Kersting, Kristian

180 publications

ICCV 2025 ART: Adaptive Relation Tuning for Generalized Relation Prediction Gopika Sudhakaran, Hikaru Shindo, Patrick Schramowski, Simone Schaub-Meyer, Kristian Kersting, Stefan Roth

ICLR 2025 BlendRL: A Framework for Merging Symbolic and Neural Policy Learning Hikaru Shindo, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting

ICML 2025 Bongard in Wonderland: Visual Puzzles That Still Make AI Go Mad? Antonia Wüst, Tim Tobiasch, Lukas Helff, Inga Ibs, Wolfgang Stammer, Devendra Singh Dhami, Constantin A. Rothkopf, Kristian Kersting

AISTATS 2025 Credibility-Aware Multimodal Fusion Using Probabilistic Circuits Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan

NeurIPS 2025 EmoNet-Face: An Expert-Annotated Benchmark for Synthetic Emotion Recognition Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby, Maurice Kraus, Felix Friedrich, Huu Nguyen, Krishna Kalyan, Kourosh Nadi, Kristian Kersting, Sören Auer

NeurIPS 2025 Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data Harsh Poonia, Felix Divo, Kristian Kersting, Devendra Singh Dhami

ICLRW 2025 Federated Circuits: A Unified Framework for Scalable and Efficient Federated Learning Jonas Seng, Florian Peter Busch, Pooja Prasad, Devendra Singh Dhami, Martin Mundt, Kristian Kersting

TMLR 2025 Forecasting Company Fundamentals Felix Divo, Eric Endress, Kevin Endler, Kristian Kersting, Devendra Singh Dhami

AAAI 2025 Human-in-the-Loop or AI-in-the-Loop? Automate or Collaborate? Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting

AutoML 2025 Hyperparameter Optimization via Interacting with Probabilistic Circuits Jonas Seng, Fabrizio Ventola, Zhongjie Yu, Kristian Kersting

ICLRW 2025 LLMs Lost in Translation: M-Alert Uncovers Cross-Linguistic Safety Gaps Felix Friedrich, Simone Tedeschi, Patrick Schramowski, Manuel Brack, Roberto Navigli, Huu Nguyen, Bo Li, Kristian Kersting

ICML 2025 LlavaGuard: An Open VLM-Based Framework for Safeguarding Vision Datasets and Models Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick Schramowski

NeurIPS 2025 Measuring and Guiding Monosemanticity Ruben Härle, Felix Friedrich, Manuel Brack, Björn Deiseroth, Stephan Waeldchen, Patrick Schramowski, Kristian Kersting

NeurIPS 2025 Object-Centric Concept-Bottlenecks David Steinmann, Wolfgang Stammer, Antonia Wüst, Kristian Kersting

ICLR 2025 ObscuraCoder: Powering Efficient Code LM Pre-Training via Obfuscation Grounding Indraneil Paul, Haoyi Yang, Goran Glavaš, Kristian Kersting, Iryna Gurevych

ECML-PKDD 2025 Right on Time: Revising Time Series Models by Constraining Their Explanations Maurice Kraus, David Steinmann, Antonia Wüst, Andre Kokozinski, Kristian Kersting

UAI 2025 Scaling Probabilistic Circuits via Data Partitioning Jonas Seng, Florian Peter Busch, Pooja Prasad, Devendra Singh Dhami, Martin Mundt, Kristian Kersting

TMLR 2025 Structural Causal Circuits: Probabilistic Circuits Climbing All Rungs of Pearl's Ladder of Causation Florian Peter Busch, Moritz Willig, Matej Zečević, Kristian Kersting, Devendra Singh Dhami

ICLR 2025 Systems with Switching Causal Relations: A Meta-Causal Perspective Moritz Willig, Tim Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, Kristian Kersting

TMLR 2025 Tractable Representation Learning with Probabilistic Circuits Steven Braun, Sahil Sidheekh, Antonio Vergari, Martin Mundt, Sriraam Natarajan, Kristian Kersting

DMLR 2025 V-LoL: A Diagnostic Dataset for Visual Logical Learning Lukas Helff, Wolfgang Stammer, Hikaru Shindo, Devendra Singh Dhami, Kristian Kersting

