Original Publications

2021

[147], , , and . Juice: A Julia Package for Logic and Probabilistic Circuits, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), .
[146], , , and . Noisy Variational Quantum Algorithm Simulation via Knowledge Compilation for Repeated Inference, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), .

2020

[145], , , and . Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations, In Advances in Neural Information Processing Systems 33 (NeurIPS), . Oral spotlight presentation, acceptance rate 385/9454 = 4.1%
[144], , and . Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), .
[143], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Algorithmic Fairness through the Lens of Causality and Interpretability Workshop at NeurIPS (AFCI), .
[142], , , and . On Effective Parallelization of Monte Carlo Tree Search, In Deep Reinforcement Learning Workshop at NeurIPS (DRLW), .
[141], , , , and . SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning, In Conference on Robot Learning, .
[140], and . Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi] ACM SIGPLAN distinguished paper award
[139], and . Probabilistic Circuits: A Unifying Framework for Tractable Probabilistic Models, In , .
[138], and . Strudel: Learning Structured-Decomposable Probabilistic Circuits, In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM), .
[137], , , and . Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), .
[136], and . On the Relationship Between Probabilistic Circuits and Determinantal Point Processes, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[135] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[134], and . Towards Probabilistic Sufficient Explanations, In Extending Explainable AI Beyond Deep Models and Classifiers Workshop at ICML (XXAI), .
[133], , , and . Handling Missing Data in Decision Trees: A Probabilistic Approach, In The Art of Learning with Missing Values Workshop at ICML (Artemiss), .
[132], , , and . Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing, In Proceedings of the 37th International Conference on Machine Learning (ICML), .
[131], , , , , , , and . Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits, In Proceedings of the 37th International Conference on Machine Learning (ICML), .
[130], and . Off-Policy Deep Reinforcement Learning with Analogous Disentangled Exploration, In Proceedings of the 19th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), .
[129], , , , and . Watch the Unobserved: A Simple Approach to Parallelizing Monte Carlo Tree Search, In Proceedings of the 8th International Conference on Learning Representations (ICLR), . Oral full presentation, acceptance rate 48/2594 = 1.9%
[128], , , and . From Variational to Deterministic Autoencoders, In Proceedings of the 8th International Conference on Learning Representations (ICLR), .
[127], , , and . Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams, In Proceedings of the Symposium on Intelligent Data Analysis (IDA), .
[126], , , and . Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning, In Entropy, volume 22, .  [doi]
[125], and . Lecture Notes: Probabilistic Circuits: Representation and Inference, In , .
[124] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Ninth International Workshop on Statistical Relational AI (StarAI), . StarAI best paper award
[123], , and . Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns, In Proceedings of the 34th AAAI Conference on Artificial Intelligence, .

2019

[122], , , and . On Tractable Computation of Expected Predictions, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[121], , , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[120], , and . Smoothing Structured Decomposable Circuits, In Advances in Neural Information Processing Systems 32 (NeurIPS), . Oral spotlight presentation, acceptance rate 164/6743 = 2.4%
[119], , , and . Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing, In Proceedings of the NeurIPS Workshop on Knowledge Representation and Reasoning Meets Machine Learning (KR2ML), .
[118], , , and . On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications, In Proceedings of the NeurIPS Workshop on Energy Efficient Machine Learning and Cognitive Computing (EMC2), .
[117] and . On Constrained Open-World Probabilistic Databases, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), .
[116], and . The Institutional Life of Algorithmic Risk Assessment, In Berkeley Technology Law Journal, .
[115], , and . What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), .
[114], and . Generating and Sampling Orbits for Lifted Probabilistic Inference, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), . Oral full presentation, acceptance rate 35/450 = 7%
[113] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[112], , , , , , and . Random Sum-Product Networks: A Simple but Effective Approach to Probabilistic Deep Learning, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), . Oral full presentation
[111], and . Symbolic Exact Inference for Discrete Probabilistic Programs, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[110], , and . Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), .
[109], , , and . Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[108] and . On Constrained Open-World Probabilistic Databases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[107], and . The Institutional Life of Algorithms: Lessons from California's Money Bail Reform Act, In The 8th Annual Conference On Robotics, Law & Policy, .
[106] and . Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), . Oral full presentation, acceptance rate 460/7700 = 6%

2018

[105], , , and . Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing, In Advances in Neural Information Processing Systems 31 (NeurIPS), .
[104] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Advances in Neural Information Processing Systems 31 (NeurIPS), . Oral full presentation, acceptance rate 30/4856 = 0.6%
[103] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), .
[102], and . Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), .
[101], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), .
[100], and . Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, .

