Multimodale Registrierung mit effizienten Lernverfahren für neuronale Netze. - Torsten Rohlfing; Jürgen Beier; Oliver Schulte; Norbert Hosten; Roland Felix (Bildverarbeitung für die Medizin, 1998)

Minimal Belief Change and Pareto-Optimality. - Oliver Schulte (Australian Joint Conference on Artificial Intelligence, 1999)

The Logic Of Reliable And Efficient Inquiry. - Oliver Schulte (J. Philos. Log., 1999)

Minimal Belief Change and the Pareto Principle. - Oliver Schulte (Synth., 1999)

Knowledge and Planning in an Action-Based Multi-agent Framework: A Case Study. - Bradley Bart; James P. Delgrande; Oliver Schulte (AI, 2001)

Iterated backward inference: an algorithm for proper rationalizability. - Oliver Schulte (TARK, 2003)

Representing von Neumann-Morgenstern Games in the Situation Calculus. - Oliver Schulte; James P. Delgrande (Ann. Math. Artif. Intell., 2004)

Mind change efficient learning. - Wei Luo 001; Oliver Schulte (Inf. Comput., 2006)

Mind Change Optimal Learning of Bayes Net Structure. - Oliver Schulte; Wei Luo 001; Russell Greiner (COLT, 2007)

The minimum consistent subset cover problem and its applications in data mining. - Byron J. Gao; Martin Ester; Jin-yi Cai; Oliver Schulte; Hui Xiong 001 (KDD, 2007)

Association Rules in the Relational Calculus - Oliver Schulte; Flavia Moser; Martin Ester; Zhiyong Lu (CoRR, 2007)

Logically Reliable Inductive Inference. - Oliver Schulte (Induction, Algorithmic Learning Theory, and Philosophy, 2007)

Join Bayes Nets: A new type of Bayes net for relational data - Oliver Schulte; Hassan Khosravi; Flavia Moser; Martin Ester (CoRR, 2008)

A new hybrid method for Bayesian network learning With dependency constraints. - Oliver Schulte; Gustavo Frigo; Russell Greiner; Wei Luo 001; Hassan Khosravi (CIDM, 2009)

LNBC: A Link-Based Naive Bayes Classifier. - Bahareh Bina; Oliver Schulte; Hassan Khosravi (ICDM Workshops, 2009)

Simultaneous Discovery of Conservation Laws and Hidden Particles with Smith Matrix Decomposition. - Oliver Schulte (IJCAI, 2009)

Structure Learning for Markov Logic Networks with Many Descriptive Attributes. - Hassan Khosravi; Oliver Schulte; Tong Man; Xiaoyuan Xu; Bahareh Bina (AAAI, 2010)

The IMAP Hybrid Method for Learning Gaussian Bayes Nets. - Oliver Schulte; Gustavo Frigo; Russell Greiner; Hassan Khosravi (Canadian AI, 2010)

Discovery of Conservation Laws via Matrix Search. - Oliver Schulte; Mark S. Drew (Discovery Science, 2010)

Mind change optimal learning of Bayes net structure from dependency and independency data. - Oliver Schulte; Wei Luo 001; Russell Greiner (Inf. Comput., 2010)

Evolutionary equilibrium in Bayesian routing games: Specialization and niche formation. - Petra Berenbrink; Oliver Schulte (Theor. Comput. Sci., 2010)

Deidentification within Unstructured Medical Records. - Verónica Dahl; Sara Saghaei; Oliver Schulte (Biology, Computation and Linguistics, 2011)

A Tractable Pseudo-Likelihood Function for Bayes Nets Applied to Relational Data. - Oliver Schulte (SDM, 2011)

Random Regression for Bayes Nets Applied to Relational Data. - Oliver Schulte; Hassan Khosravi; Tianxiang Gao; Yuke Zhu (StarAI@UAI, 2012)

Learning compact Markov logic networks with decision trees. - Hassan Khosravi; Oliver Schulte; Jianfeng Hu; Tianxiang Gao (Mach. Learn., 2012)

Learning graphical models for relational data via lattice search. - Oliver Schulte; Hassan Khosravi (Mach. Learn., 2012)

Learning directed relational models with recursive dependencies. - Oliver Schulte; Hassan Khosravi; Tong Man (Mach. Learn., 2012)

Identifying Important Nodes in Heterogenous Networks. - Oliver Schulte; Fatemeh Riahi; Qing Li (AAAI (Late-Breaking Developments), 2013)

Transaction-based link strength prediction in a social network. - Hassan Khosravi; Ali Bozorgkhan; Oliver Schulte (CIDM, 2013)

A hierarchy of independence assumptions for multi-relational Bayes net classifiers. - Oliver Schulte; Bahareh Bina; Branden Crawford; D. Bingham; Yi Xiong (CIDM, 2013)