NeurIPS 2025 When Causal Dynamics Matter: Adapting Causal Strategies Through Meta-Aware Interventions Moritz Willig, Tim Tobiasch, Devendra Singh Dhami, Kristian Kersting

ICML 2025 Where Is the Truth? the Risk of Getting Confounded in a Continual World Florian Peter Busch, Roshni Ramanna Kamath, Rupert Mitchell, Wolfgang Stammer, Kristian Kersting, Martin Mundt

NeurIPS 2025 xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories Maurice Kraus, Felix Divo, Devendra Singh Dhami, Kristian Kersting

PGM 2024 $Ψ$net: Efficient Causal Modeling at Scale Florian Peter Busch, Moritz Willig, Jonas Seng, Kristian Kersting, Devendra Singh Dhami

UAI 2024 $χ$SPN: Characteristic Interventional Sum-Product Networks for Causal Inference in Hybrid Domains Harsh Poonia, Moritz Willig, Zhongjie Yu, Matej Ze\vcević, Kristian Kersting, Devendra Singh Dhami

ICLR 2024 Adaptive Rational Activations to Boost Deep Reinforcement Learning Quentin Delfosse, Patrick Schramowski, Martin Mundt, Alejandro Molina, Kristian Kersting

ICLR 2024 Be Careful What You Smooth for: Label Smoothing Can Be a Privacy Shield but Also a Catalyst for Model Inversion Attacks Lukas Struppek, Dominik Hintersdorf, Kristian Kersting

AutoML 2024 Bi-Level One-Shot Architecture Search for Probabilistic Time Series Forecasting Jonas Seng, Fabian Kalter, Zhongjie Yu, Fabrizio Ventola, Kristian Kersting

ICLRW 2024 Biased Causal Strength Judgments in Humans and Large Language Models Anita Keshmirian, Moritz Willig, Babak Hemmatian, Ulrike Hahn, Kristian Kersting, Tobias Gerstenberg

NeurIPSW 2024 Bongard in Wonderland: Visual Puzzles That Still Make AI Go Mad? Antonia Wüst, Tim Tobiasch, Lukas Helff, Devendra Singh Dhami, Constantin A. Rothkopf, Kristian Kersting

NeurIPSW 2024 Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting

AISTATS 2024 Deep Classifier Mimicry Without Data Access Steven Braun, Martin Mundt, Kristian Kersting

NeurIPS 2024 DeiSAM: Segment Anything with Deictic Prompting Hikaru Shindo, Manuel Brack, Gopika Sudhakaran, Devendra Singh Dhami, Patrick Schramowski, Kristian Kersting

JAIR 2024 Does CLIP Know My Face? Dominik Hintersdorf, Lukas Struppek, Manuel Brack, Felix Friedrich, Patrick Schramowski, Kristian Kersting

ICLRW 2024 Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting

IJCAI 2024 Exploiting Cultural Biases via Homoglyphs inText-to-Image Synthesis (Abstract Reprint) Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting

ICLRW 2024 Exploring the Adversarial Capabilities of Large Language Models Lukas Struppek, Minh Hieu Le, Dominik Hintersdorf, Kristian Kersting

NeurIPS 2024 Finding NeMo: Localizing Neurons Responsible for Memorization in Diffusion Models Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, Franziska Boenisch

ICMLW 2024 Finding NeMo: Localizing Neurons Responsible for Memorization in Diffusion Models Lukas Struppek, Dominik Hintersdorf, Kristian Kersting, Adam Dziedzic, Franziska Boenisch

NeurIPS 2024 Graph Neural Networks Need Cluster-Normalize-Activate Modules Arseny Skryagin, Felix Divo, Mohammad Amin Ali, Devendra Singh Dhami, Kristian Kersting

NeurIPS 2024 Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, Wolfgang Stammer, Kristian Kersting

NeurIPSW 2024 Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents Quentin Delfosse, Sebastian Sztwiertnia, Mark Rothermel, Wolfgang Stammer, Kristian Kersting

CVPR 2024 LEDITS++: Limitless Image Editing Using Text-to-Image Models Manuel Brack, Felix Friedrich, Katharia Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinario Passos

