Publications by Year
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2018 | |
| [99] | . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NIPS), 2018. Oral full presentation, acceptance rate 30/4856 = 0.6% |
| [98] | . Learning Logistic Circuits, In Proceedings of the UAI 2018 Workshop: Uncertainty in Deep Learning, 2018. ![[pdf]](../img/icon_pdf.jpg "pdf"){kind=icon} |
| [97] | . Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018. |
| [96] | . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), 2018. |
| [95] | . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), 2018. |
| [94] | . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), 2018. |
| [93] | . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), 2018. |
| [92] | . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), 2018. |
| [91] | . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In ArXiv e-prints, 2018. |
| [90] | . Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, 2018. |
2017 | |
| [89] | . A Semantic Loss Function for Deep Learning Under Weak Supervision, In NIPS 2017 Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond, 2017. LLD best paper award runner up |
| [88] | . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In CoRR, volume abs/1711.11157, 2017. |
| [87] | . Coded Machine Learning: Joint Informed Replication and Learning for Linear Regression, In Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing, 2017. ![[doi]](../img/doi.gif "doi") |
| [86] | . Don’t Fear the Bit Flips: Robust Linear Prediction Through Informed Channel Coding, In ICML 2017 Workshop on Reliable Machine Learning in the Wild, 2017. |
| [85] | . Towards Compact Interpretable Models: Shrinking of Learned Probabilistic Sentential Decision Diagrams, In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), 2017. |
| [84] | . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In IJCAI 2017 Workshop on Logical Foundations for Uncertainty and Machine Learning, 2017. |
| [83] | . Probabilistic Program Abstractions, In Seventh International Workshop on Statistical Relational AI (StarAI), 2017. |
| [82] | . Domain Recursion for Lifted Inference with Existential Quantifiers, In Seventh International Workshop on Statistical Relational AI (StarAI), 2017. |
| [81] | . Open-World Probabilistic Databases: An Abridged Report, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, 2017. |
| [80] | . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017. |
| [79] | . Combining Stochastic Constraint Optimization and Probabilistic Programming: From Knowledge Compilation to Constraint Solving, In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (CP), 2017. |
| [78] | . Query Processing on Probabilistic Data: A Survey, Foundations and Trends in Databases, Now Publishers, 2017. |
| [77] | . Probabilistic Program Abstractions, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017. |
| [76] | . Learning the Structure of Probabilistic Sentential Decision Diagrams, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), 2017. Oral full presentation, acceptance rate 29/289 = 10% |
| [75] | . Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification, In CoRR, volume abs/1703.02641, 2017. |
2016 | |
| [74] | . New Liftable Classes for First-Order Probabilistic Inference, In Advances in Neural Information Processing Systems 29 (NIPS), 2016. |
| [73] | . Algebraic Model Counting, In International Journal of Applied Logic, 2016. |
| [72] | . Robust Channel Coding Strategies for Machine Learning Data, In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, 2016. |
| [71] | . First-Order Model Counting in a Nutshell, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, 2016. |
| [70] | . Hashing-Based Approximate Probabilistic Inference in Hybrid Domains: An Abridged Report, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, 2016. |
| [69] | . Tp-Compilation for Inference in Probabilistic Logic Programs, In International Journal of Approximate Reasoning, 2016. |
| [68] | . Open-World Probabilistic Databases, In Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2016. KR best student paper award |
| [67] | . Open World Probabilistic Databases (Extended Abstract), In Proceedings of the 29th International Workshop on Description Logics (DL), 2016. |
| [66] | . Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, volume 232, 2016. |
| [65] | . Component Caching in Hybrid Domains with Piecewise Polynomial Densities, In Proceedings of the 30th Conference on Artificial Intelligence (AAAI), 2016. |
| [64] | . A Relaxed Tseitin Transformation for Weighted Model Counting, In International Workshop on Statistical Relational AI, 2016. |