Learning Bayes Nets for Relational Data with Link Uncertainty. - Zhensong Qian; Oliver Schulte (GKR, 2013)

Simple decision forests for multi-relational classification. - Bahareh Bina; Oliver Schulte; Branden Crawford; Zhensong Qian; Yi Xiong (Decis. Support Syst., 2013)

The Minimum Consistent Subset Cover Problem: A Minimization View of Data Mining. - Byron J. Gao; Martin Ester; Hui Xiong 001; Jin-Yi Cai; Oliver Schulte (IEEE Trans. Knowl. Data Eng., 2013)

A Proposal for Statistical Outlier Detection in Relational Structures. - Fatemeh Riahi; Oliver Schulte; Qing Li (StarAI@AAAI, 2014)

Aggregating predictions vs. aggregating features for relational classification. - Oliver Schulte; Kurt Routley (CIDM, 2014)

Computing Multi-Relational Sufficient Statistics for Large Databases. - Zhensong Qian; Oliver Schulte; Yan Sun (CIKM, 2014)

Modelling relational statistics with Bayes Nets. - Oliver Schulte; Hassan Khosravi; Arthur E. Kirkpatrick; Tianxiang Gao; Yuke Zhu (Mach. Learn., 2014)

FactorBase : Multi-relational model learning with SQL all the way. - Zhensong Qian; Oliver Schulte (DSAA, 2015)

What is the Value of an Action in Ice Hockey? Q-Learning for the NHL. - Oliver Schulte; Zeyu Zhao 001; Kurt Routley (MLSA@PKDD/ECML, 2015)

Model-Based Outlier Detection for Object-Relational Data. - Fatemeh Riahi; Oliver Schulte (SSCI, 2015)

A Markov Game Model for Valuing Player Actions in Ice Hockey. - Kurt Routley; Oliver Schulte (UAI, 2015)

The CTU Prague Relational Learning Repository. - Jan Motl; Oliver Schulte (CoRR, 2015)

SQL for SRL: Structure Learning Inside a Database System. - Oliver Schulte; Zhensong Qian (CoRR, 2015)

FactorBase: SQL for Learning A Multi-Relational Graphical Model. - Oliver Schulte; Zhensong Qian (CoRR, 2015)

Propositionalization for Unsupervised Outlier Detection in Multi-Relational Data. - Fatemeh Riahi; Oliver Schulte (FLAIRS, 2016)

Fast learning of relational dependency networks. - Oliver Schulte; Zhensong Qian; Arthur E. Kirkpatrick; Xiaoqian Yin; Yan Sun (Mach. Learn., 2016)

Biased penalty calls in the National Hockey League. - David Beaudoin; Oliver Schulte; Tim B. Swartz (Stat. Anal. Data Min., 2016)

Locally Consistent Bayesian Network Scores for Multi-Relational Data. - Oliver Schulte; Sajjad Gholami (IJCAI, 2017)

A Markov Game model for valuing actions, locations, and team performance in ice hockey. - Oliver Schulte; Mahmoud Khademi; Sajjad Gholami; Zeyu Zhao 001; Mehrsan Javan Roshtkhari; Philippe Desaulniers (Data Min. Knowl. Discov., 2017)

Dynamic Gated Graph Neural Networks for Scene Graph Generation. - Mahmoud Khademi; Oliver Schulte (ACCV (6), 2018)

Image Caption Generation With Hierarchical Contextual Visual Spatial Attention. - Mahmoud Khademi; Oliver Schulte (CVPR Workshops, 2018)

Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation. - Guiliang Liu; Oliver Schulte (IJCAI, 2018)

Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts. - Yejia Liu; Oliver Schulte; Chao Li 025 (MLSA@PKDD/ECML, 2018)

Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees. - Guiliang Liu; Oliver Schulte; Wang Zhu 001; Qingcan Li (ECML/PKDD (2), 2018)

Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHL. - Guiliang Liu; Wang Zhu 001; Oliver Schulte (MLSA@PKDD/ECML, 2018)

Model Trees for Identifying Exceptional Players in the NHL Draft. - Oliver Schulte; Yejia Liu; Chao Li 015 (CoRR, 2018)

Inference, Learning, and Population Size: Projectivity for SRL Models. - Manfred Jaeger; Oliver Schulte (CoRR, 2018)

Detecting Data Errors with Statistical Constraints. - Jing Nathan Yan; Oliver Schulte; Jiannan Wang 001; Reynold Cheng (CoRR, 2019)

FACTORBASE: multi-relational structure learning with SQL all the way. - Oliver Schulte; Zhensong Qian (Int. J. Data Sci. Anal., 2019)

Causal Learning with Occam's Razor. - Oliver Schulte (Stud Logica, 2019)

Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation. - Mahmoud Khademi; Oliver Schulte (AAAI, 2020)