NeurIPSW 2024 LLAVAGUARD: VLM-Based Safeguards for Vision Dataset Curation and Safety Assessment Lukas Helff, Felix Friedrich, Manuel Brack, Kristian Kersting, Patrick Schramowski

MLJ 2024 Learning Differentiable Logic Programs for Abstract Visual Reasoning Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, Kristian Kersting

ICLR 2024 Learning Large DAGs Is Harder than You Think: Many Losses Are Minimal for the Wrong DAG Jonas Seng, Matej Zečević, Devendra Singh Dhami, Kristian Kersting

TMLR 2024 Learning by Self-Explaining Wolfgang Stammer, Felix Friedrich, David Steinmann, Manuel Brack, Hikaru Shindo, Kristian Kersting

ICML 2024 Learning to Intervene on Concept Bottlenecks David Steinmann, Wolfgang Stammer, Felix Friedrich, Kristian Kersting

CoLLAs 2024 Masked Autoencoders Are Efficient Continual Federated Learners Subarnaduti Paul, Lars-Joel Frey, Roshni Ramanna Kamath, Kristian Kersting, Martin Mundt

ICML 2024 Mechanistic Design and Scaling of Hybrid Architectures Michael Poli, Armin W Thomas, Eric Nguyen, Pragaash Ponnusamy, Björn Deiseroth, Kristian Kersting, Taiji Suzuki, Brian Hie, Stefano Ermon, Christopher Re, Ce Zhang, Stefano Massaroli

NeurIPS 2024 Neural Concept Binder Wolfgang Stammer, Antonia Wüst, David Steinmann, Kristian Kersting

UAI 2024 Pix2Code: Learning to Compose Neural Visual Concepts as Programs Antonia Wüst, Wolfgang Stammer, Quentin Delfosse, Devendra Singh Dhami, Kristian Kersting

NeurIPSW 2024 Right on Time: Revising Time Series Models by Constraining Their Explanations Maurice Kraus, David Steinmann, Antonia Wüst, Andre Kokozinski, Kristian Kersting

NeurIPSW 2024 SCAR: Sparse Conditioned Autoencoders for Concept Detection and Steering in LLMs Ruben Härle, Felix Friedrich, Manuel Brack, Björn Deiseroth, Patrick Schramowski, Kristian Kersting

MLJ 2024 Structural Causal Models Reveal Confounder Bias in Linear Program Modelling Matej Zecevic, Devendra Singh Dhami, Kristian Kersting

NeurIPSW 2024 Systems with Switching Causal Relations: A Meta-Causal Perspective Moritz Willig, Tim Tobiasch, Florian Peter Busch, Jonas Seng, Devendra Singh Dhami, Kristian Kersting

NeurIPS 2023 ATMAN: Understanding Transformer Predictions Through Memory Efficient Attention Manipulation Björn Deiseroth, Mayukh Deb, Samuel Weinbach, Manuel Brack, Patrick Schramowski, Kristian Kersting

ECML-PKDD 2023 Boosting Object Representation Learning via Motion and Object Continuity Quentin Delfosse, Wolfgang Stammer, Thomas Rothenbacher, Dwarak Vittal, Kristian Kersting

TMLR 2023 Causal Parrots: Large Language Models May Talk Causality but Are Not Causal Matej Zečević, Moritz Willig, Devendra Singh Dhami, Kristian Kersting

NeurIPS 2023 Characteristic Circuits Zhongjie Yu, Martin Trapp, Kristian Kersting

NeurIPSW 2023 Defending Our Privacy with Backdoors Dominik Hintersdorf, Lukas Struppek, Daniel Neider, Kristian Kersting

NeurIPS 2023 Do Not Marginalize Mechanisms, Rather Consolidate! Moritz Willig, Matej Zečević, Devendra Dhami, Kristian Kersting

JAIR 2023 Exploiting Cultural Biases via Homoglyphs in Text-to-Image Synthesis Lukas Struppek, Dominik Hintersdorf, Felix Friedrich, Manuel Brack, Patrick Schramowski, Kristian Kersting

ICML 2023 ILLUME: Rationalizing Vision-Language Models Through Human Interactions Manuel Brack, Patrick Schramowski, Björn Deiseroth, Kristian Kersting

NeurIPS 2023 Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction Quentin Delfosse, Hikaru Shindo, Devendra Dhami, Kristian Kersting