2017

[99], , , and . A Semantic Loss Function for Deep Learning Under Weak Supervision, In NIPS 2017 Workshop on Learning with Limited Labeled Data: Weak Supervision and Beyond, . LLD best paper award runner up
[98], , and . Coded Machine Learning: Joint Informed Replication and Learning for Linear Regression, In Proceedings of the 55th Annual Allerton Conference on Communication, Control, and Computing, .  [doi]
[97], , , , and . 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), .  [doi]
[96], and . Learning the Structure of Probabilistic Sentential Decision Diagrams, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), . Oral full presentation, acceptance rate 29/289 = 10%
[95], and . Open-World Probabilistic Databases: An Abridged Report, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), Sister Conference Best Paper Track, .
[94], , and . Don’t Fear the Bit Flips: Robust Linear Prediction Through Informed Channel Coding, In ICML 2017 Workshop on Reliable Machine Learning in the Wild, .
[93], , and . Domain Recursion for Lifted Inference with Existential Quantifiers, In Seventh International Workshop on Statistical Relational AI (StarAI), .
[92] and . Towards Compact Interpretable Models: Shrinking of Learned Probabilistic Sentential Decision Diagrams, In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), .
[91], and . Probabilistic Program Abstractions, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), .
[90] and . Query Processing on Probabilistic Data: A Survey, Foundations and Trends in Databases, Now Publishers, .  [doi]
[89], and . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), .  [doi]
[88], , and . Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification, In CoRR, volume abs/1703.02641, .

2016

[87], , and . New Liftable Classes for First-Order Probabilistic Inference, In Advances in Neural Information Processing Systems 29 (NIPS), .
[86], and . Algebraic Model Counting, In International Journal of Applied Logic, .  [doi]
[85], , and . Robust Channel Coding Strategies for Machine Learning Data, In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, .
[84], and . 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, .
[83]. First-Order Model Counting in a Nutshell, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, .
[82], , , and . Tp-Compilation for Inference in Probabilistic Logic Programs, In International Journal of Approximate Reasoning, .  [doi]
[81], and . Open-World Probabilistic Databases, In Proceedings of the 15th International Conference on Principles of Knowledge Representation and Reasoning (KR), . KR best student paper award
[80], , and . Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, volume 232, .  [doi]
[79], and . A Relaxed Tseitin Transformation for Weighted Model Counting, In International Workshop on Statistical Relational AI, .
[78], and . Component Caching in Hybrid Domains with Piecewise Polynomial Densities, In Proceedings of the 30th Conference on Artificial Intelligence (AAAI), .
[77], , and . Quantifying Causal Effects on Query Answering in Databases, In 8th USENIX Workshop on the Theory and Practice of Provenance (TaPP), USENIX Association, .

2015

[76], , , and . Tractable Learning for Complex Probability Queries, In Advances in Neural Information Processing Systems 28 (NIPS), .
[75] and . Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory, In Theory and Practice of Logic Programming, volume 15, .  [doi]
[74], , , , , , and . Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas, In Theory and Practice of Logic Programming, volume 15, .  [doi]
[73], , , and . Anytime Inference in Probabilistic Logic Programs with Tp-compilation, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[72], , , and . Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[71], , , and . Efficient Algorithms for Bayesian Network Parameter Learning from Incomplete Data, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), . Oral full presentation, acceptance rate 28/292 = 9%
[70]. Towards High-Level Probabilistic Reasoning with Lifted Inference, In Proceedings of the AAAI Spring Symposium on KRR, .
[69] and . On the Role of Canonicity in Knowledge Compilation, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), .
[68] and . Lifted Probabilistic Inference for Asymmetric Graphical Models, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), .
[67], , and . Lifted Generative Learning of Markov Logic Networks, In Machine Learning, volume 103, .  [doi]
[66], , , , , , , , and . Innovation Lab @ KU Leuven: Education, Engineering and Artificial Intelligence, In , .
[65], , , , , and . ProbLog2: Probabilistic logic programming, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track, .
[64], and . Probability Distributions over Structured Spaces, In Proceedings of the AAAI Spring Symposium on KRR, .
[63], and . Tractable Learning for Structured Probability Spaces: A Case Study in Learning Preference Distributions, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[62], and . Hashing-Based Approximate Probabilistic Inference in Hybrid Domains, In Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence (UAI), . UAI best paper award
[61], and . Probabilistic Inference in Hybrid Domains by Weighted Model Integration, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[60], , and . Symmetric Weighted First-Order Model Counting, In Proceedings of the 34th ACM Symposium on Principles of Database Systems (PODS), .

2014

[59], and . Lifted probabilistic inference: A guide for the database researcher, In Bulletin of the Technical Committee on Data Engineering, volume 37, .
[58], and . Lifted inference for probabilistic logic programs, In Workshop on Probabilistic Logic Programming (PLP), .
[57] and . 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, . AAAI best paper award honorable mention
[56], and . Skolemization for weighted first-order model counting, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), .
[55], , and . Efficient probabilistic inference for dynamic relational models, . International Workshop on Statistical Relational AI
[54], , and . Compiling probabilistic logic programs into sentential decision diagrams, In Workshop on Probabilistic Logic Programming (PLP), .
[53], and . Understanding the complexity of lifted inference and asymmetric weighted model counting, In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), .
[52], , and . Probabilistic sentential decision diagrams, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), .
[51], and . The most probable database problem, In Proceedings of the First International Workshop on Big Uncertain Data (BUDA), .
[50], , and . Probabilistic sentential decision diagrams: Learning with massive logical constraints, In ICML Workshop on Learning Tractable Probabilistic Models (LTPM), .
[49], , and . Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), .
[48], , and . Explanation-based approximate weighted model counting for probabilistic logics, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, .