| [63] | . Quantifying Causal Effects on Query Answering in Databases, In 8th USENIX Workshop on the Theory and Practice of Provenance (TaPP), USENIX Association, 2016. |
2015 | |
| [62] | . Tractable Learning for Complex Probability Queries, In Advances in Neural Information Processing Systems 28 (NIPS), 2015. |
| [61] | . Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas, In Theory and Practice of Logic Programming, volume 15, 2015. |
| [60] | . Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory, In Theory and Practice of Logic Programming, volume 15, 2015. |
| [59] | . Anytime Inference in Probabilistic Logic Programs with Tp-compilation, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [58] | . Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [57] | . Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. Oral full presentation, acceptance rate 28/292 = 9% |
| [56] | . Towards High-Level Probabilistic Reasoning with Lifted Inference, In Proceedings of the AAAI Spring Symposium on KRR, 2015. |
| [55] | . On the Role of Canonicity in Knowledge Compilation, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015. |
| [54] | . Lifted Probabilistic Inference for Asymmetric Graphical Models, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), 2015. |
| [53] | . Lifted Generative Learning of Markov Logic Networks, In Machine Learning, volume 103, 2015. |
| [52] | . Innovation Lab @ KU Leuven: Education, Engineering and Artificial Intelligence, In , 2015. |
| [51] | . ProbLog2: Probabilistic logic programming, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track, 2015. |
| [50] | . Probability Distributions over Structured Spaces, In Proceedings of the AAAI Spring Symposium on KRR, 2015. |
| [49] | . Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [48] | . Hashing-Based Approximate Probabilistic Inference in Hybrid Domains, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), 2015. UAI best paper award |
| [47] | . Probabilistic Inference in Hybrid Domains by Weighted Model Integration, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), 2015. |
| [46] | . Symmetric Weighted First-Order Model Counting, In Proceedings of the 34th ACM Symposium on Principles of Database Systems (PODS), 2015. |
2014 | |
| [45] | . Lifted probabilistic inference: A guide for the database researcher, In Bulletin of the Technical Committee on Data Engineering, volume 37, 2014. |
| [44] | . Understanding the complexity of lifted inference and asymmetric weighted model counting, In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), 2014. |
| [43] | . Probabilistic sentential decision diagrams, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. |
| [42] | . Lifted inference for probabilistic logic programs, In Workshop on Probabilistic Logic Programming (PLP), 2014. |
| [41] | . Tractability through exchangeability: A new perspective on efficient probabilistic inference, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI Conference on Artificial Intelligence, 2014. AAAI best paper award honorable mention |
| [40] | . Skolemization for weighted first-order model counting, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), 2014. |
| [39] | . Efficient probabilistic inference for dynamic relational models, 2014. International Workshop on Statistical Relational AI |
| [38] | . Compiling probabilistic logic programs into sentential decision diagrams, In Workshop on Probabilistic Logic Programming (PLP), 2014. |
| [37] | . An Efficient Method for Bayesian Network Parameter Learning from Incomplete Data, In ICML Workshop on Causal Modeling & Machine Learning, 2014. |
| [36] | . The most probable database problem, In Proceedings of the First International Workshop on Big Uncertain Data (BUDA), 2014. |
| [35] | . Probabilistic sentential decision diagrams: Learning with massive logical constraints, In ICML Workshop on Learning Tractable Probabilistic Models (LTPM), 2014. |
| [34] | . Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), 2014. |
| [33] | . Explanation-based approximate weighted model counting for probabilistic logics, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, 2014. |
2013 | |
| [32] | . On the complexity and approximation of binary evidence in lifted inference, In Advances in Neural Information Processing Systems 26 (NIPS), 2013. Oral spotlight presentation, acceptance rate 72/1420 = 5% |
| [31] | . Machine learning and data mining for sports analytics, 2013. LStat 25th Anniversary Scientific Event |
| [30] | . On the completeness of lifted variable elimination, In International Workshop on Statistical Relational AI (StarAI-13), Bellevue, Washington, 15 July 2013, 2013. |