NeurIPSW 2023 Leveraging Diffusion-Based Image Variations for Robust Training on Poisoned Data Lukas Struppek, Martin Hentschel, Clifton Poth, Dominik Hintersdorf, Kristian Kersting

ICMLW 2023 Mitigating Inappropriateness in Image Generation: Can There Be Value in Reflecting the Worlds Ugliness? Manuel Brack, Felix Friedrich, Patrick Schramowski, Kristian Kersting

NeurIPS 2023 MultiFusion: Fusing Pre-Trained Models for Multi-Lingual, Multi-Modal Image Generation Marco Bellagente, Manuel Brack, Hannah Teufel, Felix Friedrich, Björn Deiseroth, Constantin Eichenberg, Andrew M Dai, Robert Baldock, Souradeep Nanda, Koen Oostermeijer, Andres Felipe Cruz-Salinas, Patrick Schramowski, Kristian Kersting, Samuel Weinbach

TMLR 2023 Not All Causal Inference Is the Same Matej Zečević, Devendra Singh Dhami, Kristian Kersting

UAI 2023 Probabilistic Circuits That Know What They Don’t Know Fabrizio Ventola, Steven Braun, Zhongjie Yu, Martin Mundt, Kristian Kersting

UAI 2023 Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference Sahil Sidheekh, Kristian Kersting, Sriraam Natarajan

ICCV 2023 Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image Synthesis Lukas Struppek, Dominik Hintersdorf, Kristian Kersting

NeurIPS 2023 SEGA: Instructing Text-to-Image Models Using Semantic Guidance Manuel Brack, Felix Friedrich, Dominik Hintersdorf, Lukas Struppek, Patrick Schramowski, Kristian Kersting

CVPR 2023 Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models Patrick Schramowski, Manuel Brack, Björn Deiseroth, Kristian Kersting

JAIR 2023 Scalable Neural-Probabilistic Answer Set Programming Arseny Skryagin, Daniel Ochs, Devendra Singh Dhami, Kristian Kersting

ICCV 2023 Vision Relation Transformer for Unbiased Scene Graph Generation Gopika Sudhakaran, Devendra Singh Dhami, Kristian Kersting, Stefan Roth

MLJ 2023 αILP: Thinking Visual Scenes as Differentiable Logic Programs Hikaru Shindo, Viktor Pfanschilling, Devendra Singh Dhami, Kristian Kersting

ICLR 2022 CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability Martin Mundt, Steven Lang, Quentin Delfosse, Kristian Kersting

PGM 2022 Explaining Deep Tractable Probabilistic Models: The Sum-Product Network Case Bhagirath Athresh Karanam, Saurabh Mathur, Predrag Radivojac, David M Haas, Kristian Kersting, Sriraam Natarajan

ICLRW 2022 Finding Structure and Causality in Linear Programs Matej Zečević, Florian Peter Busch, Devendra Singh Dhami, Kristian Kersting

CVPR 2022 Interactive Disentanglement: Learning Concepts by Interacting with Their Prototype Representations Wolfgang Stammer, Marius Memmel, Patrick Schramowski, Kristian Kersting

IJCAI 2022 Neuro-Symbolic Verification of Deep Neural Networks Xuan Xie, Kristian Kersting, Daniel Neider

ICML 2022 Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks Lukas Struppek, Dominik Hintersdorf, Antonio De Almeida Correira, Antonia Adler, Kristian Kersting

UAI 2022 Predictive Whittle Networks for Time Series Zhongjie Yu, Fabrizio Ventola, Nils Thoma, Devendra Singh Dhami, Martin Mundt, Kristian Kersting

IJCAI 2022 To Trust or Not to Trust Prediction Scores for Membership Inference Attacks Dominik Hintersdorf, Lukas Struppek, Kristian Kersting

NeurIPS 2021 Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models Matej Zečević, Devendra Dhami, Athresh Karanam, Sriraam Natarajan, Kristian Kersting

UAI 2021 Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression Zhongjie Yu, Mingye Zhu, Martin Trapp, Arseny Skryagin, Kristian Kersting

AAAI 2021 Right for Better Reasons: Training Differentiable Models by Constraining Their Influence Functions Xiaoting Shao, Arseny Skryagin, Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