2013

[47] and . On the complexity and approximation of binary evidence in lifted inference, In Advances in Neural Information Processing Systems 26 (NIPS), . Oral spotlight presentation, acceptance rate 72/1420 = 5%
[46], , , , , and . Machine learning and data mining for sports analytics, . LStat 25th Anniversary Scientific Event
[45], and . Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, .
[44]. On the complexity and approximation of binary evidence for lifted inference, In Proceedings of StaRAI, Statistical Relational AI workshop, Bellevue, Washington, USA, .
[43], , , and . On the completeness of lifted variable elimination, In International Workshop on Statistical Relational AI (StarAI-13), Bellevue, Washington, 15 July 2013, .
[42], , , and . 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.), .
[41]. Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, . ECCAI Artificial Intelligence Dissertation Award Scientific prize IBM Belgium for Informatics

2012

[40], , and . Constraints for probabilistic logic programming, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), .
[39], , , , , and . Lifted inference for probabilistic programming, In Proceedings of the NIPS Probabilistic Programming Workshop,, .
[38], , , , , , and . ProbLog2: From probabilistic programming to statistical relational learning, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), .
[37], , , and . Lifted Variable Elimination: A Novel Operator and Completeness Results, In ArXiv e-prints, .
[36], and . k-optimal: A novel approximate inference algorithm for ProbLog, In Machine Learning, volume 89, .  [doi] ILP best student paper award
[35], and . 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.), .
[34] and . 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, .
[33] and . Relational learning for football-related predictions, In Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, .
[32], and . Algebraic Model Counting, In CoRR, volume abs/1211.4475, .
[31] and . Liftability of probabilistic inference: Upper and lower bounds, In Proceedings of the 2nd International Workshop on Statistical Relational AI,, .

2011

[30]. On the completeness of first-order knowledge compilation for lifted probabilistic inference, In Advances in Neural Information Processing Systems 24 (NIPS),, . Oral full presentation, acceptance rate 20/1400 = 1.4%
[29] and . 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.), .
[28], , , and . 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, .
[27] and . Relational learning for football-related predictions, In Preliminary Papers ILP, .
[26], and . k-Optimal: A novel approximate inference algorithm for ProbLog, In Preliminary Papers ILP, .
[25], and . Probabilistic logic in dynamic domains: Particle filter with distributional clauses, In Preliminary Papers ILP, .
[24], , , , , and . ProbLog, Association for Logic Programming, . ALP Newsletter
[23], and . 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, .
[22], , , and . 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.), . Oral full presentation, acceptance rate 24/285 = 8%

2010

[21], , and . DTProbLog: A decision-theoretic probabilistic Prolog, In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence, (Maria Fox, David Poole, eds.), AAAI Press, .
[20], and . Probabilistic programming for planning problems, In Statistical Relational AI workshop (Kristian Kersting, Stuart Russell, Leslie Pack Kaelbling, Alon Halevy, Sriraam Natarajan, Lilyana Mihalkova, eds.), .

2009

[19], and . 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, .  [doi]
[18], , , , , , , , , , , , , , , , , , , , , and . An exercise with statistical relational learning systems, In International Workshop on Statistical Relational Learning (Pedro Domingos, Kristian Kersting, eds.), .
[17]. Algorithms and assessment in no-limit computer poker, Master's thesis, KU Leuven, . Alcatel-Lucent Innovation Award

Other Versions of Published Work

2019

[16], , , and . Tractable Computation of the Moments of Predictive Models, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[15] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[14], , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[13], , and . Smoothing Structured Decomposable Circuits, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[12] and . Learning Logistic Circuits, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[11], , and . What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[10] and . On Constrained Open-World Probabilistic Databases, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .

2018

[9] and . Learning Logistic Circuits, In Proceedings of the UAI 2018 Workshop: Uncertainty in Deep Learning, .
[8] and . Approximate Knowledge Compilation by Online Collapsed Importance Sampling, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), .
[7], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), .
[6] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the ICML Workshop on Tractable Probabilistic Models (TPM), .

2017

[5], and . Probabilistic Program Abstractions, In Seventh International Workshop on Statistical Relational AI (StarAI), .
[4], and . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In IJCAI 2017 Workshop on Logical Foundations for Uncertainty and Machine Learning, .

2016

[3], and . Open World Probabilistic Databases (Extended Abstract), In Proceedings of the 29th International Workshop on Description Logics (DL), .

2014

[2], , and . An Efficient Method for Bayesian Network Parameter Learning from Incomplete Data, In ICML Workshop on Causal Modeling & Machine Learning, .

2009

[1], and . 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.), .