| [29] | . On the complexity and approximation of binary evidence for lifted inference, In Proceedings of StaRAI, Statistical Relational AI workshop, Bellevue, Washington, USA, 2013. |
| [28] | . Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, 2013. |
| [27] | . Completeness results for lifted variable elimination, In Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR Workshop and Conference Proceedings (Carlos M. Carvalho, Pradeep Ravikumar, eds.), 2013. |
| [26] | . Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, 2013. ECCAI Artificial Intelligence Dissertation Award Scientific prize IBM Belgium for Informatics |
2012 | |
| [25] | . Constraints for probabilistic logic programming, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012. |
| [24] | . Lifted inference for probabilistic programming, In Proceedings of the NIPS Probabilistic Programming Workshop,, 2012. |
| [23] | . ProbLog2: From probabilistic programming to statistical relational learning, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), 2012. |
| [22] | . Lifted Variable Elimination: A Novel Operator and Completeness Results, In ArXiv e-prints, 2012. |
| [21] | . k-optimal: A novel approximate inference algorithm for ProbLog, In Machine Learning, volume 89, 2012. ILP best student paper award |
| [20] | . Lifted relax, compensate and then recover: From approximate to exact lifted probabilistic inference, In Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI) (Nando de Freitas, Kevin Murphy, eds.), 2012. |
| [19] | . Conditioning in first-order knowledge compilation and lifted probabilistic inference, In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, (Joerg Hoffmann, Bart Selman, eds.), AAAI Press, 2012. |
| [18] | . Relational learning for football-related predictions, In Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, 2012. |
| [17] | . Algebraic Model Counting, In CoRR, volume abs/1211.4475, 2012. |
| [16] | . Liftability of probabilistic inference: Upper and lower bounds, In Proceedings of the 2nd International Workshop on Statistical Relational AI,, 2012. |
2011 | |
| [15] | . On the completeness of first-order knowledge compilation for lifted probabilistic inference, In Advances in Neural Information Processing Systems 24 (NIPS),, 2011. Oral full presentation, acceptance rate 20/1400 = 1.4% |
| [14] | . Automatic discretization of actions and states in Monte-Carlo tree search, In Proceedings of the ECML/PKDD 2011 Workshop on Machine Learning and Data Mining in and around Games, (Tom Croonenborghs, Kurt Driessens, Olana Missura, eds.), 2011. |
| [13] | . Lifted probabilistic inference by first-order knowledge compilation, In Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI) (Toby Walsh, ed.), AAAI Press/International Joint Conferences on Artificial Intelligence, 2011. |
| [12] | . Relational learning for football-related predictions, In Preliminary Papers ILP, 2011. |
| [11] | . k-Optimal: A novel approximate inference algorithm for ProbLog, In Preliminary Papers ILP, 2011. |
| [10] | . Probabilistic logic in dynamic domains: Particle filter with distributional clauses, In Preliminary Papers ILP, 2011. |
| [9] | . ProbLog, Association for Logic Programming, 2011. ALP Newsletter |
| [8] | . An algebraic Prolog for reasoning about possible worlds, In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, (Wolfram Burgard, Dan Roth, eds.), AAAI Press, 2011. |
| [7] | . Inference in probabilistic logic programs using weighted CNF's, In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), (Fabio Gagliardi Cozman, Avi Pfeffer, eds.), 2011. Oral full presentation, acceptance rate 24/285 = 8% |
2010 | |
| [6] | . DTProbLog: A decision-theoretic probabilistic Prolog, In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence, (Maria Fox, David Poole, eds.), AAAI Press, 2010. |
| [5] | . Probabilistic programming for planning problems, In Statistical Relational AI workshop (Kristian Kersting, Stuart Russell, Leslie Pack Kaelbling, Alon Halevy, Sriraam Natarajan, Lilyana Mihalkova, eds.), 2010. |
2009 | |
| [4] | . Monte-Carlo tree search in poker using expected reward distributions, In Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC) (Toon Calders, Karl Tuyls, Mykola Pechenizkiy, eds.), 2009. |
| [3] | . Monte-Carlo tree search in poker using expected reward distributions, In Proceedings of the 1st Asian Conference on Machine Learning (ACML), Lecture Notes in Computer Science, Springer, 2009. |
| [2] | . An exercise with statistical relational learning systems, In International Workshop on Statistical Relational Learning (Pedro Domingos, Kristian Kersting, eds.), 2009. |
| [1] | . Algorithms and assessment in no-limit computer poker, Master's thesis, KU Leuven, 2009. Alcatel-Lucent Innovation Award |