CVPR 2021 Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with Their Explanations Wolfgang Stammer, Patrick Schramowski, Kristian Kersting

ICML 2021 Whittle Networks: A Deep Likelihood Model for Time Series Zhongjie Yu, Fabrizio G Ventola, Kristian Kersting

PGM 2020 Discriminative Non-Parametric Learning of Arithmetic Circuits Nandini Ramanan, Mayukh Das, Kristian Kersting, Sriraam Natarajan

PGM 2020 Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting

ICML 2020 Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van Den Broeck, Kristian Kersting, Zoubin Ghahramani

ECML-PKDD 2020 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part I Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera

ECML-PKDD 2020 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part II Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera

ECML-PKDD 2020 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part III Frank Hutter, Kristian Kersting, Jefrey Lijffijt, Isabel Valera

ICLR 2020 Padé Activation Units: End-to-End Learning of Flexible Activation Functions in Deep Networks Alejandro Molina, Patrick Schramowski, Kristian Kersting

PGM 2020 Residual Sum-Product Networks Fabrizio Ventola, Karl Stelzner, Alejandro Molina, Kristian Kersting

ICLR 2020 Structured Object-Aware Physics Prediction for Video Modeling and Planning Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, Kristian Kersting

AAAI 2019 Automatic Bayesian Density Analysis Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera

AAAI 2019 Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan

ICML 2019 Faster Attend-Infer-Repeat with Tractable Probabilistic Models Karl Stelzner, Robert Peharz, Kristian Kersting

UAI 2019 Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Xiaoting Shao, Martin Trapp, Kristian Kersting, Zoubin Ghahramani

AAAI 2018 Core Dependency Networks Alejandro Molina, Alexander Munteanu, Kristian Kersting

IJCAI 2018 Efficient Symbolic Integration for Probabilistic Inference Samuel Kolb, Martin Mladenov, Scott Sanner, Vaishak Belle, Kristian Kersting

IJCAI 2018 Lifted Filtering via Exchangeable Decomposition Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste

AAAI 2018 Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting

AAAI 2018 Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito

IJCAI 2018 Systems AI: A Declarative Learning Based Programming Perspective Parisa Kordjamshidi, Dan Roth, Kristian Kersting

ECML-PKDD 2017 Graph Enhanced Memory Networks for Sentiment Analysis Zhao Xu, Romain Vial, Kristian Kersting

AAAI 2017 Lifted Inference for Convex Quadratic Programs Martin Mladenov, Leonard Kleinhans, Kristian Kersting

AAAI 2017 Poisson Sum-Product Networks: A Deep Architecture for Tractable Multivariate Poisson Distributions Alejandro Molina, Sriraam Natarajan, Kristian Kersting

UAI 2017 Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI 2017, Sydney, Australia, August 11-15, 2017 Gal Elidan, Kristian Kersting, Alexander Ihler

IJCAI 2017 Stochastic Online Anomaly Analysis for Streaming Time Series Zhao Xu, Kristian Kersting, Lorenzo von Ritter

AAAI 2017 The Symbolic Interior Point Method Martin Mladenov, Vaishak Belle, Kristian Kersting

AAAI 2016 Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach Shuo Yang, Tushar Khot, Kristian Kersting, Sriraam Natarajan

IJCAI 2016 Learning Using Unselected Features (LUFe) Joseph G. Taylor, Viktoriia Sharmanska, Kristian Kersting, David Weir, Novi Quadrianto

MLJ 2016 Propagation Kernels: Efficient Graph Kernels from Propagated Information Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting

IJCAI 2015 Computer Science on the Move: Inferring Migration Regularities from the Web via Compressed Label Propagation Fabian Hadiji, Martin Mladenov, Christian Bauckhage, Kristian Kersting

UAI 2015 Equitable Partitions of Concave Free Energies Martin Mladenov, Kristian Kersting

MLJ 2015 Gradient-Based Boosting for Statistical Relational Learning: The Markov Logic Network and Missing Data Cases Tushar Khot, Sriraam Natarajan, Kristian Kersting, Jude W. Shavlik

UAI 2015 Parameterizing the Distance Distribution of Undirected Networks Christian Bauckhage, Kristian Kersting, Fabian Hadiji

