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ORCIDOliver Schulte
Person information
- affiliation: Simon Fraser University, Burnaby, Canada
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2020 – today
- 2024
[c57]Erfaneh Mahmoudzadeh, Parmis Naddaf, Kiarash Zahirnia, Oliver Schulte:
Deep Generative Models for Subgraph Prediction. ECAI 2024: 3128-3136- [i27]Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley:
Disentanglement in Implicit Causal Models via Switch Variable. CoRR abs/2402.11124 (2024) - [i26]Oliver Schulte, Pascal Poupart:
Why Online Reinforcement Learning is Causal. CoRR abs/2403.04221 (2024) - [i25]Erfaneh Mahmoudzadeh, Parmis Naddaf, Kiarash Zahirnia, Oliver Schulte:
Deep Generative Models for Subgraph Prediction. CoRR abs/2408.04053 (2024) - [i24]Jia Jun Cheng Xian, Sadegh Mahdavi, Renjie Liao, Oliver Schulte:
From Graph Diffusion to Graph Classification. CoRR abs/2411.17236 (2024) - 2023
- [c56]Xiangyu Sun, Oliver Schulte, Guiliang Liu, Pascal Poupart:
NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledge. AISTATS 2023: 1942-1964 - [c55]Parmis Naddaf
, Erfaneh Mahmoudzaheh Ahmadi Nejad, Kiarash Zahirnia, Manfred Jaeger, Oliver Schulte:
Joint Link Prediction Via Inference from a Model. CIKM 2023: 1877-1886 - [c54]Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley:
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting. ICML 2023: 31596-31612 - [c53]Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini:
A Simple Latent Variable Model for Graph Learning and Inference. LoG 2023: 26 - [c52]Xiangyu Sun, Oliver Schulte:
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing. NeurIPS 2023 - [c51]Kiarash Zahirnia, Yaochen Hu, Mark Coates, Oliver Schulte:
Neural Graph Generation from Graph Statistics. NeurIPS 2023 - [i23]Xiangyu Sun, Oliver Schulte:
Cause-Effect Inference in Location-Scale Noise Models: Maximum Likelihood vs. Independence Testing. CoRR abs/2301.12930 (2023) - [i22]Oliver Schulte:
From Graph Generation to Graph Classification. CoRR abs/2302.07989 (2023) - [i21]Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte, Mark Crowley:
Generative Causal Representation Learning for Out-of-Distribution Motion Forecasting. CoRR abs/2302.08635 (2023) - [i20]Oliver Schulte:
Computing Expected Motif Counts for Exchangeable Graph Generative Models. CoRR abs/2305.01089 (2023) - 2022
- [c50]Kiarash Zahirnia, Oliver Schulte, Ke Li, Ankita Sakhuja, Parmis Naddaf:
Deep Learning of Latent Edge Types from Relational Data. Canadian AI 2022 - [c49]Yudong Luo, Guiliang Liu, Haonan Duan, Oliver Schulte, Pascal Poupart:
Distributional Reinforcement Learning with Monotonic Splines. ICLR 2022 - [c48]Oliver Schulte:
Valuing Actions and Ranking Hockey Players With Machine Learning. LINHAC 2022: 2-9 - [c47]Guiliang Liu, Yudong Luo, Oliver Schulte, Pascal Poupart:
Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game. NeurIPS 2022 - [c46]Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li:
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders. NeurIPS 2022 - [i19]Kiarash Zahirnia, Oliver Schulte, Parmis Naddaf, Ke Li:
Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders. CoRR abs/2210.16844 (2022) - 2021
- [c45]Guiliang Liu, Xiangyu Sun, Oliver Schulte, Pascal Poupart:
Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning. NeurIPS 2021: 19622-19636 - [c44]Mohan Zhang, Oliver Schulte, Yudong Luo:
Leveraging Approximate Constraints for Localized Data Error Detection. aiDM@SIGMOD 2021: 36-44 - [i18]Kiarash Zahirnia, Ankita Sakhuja, Oliver Schulte, Parmis Nadaf, Ke Li, Xia Hu:
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning. CoRR abs/2106.15239 (2021) - [i17]Xiangyu Sun, Guiliang Liu, Pascal Poupart, Oliver Schulte:
NTS-NOTEARS: Learning Nonparametric Temporal DAGs With Time-Series Data and Prior Knowledge. CoRR abs/2109.04286 (2021) - [i16]Richard Mar, Oliver Schulte:
Pre and Post Counting for Scalable Statistical-Relational Model Discovery. CoRR abs/2110.09767 (2021) - 2020
- [j19]Fatemeh Riahi, Oliver Schulte:
Model-based exception mining for object-relational data. Data Min. Knowl. Discov. 34(3): 681-722 (2020) - [j18]Guiliang Liu, Yudong Luo, Oliver Schulte, Tarak Kharrat:
Deep soccer analytics: learning an action-value function for evaluating soccer players. Data Min. Knowl. Discov. 34(5): 1531-1559 (2020) - [c43]Mahmoud Khademi, Oliver Schulte:
Deep Generative Probabilistic Graph Neural Networks for Scene Graph Generation. AAAI 2020: 11237-11245 - [c42]Yudong Luo, Oliver Schulte, Pascal Poupart:
Inverse Reinforcement Learning for Team Sports: Valuing Actions and Players. IJCAI 2020: 3356-3363 - [c41]Manfred Jaeger, Oliver Schulte:
A Complete Characterization of Projectivity for Statistical Relational Models. IJCAI 2020: 4283-4290 - [c40]Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu:
Cracking the Black Box: Distilling Deep Sports Analytics. KDD 2020: 3154-3162 - [c39]Guiliang Liu, Oliver Schulte, Pascal Poupart, Mike Rudd, Mehrsan Javan:
Learning Agent Representations for Ice Hockey. NeurIPS 2020 - [c38]Jing Nathan Yan, Oliver Schulte, Mohan Zhang, Jiannan Wang, Reynold Cheng:
SCODED: Statistical Constraint Oriented Data Error Detection. SIGMOD Conference 2020: 845-860 - [i15]Manfred Jaeger, Oliver Schulte:
A Complete Characterization of Projectivity for Statistical Relational Models. CoRR abs/2004.10984 (2020) - [i14]Xiangyu Sun, Jack Davis, Oliver Schulte, Guiliang Liu:
Cracking the Black Box: Distilling Deep Sports Analytics. CoRR abs/2006.04551 (2020)
2010 – 2019
- 2019
- [j17]Oliver Schulte, Zhensong Qian:
FACTORBASE: multi-relational structure learning with SQL all the way. Int. J. Data Sci. Anal. 7(4): 289-309 (2019) - [j16]Oliver Schulte:
Causal Learning with Occam's Razor. Stud Logica 107(5): 991-1023 (2019) - [i13]Jing Nathan Yan, Oliver Schulte, Jiannan Wang, Reynold Cheng:
Detecting Data Errors with Statistical Constraints. CoRR abs/1902.09711 (2019) - 2018
- [c37]Mahmoud Khademi, Oliver Schulte:
Dynamic Gated Graph Neural Networks for Scene Graph Generation. ACCV (6) 2018: 669-685 - [c36]Mahmoud Khademi, Oliver Schulte:
Image Caption Generation With Hierarchical Contextual Visual Spatial Attention. CVPR Workshops 2018: 1943-1951 - [c35]Guiliang Liu, Oliver Schulte:
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation. IJCAI 2018: 3442-3448 - [c34]Guiliang Liu, Wang Zhu, Oliver Schulte:
Interpreting Deep Sports Analytics: Valuing Actions and Players in the NHL. MLSA@PKDD/ECML 2018: 69-81 - [c33]Yejia Liu, Oliver Schulte, Chao Li:
Model Trees for Identifying Exceptional Players in the NHL and NBA Drafts. MLSA@PKDD/ECML 2018: 93-105 - [c32]Guiliang Liu, Oliver Schulte, Wang Zhu, Qingcan Li:
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees. ECML/PKDD (2) 2018: 414-429 - [i12]Oliver Schulte, Yejia Liu, Chao Li:
Model Trees for Identifying Exceptional Players in the NHL Draft. CoRR abs/1802.08765 (2018) - [i11]Guiliang Liu, Oliver Schulte:
Deep Reinforcement Learning in Ice Hockey for Context-Aware Player Evaluation. CoRR abs/1805.11088 (2018) - [i10]Fatemeh Riahi, Oliver Schulte:
Model-based Exception Mining for Object-Relational Data. CoRR abs/1807.00381 (2018) - [i9]Manfred Jaeger, Oliver Schulte:
Inference, Learning, and Population Size: Projectivity for SRL Models. CoRR abs/1807.00564 (2018) - [i8]Guiliang Liu, Oliver Schulte, Wang Zhu, Qingcan Li:
Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees. CoRR abs/1807.05887 (2018) - 2017
- [j15]Oliver Schulte, Mahmoud Khademi, Sajjad Gholami, Zeyu Zhao, Mehrsan Javan Roshtkhari, Philippe Desaulniers:
A Markov Game model for valuing actions, locations, and team performance in ice hockey. Data Min. Knowl. Discov. 31(6): 1735-1757 (2017) - [c31]Oliver Schulte, Sajjad Gholami:
Locally Consistent Bayesian Network Scores for Multi-Relational Data. IJCAI 2017: 2693-2700 - 2016
- [j14]Oliver Schulte, Zhensong Qian, Arthur E. Kirkpatrick, Xiaoqian Yin, Yan Sun:
Fast learning of relational dependency networks. Mach. Learn. 103(3): 377-406 (2016) - [j13]David Beaudoin, Oliver Schulte, Tim B. Swartz:
Biased penalty calls in the National Hockey League. Stat. Anal. Data Min. 9(5): 365-372 (2016) - [c30]Fatemeh Riahi, Oliver Schulte:
Propositionalization for Unsupervised Outlier Detection in Multi-Relational Data. FLAIRS 2016: 448-453 - 2015
- [c29]Zhensong Qian, Oliver Schulte:
FactorBase : Multi-relational model learning with SQL all the way. DSAA 2015: 1-10 - [c28]Oliver Schulte, Zeyu Zhao, Kurt Routley:
What is the Value of an Action in Ice Hockey? Q-Learning for the NHL. MLSA@PKDD/ECML 2015: 77-86 - [c27]Fatemeh Riahi, Oliver Schulte:
Model-Based Outlier Detection for Object-Relational Data. SSCI 2015: 1590-1598 - [c26]Kurt Routley, Oliver Schulte:
A Markov Game Model for Valuing Player Actions in Ice Hockey. UAI 2015: 782-791 - [i7]Oliver Schulte, Zhensong Qian:
SQL for SRL: Structure Learning Inside a Database System. CoRR abs/1507.00646 (2015) - [i6]Oliver Schulte, Zhensong Qian:
FactorBase: SQL for Learning A Multi-Relational Graphical Model. CoRR abs/1508.02428 (2015) - [i5]Jan Motl, Oliver Schulte:
The CTU Prague Relational Learning Repository. CoRR abs/1511.03086 (2015) - 2014
- [j12]Oliver Schulte, Hassan Khosravi, Arthur E. Kirkpatrick, Tianxiang Gao, Yuke Zhu:
Modelling relational statistics with Bayes Nets. Mach. Learn. 94(1): 105-125 (2014) - [c25]Fatemeh Riahi, Oliver Schulte, Qing Li:
A Proposal for Statistical Outlier Detection in Relational Structures. StarAI@AAAI 2014 - [c24]Oliver Schulte, Kurt Routley:
Aggregating predictions vs. aggregating features for relational classification. CIDM 2014: 121-128 - [c23]Zhensong Qian, Oliver Schulte, Yan Sun:
Computing Multi-Relational Sufficient Statistics for Large Databases. CIKM 2014: 1249-1258 - [i4]Zhensong Qian, Oliver Schulte, Yan Sun:
Computing Multi-Relational Sufficient Statistics for Large Databases. CoRR abs/1408.5389 (2014) - [i3]Oliver Schulte, Zhensong Qian, Arthur E. Kirkpatrick, Xiaoqian Yin, Yan Sun:
Fast Learning of Relational Dependency Networks. CoRR abs/1410.7835 (2014) - 2013
- [j11]Bahareh Bina, Oliver Schulte, Branden Crawford, Zhensong Qian, Yi Xiong:
Simple decision forests for multi-relational classification. Decis. Support Syst. 54(3): 1269-1279 (2013) - [j10]Byron J. Gao, Martin Ester, Hui Xiong, Jin-Yi Cai, Oliver Schulte:
The Minimum Consistent Subset Cover Problem: A Minimization View of Data Mining. IEEE Trans. Knowl. Data Eng. 25(3): 690-703 (2013) - [c22]Oliver Schulte, Fatemeh Riahi, Qing Li:
Identifying Important Nodes in Heterogenous Networks. AAAI (Late-Breaking Developments) 2013 - [c21]Oliver Schulte, Bahareh Bina, Branden Crawford, D. Bingham, Yi Xiong:
A hierarchy of independence assumptions for multi-relational Bayes net classifiers. CIDM 2013: 150-159 - [c20]Hassan Khosravi, Ali Bozorgkhan, Oliver Schulte:
Transaction-based link strength prediction in a social network. CIDM 2013: 191-198 - [c19]Zhensong Qian, Oliver Schulte:
Learning Bayes Nets for Relational Data with Link Uncertainty. GKR 2013: 123-137 - 2012
- [j9]Oliver Schulte, Hassan Khosravi:
Learning graphical models for relational data via lattice search. Mach. Learn. 88(3): 331-368 (2012) - [j8]Hassan Khosravi, Oliver Schulte, Jianfeng Hu, Tianxiang Gao:
Learning compact Markov logic networks with decision trees. Mach. Learn. 89(3): 257-277 (2012) - [j7]Oliver Schulte, Hassan Khosravi, Tong Man:
Learning directed relational models with recursive dependencies. Mach. Learn. 89(3): 299-316 (2012) - [c18]Oliver Schulte, Hassan Khosravi, Tianxiang Gao, Yuke Zhu:
Random Regression for Bayes Nets Applied to Relational Data. StarAI@UAI 2012 - 2011
- [c17]Hassan Khosravi, Oliver Schulte, Jianfeng Hu, Tianxiang Gao:
Learning Compact Markov Logic Networks with Decision Trees. ILP 2011: 20-25 - [c16]Oliver Schulte, Hassan Khosravi, Tong Man:
Learning Directed Relational Models with Recursive Dependencies. ILP 2011: 39-44 - [c15]Oliver Schulte:
A Tractable Pseudo-Likelihood Function for Bayes Nets Applied to Relational Data. SDM 2011: 462-473 - [p2]Verónica Dahl, Sara Saghaei, Oliver Schulte:
Deidentification within Unstructured Medical Records. Biology, Computation and Linguistics 2011: 43-54 - 2010
- [j6]Oliver Schulte, Wei Luo, Russell Greiner:
Mind change optimal learning of Bayes net structure from dependency and independency data. Inf. Comput. 208(1): 63-82 (2010) - [j5]Petra Berenbrink, Oliver Schulte:
Evolutionary equilibrium in Bayesian routing games: Specialization and niche formation. Theor. Comput. Sci. 411(7-9): 1054-1074 (2010) - [c14]Hassan Khosravi, Oliver Schulte, Tong Man, Xiaoyuan Xu, Bahareh Bina:
Structure Learning for Markov Logic Networks with Many Descriptive Attributes. AAAI 2010: 487-493 - [c13]Oliver Schulte, Gustavo Frigo, Russell Greiner, Hassan Khosravi:
The IMAP Hybrid Method for Learning Gaussian Bayes Nets. Canadian AI 2010: 123-134 - [c12]Oliver Schulte, Mark S. Drew:
Discovery of Conservation Laws via Matrix Search. Discovery Science 2010: 236-250
2000 – 2009
- 2009
- [c11]Oliver Schulte, Gustavo Frigo, Russell Greiner, Wei Luo, Hassan Khosravi:
A new hybrid method for Bayesian network learning With dependency constraints. CIDM 2009: 53-60 - [c10]Bahareh Bina, Oliver Schulte, Hassan Khosravi:
LNBC: A Link-Based Naive Bayes Classifier. ICDM Workshops 2009: 489-494 - [c9]Oliver Schulte:
Simultaneous Discovery of Conservation Laws and Hidden Particles with Smith Matrix Decomposition. IJCAI 2009: 1481-1487 - 2008
- [i2]Oliver Schulte, Hassan Khosravi, Flavia Moser, Martin Ester:
Join Bayes Nets: A new type of Bayes net for relational data. CoRR abs/0811.4458 (2008) - 2007
- [c8]Oliver Schulte, Wei Luo, Russell Greiner:
Mind Change Optimal Learning of Bayes Net Structure. COLT 2007: 187-202 - [c7]Petra Berenbrink, Oliver Schulte:
Evolutionary Equilibrium in Bayesian Routing Games: Specialization and Niche Formation. ESA 2007: 29-40 - [c6]Byron J. Gao, Martin Ester, Jin-yi Cai, Oliver Schulte, Hui Xiong:
The minimum consistent subset cover problem and its applications in data mining. KDD 2007: 310-319 - [p1]Oliver Schulte:
Logically Reliable Inductive Inference. Induction, Algorithmic Learning Theory, and Philosophy 2007: 157-178 - [i1]Oliver Schulte, Flavia Moser, Martin Ester, Zhiyong Lu:
Association Rules in the Relational Calculus. CoRR abs/0710.2083 (2007) - 2006
- [j4]Wei Luo, Oliver Schulte:
Mind change efficient learning. Inf. Comput. 204(6): 989-1011 (2006) - 2005
- [c5]Wei Luo, Oliver Schulte:
Mind Change Efficient Learning. COLT 2005: 398-412 - 2004
- [j3]Oliver Schulte, James P. Delgrande:
Representing von Neumann-Morgenstern Games in the Situation Calculus. Ann. Math. Artif. Intell. 42(1-3): 73-101 (2004) - 2003
- [c4]Oliver Schulte:
Iterated backward inference: an algorithm for proper rationalizability. TARK 2003: 15-28 - 2001
- [c3]Bradley Bart, James P. Delgrande, Oliver Schulte:
Knowledge and Planning in an Action-Based Multi-agent Framework: A Case Study. AI 2001: 121-130
1990 – 1999
- 1999
- [j2]Oliver Schulte:
The Logic Of Reliable And Efficient Inquiry. J. Philos. Log. 28(4): 399-438 (1999) - [j1]Oliver Schulte:
Minimal Belief Change and the Pareto Principle. Synth. 118(3): 329-361 (1999) - [c2]Oliver Schulte:
Minimal Belief Change and Pareto-Optimality. Australian Joint Conference on Artificial Intelligence 1999: 144-155 - 1998
- [c1]Torsten Rohlfing, Jürgen Beier, Oliver Schulte, Norbert Hosten, Roland Felix:
Multimodale Registrierung mit effizienten Lernverfahren für neuronale Netze. Bildverarbeitung für die Medizin 1998
Coauthor Index
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