A Complete Characterization of Projectivity for Statistical Relational Models. - Manfred Jaeger; Oliver Schulte (IJCAI, 2020)

Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players. - Yudong Luo; Oliver Schulte; Pascal Poupart (IJCAI, 2020)

Cracking the Black Box: Distilling Deep Sports Analytics. - Xiangyu Sun; Jack Davis; Oliver Schulte; Guiliang Liu (KDD, 2020)

Learning Agent Representations for Ice Hockey. - Guiliang Liu; Oliver Schulte; Pascal Poupart; Mike Rudd; Mehrsan Javan (NeurIPS, 2020)

SCODED: Statistical Constraint Oriented Data Error Detection. - Jing Nathan Yan; Oliver Schulte; Mohan Zhang; Jiannan Wang 001; Reynold Cheng (SIGMOD Conference, 2020)

Deep soccer analytics: learning an action-value function for evaluating soccer players. - Guiliang Liu; Yudong Luo; Oliver Schulte; Tarak Kharrat (Data Min. Knowl. Discov., 2020)

Model-based exception mining for object-relational data. - Fatemeh Riahi; Oliver Schulte (Data Min. Knowl. Discov., 2020)

Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. - Guiliang Liu; Xiangyu Sun; Oliver Schulte; Pascal Poupart (NeurIPS, 2021)

Leveraging Approximate Constraints for Localized Data Error Detection. - Mohan Zhang; Oliver Schulte; Yudong Luo (aiDM@SIGMOD, 2021)

Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning. - Kiarash Zahirnia; Ankita Sakhuja; Oliver Schulte; Parmis Nadaf; Ke Li 011; Xia Hu (CoRR, 2021)

NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge. - Xiangyu Sun; Guiliang Liu; Pascal Poupart; Oliver Schulte (CoRR, 2021)

Pre and Post Counting for Scalable Statistical-Relational Model Discovery. - Richard Mar; Oliver Schulte (CoRR, 2021)

Deep Learning of Latent Edge Types from Relational Data. - Kiarash Zahirnia; Oliver Schulte; Ke Li 011; Ankita Sakhuja; Parmis Naddaf (Canadian AI, 2022)

Distributional Reinforcement Learning with Monotonic Splines. - Yudong Luo; Guiliang Liu; Haonan Duan 002; Oliver Schulte; Pascal Poupart (ICLR, 2022)

Valuing Actions and Ranking Hockey Players With Machine Learning. - Oliver Schulte (LINHAC, 2022)

Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. - Guiliang Liu; Yudong Luo; Oliver Schulte; Pascal Poupart (NeurIPS, 2022)

Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders. - Kiarash Zahirnia; Oliver Schulte; Parmis Naddaf; Ke Li 011 (NeurIPS, 2022)

NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. - Xiangyu Sun; Oliver Schulte; Guiliang Liu; Pascal Poupart (AISTATS, 2023)

Joint Link Prediction Via Inference from a Model. - Parmis Naddaf; Erfaneh Mahmoudzaheh Ahmadi Nejad; Kiarash Zahirnia; Manfred Jaeger; Oliver Schulte (CIKM, 2023)

Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting. - Shayan Shirahmad Gale Bagi; Zahra Gharaee; Oliver Schulte; Mark Crowley 001 (ICML, 2023)

A Simple Latent Variable Model for Graph Learning and Inference. - Manfred Jaeger; Antonio Longa; Steve Azzolin; Oliver Schulte; Andrea Passerini (LoG, 2023)

Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing. - Xiangyu Sun; Oliver Schulte (NeurIPS, 2023)

Neural Graph Generation from Graph Statistics. - Kiarash Zahirnia; Yaochen Hu 001; Mark Coates; Oliver Schulte (NeurIPS, 2023)

From Graph Generation to Graph Classification. - Oliver Schulte (CoRR, 2023)

Computing Expected Motif Counts for Exchangeable Graph Generative Models. - Oliver Schulte (CoRR, 2023)

Deep Generative Models for Subgraph Prediction. - Erfaneh Mahmoudzadeh; Parmis Naddaf; Kiarash Zahirnia; Oliver Schulte (ECAI, 2024)

Disentanglement in Implicit Causal Models via Switch Variable. - Shayan Shirahmad Gale Bagi; Zahra Gharaee; Oliver Schulte; Mark Crowley 001 (CoRR, 2024)

Why Online Reinforcement Learning is Causal. - Oliver Schulte; Pascal Poupart (CoRR, 2024)

From Graph Diffusion to Graph Classification. - Jia Jun Cheng Xian; Sadegh Mahdavi; Renjie Liao; Oliver Schulte (CoRR, 2024)

When Should Reinforcement Learning Use Causal Reasoning? - Oliver Schulte; Pascal Poupart (Trans. Mach. Learn. Res., 2025)