MLJ 2015 Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data Fabian Hadiji, Alejandro Molina, Sriraam Natarajan, Kristian Kersting

MLOSS 2015 pyGPs -- a Python Library for Gaussian Process Regression and Classification Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting

AISTATS 2014 Efficient Lifting of MAP LP Relaxations Using K-Locality Martin Mladenov, Kristian Kersting, Amir Globerson

UAI 2014 Lifted Message Passing as Reparametrization of Graphical Models Martin Mladenov, Amir Globerson, Kristian Kersting

AAAI 2014 Lifting Relational MAP-LPs Using Cluster Signatures Udi Apsel, Kristian Kersting, Martin Mladenov

NeurIPS 2014 Mind the Nuisance: Gaussian Process Classification Using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto

AAAI 2014 Power Iterated Color Refinement Kristian Kersting, Martin Mladenov, Roman Garnett, Martin Grohe

ACML 2013 Coinciding Walk Kernels: Parallel Absorbing Random Walks for Learning with Graphs and Few Labels Marion Neumann, Roman Garnett, Kristian Kersting

MLJ 2013 Exploiting Symmetries for Scaling Loopy Belief Propagation and Relational Training Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan

MLJ 2013 Guest Editor's Introduction: Special Issue of the ECML PKDD 2013 Journal Track Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný

ECML-PKDD 2013 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný

ECML-PKDD 2013 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný

ECML-PKDD 2013 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný

AAAI 2013 Reduce and Re-Lift: Bootstrapped Lifted Likelihood Maximization for MAP Fabian Hadiji, Kristian Kersting

ECML-PKDD 2012 Efficient Graph Kernels by Randomization Marion Neumann, Novi Patricia, Roman Garnett, Kristian Kersting

JMLR 2012 Exploration in Relational Domains for Model-Based Reinforcement Learning Tobias Lang, Marc Toussaint, Kristian Kersting

MLJ 2012 Gradient-Based Boosting for Statistical Relational Learning: The Relational Dependency Network Case Sriraam Natarajan, Tushar Khot, Kristian Kersting, Bernd Gutmann, Jude W. Shavlik

UAI 2012 Latent Dirichlet Allocation Uncovers Spectral Characteristics of Drought Stressed Plants Mirwaes Wahabzada, Kristian Kersting, Christian Bauckhage, Christoph Römer, Agim Ballvora, Francisco Pinto, Uwe Rascher, Jens Leon, Lutz Ploemer

AISTATS 2012 Lifted Linear Programming Martin Mladenov, Babak Ahmadi, Kristian Kersting

ECML-PKDD 2012 Lifted Online Training of Relational Models with Stochastic Gradient Methods Babak Ahmadi, Kristian Kersting, Sriraam Natarajan

AISTATS 2012 Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data Martin Schiegg, Marion Neumann, Kristian Kersting

ECML-PKDD 2012 Matrix Factorization as Search Kristian Kersting, Christian Bauckhage, Christian Thurau, Mirwaes Wahabzada

AAAI 2012 Pre-Symptomatic Prediction of Plant Drought Stress Using Dirichlet-Aggregation Regression on Hyperspectral Images Kristian Kersting, Zhao Xu, Mirwaes Wahabzada, Christian Bauckhage, Christian Thurau, Christoph Römer, Agim Ballvora, Uwe Rascher, Jens Leon, Lutz Plümer

NeurIPS 2012 Symbolic Dynamic Programming for Continuous State and Observation POMDPs Zahra Zamani, Scott Sanner, Pascal Poupart, Kristian Kersting

MLJ 2011 Guest Editorial to the Special Issue on Inductive Logic Programming, Mining and Learning in Graphs and Statistical Relational Learning Hendrik Blockeel, Karsten M. Borgwardt, Luc De Raedt, Pedro M. Domingos, Kristian Kersting, Xifeng Yan

IJCAI 2011 Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach Sriraam Natarajan, Saket Joshi, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

ECML-PKDD 2011 Larger Residuals, Less Work: Active Document Scheduling for Latent Dirichlet Allocation Mirwaes Wahabzada, Kristian Kersting

AAAI 2011 Markov Logic Sets: Towards Lifted Information Retrieval Using PageRank and Label Propagation Marion Neumann, Babak Ahmadi, Kristian Kersting

