Original Publications

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2024

[206], , and . Prepacking: A Simple Method for Fast Prefilling and Increased Throughput in Large Language Models, In Arxiv, .
[205], , , , and . A Circus of Circuits: Connections Between Decision Diagrams, Circuits, and Automata, In Arxiv, .
[204], and . Polynomial Semantics of Tractable Probabilistic Circuits, In Arxiv, .
[203], and . Image Inpainting via Tractable Steering of Diffusion Models, In Proceedings of the Twelfth International Conference on Learning Representations (ICLR), .
[202], , , , , and . Probabilistically Rewired Message-Passing Neural Networks, In Proceedings of the Twelfth International Conference on Learning Representations (ICLR), .

2023

[201], , and . A Unified Approach to Count-Based Weakly Supervised Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[200] and . Collapsed Inference for Bayesian Deep Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[199], and . A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[198], , , , , and . Probabilistic Task-Adaptive Graph Rewiring, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, .
[197], , , , and . Scaling Integer Arithmetic in Probabilistic Programs, In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), .
[196], , and . Tractable Control for Autoregressive Language Generation, In Proceedings of the 40th International Conference on Machine Learning (ICML), . Oral full presentation, acceptance rate 155/6538 = 2.4%
[195], , and . Understanding the Distillation Process from Deep Generative Models to Tractable Probabilistic Circuits, In Proceedings of the 40th International Conference on Machine Learning (ICML), .
[194], , , and . On the Paradox of Learning to Reason from Data, In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), .
[193], and . Mixtures of All Trees, In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[192], and . Scaling Up Probabilistic Circuits by Latent Variable Distillation, In Proceedings of the International Conference on Learning Representations (ICLR), . Oral full presentation, acceptance rate 90/4849 = 1.8%
[191], , and . SIMPLE: A Gradient Estimator for k-subset sampling, In Proceedings of the International Conference on Learning Representations (ICLR), .
[190], and . Semantic Strengthening of Neuro-Symbolic Learning, In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[189], , and . Bit Blasting Probabilistic Programs, In POPL 2023 Language for Inference Workshop, .
[188], , and . Out-of-Distribution Generalization by Neural-Symbolic Joint Training, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, .
[187], and . Certifying Fairness of Probabilistic Circuits, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, .
[186], , , , and . A Unified Knowledge Distillation Framework for Deep Directed Graphical Models, In 2023 Conference on Computer Vision and Pattern Recognition (CVPR), .

2022

[185], and . Sparse Probabilistic Circuits via Pruning and Growing, In Advances in Neural Information Processing Systems 35 (NeurIPS), . Oral full presentation, acceptance rate 201/10411 = 1.9%
[184], , , and . Semantic Probabilistic Layers for Neuro-Symbolic Learning, In Advances in Neural Information Processing Systems 35 (NeurIPS), .
[183], , and . Neuro-Symbolic Entropy Regularization, In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), . Oral full presentation, acceptance rate 36/712 = 5%
[182], and . Why Is This an Outlier? Explaining Outliers by Submodular Optimization of Marginal Distributions, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), .
[181] and . Collapsed Inference for Bayesian Deep Learning, In Proceedings of the ICML Workshop on Beyond Bayes: Paths Towards Universal Reasoning Systems, .
[180], , , and . Tractable and Expressive Generative Models of Genetic Variation Data, In Proceedings of the International Conference on Research in Computational Molecular Biology (RECOMB), .  [doi]
[179], and . Lossless Compression with Probabilistic Circuits, In Proceedings of the International Conference on Learning Representations (ICLR), . Oral spotlight presentation, acceptance rate 176/3391 = 5.2%
[178], and . Solving Marginal MAP Exactly by Probabilistic Circuit Transformations, In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[177], and . Strudel: A Fast and Accurate Learner of Structured-Decomposable Probabilistic Circuits, In International Journal of Approximate Reasoning, volume 140, .  [doi]
[176], , , , , , , and . PYLON: A PyTorch Framework for Learning with Constraints, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (Demo Track), .

