AlphaZero (0)

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