IJCAI 2011 Multi-Evidence Lifted Message Passing, with Application to PageRank and the Kalman Filter Babak Ahmadi, Kristian Kersting, Scott Sanner

ECML-PKDD 2010 Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models Sriraam Natarajan, Tushar Khot, Daniel Lowd, Prasad Tadepalli, Kristian Kersting, Jude W. Shavlik

ECML-PKDD 2010 Exploration in Relational Worlds Tobias Lang, Marc Toussaint, Kristian Kersting

ECML-PKDD 2010 Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes Zhao Xu, Kristian Kersting, Thorsten Joachims

ACML 2010 Hierarchical Convex NMF for Clustering Massive Data Kristian Kersting, Mirwaes Wahabzada, Christian Thurau, Christian Bauckhage

AAAI 2010 Informed Lifting for Message-Passing Kristian Kersting, Youssef El Massaoudi, Fabian Hadiji, Babak Ahmadi

AAAI 2010 Symbolic Dynamic Programming for First-Order POMDPs Scott Sanner, Kristian Kersting

ECML-PKDD 2010 Topic Models Conditioned on Relations Mirwaes Wahabzada, Zhao Xu, Kristian Kersting

UAI 2009 Counting Belief Propagation Kristian Kersting, Babak Ahmadi, Sriraam Natarajan

IJCAI 2009 Generalized First Order Decision Diagrams for First Order Markov Decision Processes Saket Joshi, Kristian Kersting, Roni Khardon

ECML-PKDD 2009 Learning Preferences with Hidden Common Cause Relations Kristian Kersting, Zhao Xu

IJCAI 2009 Multi-Relational Learning with Gaussian Processes Zhao Xu, Kristian Kersting, Volker Tresp

MLJ 2008 Compressing Probabilistic Prolog Programs Luc De Raedt, Kristian Kersting, Angelika Kimmig, Kate Revoredo, Hannu Toivonen

AAAI 2008 Lifted Probabilistic Inference with Counting Formulas Brian Milch, Luke S. Zettlemoyer, Kristian Kersting, Michael Haimes, Leslie Pack Kaelbling

ICML 2008 Non-Parametric Policy Gradients: A Unified Treatment of Propositional and Relational Domains Kristian Kersting, Kurt Driessens

ECML-PKDD 2008 Nonstationary Gaussian Process Regression Using Point Estimates of Local Smoothness Christian Plagemann, Kristian Kersting, Wolfram Burgard

ECML-PKDD 2008 Parameter Learning in Probabilistic Databases: A Least Squares Approach Bernd Gutmann, Angelika Kimmig, Kristian Kersting, Luc De Raedt

JMLR 2007 Integrating Naïve Bayes and FOIL Niels Landwehr, Kristian Kersting, Luc De Raedt

ICML 2007 Most Likely Heteroscedastic Gaussian Process Regression Kristian Kersting, Christian Plagemann, Patrick Pfaff, Wolfram Burgard

ECML-PKDD 2006 Fisher Kernels for Relational Data Uwe Dick, Kristian Kersting

JAIR 2006 Logical Hidden Markov Models Kristian Kersting, Luc De Raedt, Tapani Raiko

ECML-PKDD 2006 TildeCRF: Conditional Random Fields for Logical Sequences Bernd Gutmann, Kristian Kersting

UAI 2005 "Say EM" for Selecting Probabilistic Models for Logical Sequences Kristian Kersting, Tapani Raiko

AAAI 2005 Towards Learning Stochastic Logic Programs from Proof-Banks Luc De Raedt, Kristian Kersting, Sunna Torge

AAAI 2005 nFOIL: Integrating Naïve Bayes and FOIL Niels Landwehr, Kristian Kersting, Luc De Raedt

ICML 2004 Bellman Goes Relational Kristian Kersting, Martijn van Otterlo, Luc De Raedt

ECML-PKDD 2004 Fisher Kernels for Logical Sequences Kristian Kersting, Thomas Gärtner

ALT 2004 Probabilistic Inductive Logic Programming Luc De Raedt, Kristian Kersting

ECML-PKDD 2003 Scaled CGEM: A Fast Accelerated EM Jörg Fischer, Kristian Kersting