2021

[175], , and . Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction, .
[174] and . Tractable Regularization of Probabilistic Circuits, In Advances in Neural Information Processing Systems 34 (NeurIPS), . Oral spotlight presentation, acceptance rate 340/9122 = 3.7%
[173], , , and . A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, In Advances in Neural Information Processing Systems 34 (NeurIPS), . Oral full presentation, acceptance rate 55/9122 = 0.6%
[172], , and . flip-hoisting: Exploiting Repeated Parameters in Discrete Probabilistic Programs, In International Conference on Probabilistic Programming (PROBPROG), .
[171], , and . An Introduction to Lifted Probabilistic Inference, MIT Press, .
[170], and . Probabilistic Generating Circuits, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), . TPM best paper award
[169], , , and . Is Parameter Learning via Weighted Model Integration Tractable?, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), .
[168], , , , and . Towards an Interpretable Latent Space in Structured Models for Video Prediction, In IJCAI 2021 Weakly Supervised Representation Learning Workshop (WSRL), .
[167], and . Probabilistic Sufficient Explanations, In Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI), .
[166], , and . Tractable Computation of Expected Kernels, In Proceedings of the 37th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[165], and . Probabilistic Generating Circuits, In Proceedings of the 38th International Conference on Machine Learning (ICML), . Long presentation, acceptance rate 166/5513 = 3%
[164], , , , and . Model Checking Finite-Horizon Markov Chains with Probabilistic Inference, In Proceedings of the 33rd International Conference on Computer-Aided Verification (CAV), .
[163], and . Open-World Probabilistic Databases: Semantics, Algorithms, Complexity, In Artificial Intelligence, .  [doi]
[162], , and . On the Tractability of SHAP Explanations, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .   AAAI distinguished paper award
[161], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .
[160], , , and . Juice: A Julia Package for Logic and Probabilistic Circuits, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), .
[159], , , and . Logical Abstractions for Noisy Variational Quantum Algorithm Simulation, In Architectural Support for Programming Languages and Operating Systems (ASPLOS), . IEEE Micro top picks 2022 honorable mention
[158]. Exploiting Program Structure for Scaling Probabilistic Programming, PhD thesis, University of California, Los Angeles, . UCLA Computer Science Outstanding Graduating PhD Student Award

2020

[157], , and . On the Tractability of SHAP Explanations, In Journal of Artificial Intelligence Research (JAIR), AI Access Foundation, .  [doi]
[156], , , 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%
[155], , and . Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), .
[154], , , and . On Effective Parallelization of Monte Carlo Tree Search, In Deep Reinforcement Learning Workshop at NeurIPS (DRLW), .  
[153], , , , and . SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning, In Conference on Robot Learning, .
[152], and . Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi] ACM SIGPLAN distinguished paper award
[151], and . Probabilistic Circuits: A Unifying Framework for Tractable Probabilistic Models, In , .
[150], , , and . Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), .  
[149], and . Strudel: Learning Structured-Decomposable Probabilistic Circuits, In Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM), .  
[148], and . On the Relationship Between Probabilistic Circuits and Determinantal Point Processes, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[147] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI), .
[146], and . Towards Probabilistic Sufficient Explanations, In Extending Explainable AI Beyond Deep Models and Classifiers Workshop at ICML (XXAI), .
[145], , , and . Handling Missing Data in Decision Trees: A Probabilistic Approach, In The Art of Learning with Missing Values Workshop at ICML (Artemiss), .
[144], , , 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), .  
[143], , , , , , , and . Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits, In Proceedings of the 37th International Conference on Machine Learning (ICML), .  
[142], , , and . Semantic and Generalized Entropy Loss Functions for Semi-Supervised Deep Learning, In Entropy, volume 22, .  [doi]
[141] and . Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings, In Ninth International Workshop on Statistical Relational AI (StarAI), . StarAI best paper award
[140], , , and . Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams, In Proceedings of the Symposium on Intelligent Data Analysis (IDA), .  
[139], 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), .  
[138], , and . Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns, In Proceedings of the 34th AAAI Conference on Artificial Intelligence, .
[137], and . Lecture Notes: Probabilistic Circuits: Representation and Inference, In , .
[136], , , , 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%
[135], , , and . From Variational to Deterministic Autoencoders, In Proceedings of the 8th International Conference on Learning Representations (ICLR), .

2019

[134], , , and . On Tractable Computation of Expected Predictions, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[133], , , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[132], , and . Smoothing Structured Decomposable Circuits, In Advances in Neural Information Processing Systems 32 (NeurIPS), .   Oral spotlight presentation, acceptance rate 164/6743 = 2.4%
[131], , , 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), .
[130], , , 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), .
[129], and . The Institutional Life of Algorithmic Risk Assessment, In Berkeley Technology Law Journal, .
[128], and . Symbolic Exact Inference for Discrete Probabilistic Programs, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[127] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[126], 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%
[125], , 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), .  
[124] and . On Constrained Open-World Probabilistic Databases, In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI), .
[123], , and . Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), .  
[122], , , and . Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[121] and . On Constrained Open-World Probabilistic Databases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[120], and . The Institutional Life of Algorithms: Lessons from California's Money Bail Reform Act, In The 8th Annual Conference On Robotics, Law & Policy, .
[119] and . Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), .   Oral full presentation, acceptance rate 460/7700 = 6%
[118], , , , , , 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

2018

[117] 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%
[116], and . Sound Abstraction and Decomposition of Probabilistic Programs, In Proceedings of the 35th International Conference on Machine Learning (ICML), .  
[115], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), .
[114] and . On Robust Trimming of Bayesian Network Classifiers, In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), .  
[113], and . Probabilistic Program Inference With Abstractions, In POPL 2018 Probabilistic Programming Languages, Semantics, and Systems Workshop, .
[112], , , and . Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing, In Advances in Neural Information Processing Systems 31 (NeurIPS), .

2017

[111], , , 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
[110] and . Query Processing on Probabilistic Data: A Survey, Foundations and Trends in Databases, Now Publishers, .  [doi]
[109], , 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]
[108], 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%
[107], and . Probabilistic Program Abstractions, In Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI), .
[106], , , , 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]
[105], and . Optimal Feature Selection for Decision Robustness in Bayesian Networks, In Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI), .  [doi]
[104], 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, .
[103], , 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, .
[102], , and . Domain Recursion for Lifted Inference with Existential Quantifiers, In Seventh International Workshop on Statistical Relational AI (StarAI), .
[101] and . Towards Compact Interpretable Models: Shrinking of Learned Probabilistic Sentential Decision Diagrams, In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), .
[100], , and . Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification, In CoRR, volume abs/1703.02641, .

2016

[99], , and . New Liftable Classes for First-Order Probabilistic Inference, In Advances in Neural Information Processing Systems 29 (NIPS), .
[98], , and . Robust Channel Coding Strategies for Machine Learning Data, In Proceedings of the 54th Annual Allerton Conference on Communication, Control, and Computing, .
[97], , and . Exploiting Local and Repeated Structure in Dynamic Bayesian Networks, In Artificial Intelligence, volume 232, .  [doi]
[96], and . Algebraic Model Counting, In International Journal of Applied Logic, .  [doi]
[95], , , and . Tp-Compilation for Inference in Probabilistic Logic Programs, In International Journal of Approximate Reasoning, .  [doi]
[94], 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, .
[93]. First-Order Model Counting in a Nutshell, In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI), Early Career Spotlight Track, .
[92], 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
[91], and . Component Caching in Hybrid Domains with Piecewise Polynomial Densities, In Proceedings of the 30th Conference on Artificial Intelligence (AAAI), .
[90], and . A Relaxed Tseitin Transformation for Weighted Model Counting, In International Workshop on Statistical Relational AI, .
[89], , and . Quantifying Causal Effects on Query Answering in Databases, In 8th USENIX Workshop on the Theory and Practice of Provenance (TaPP), USENIX Association, .

2015

[88], , and . Lifted Generative Learning of Markov Logic Networks, In Machine Learning, volume 103, .  [doi]
[87], , , , , , and . Inference and Learning in Probabilistic Logic Programs using Weighted Boolean Formulas, In Theory and Practice of Logic Programming, volume 15, .  [doi]
[86] and . Knowledge Compilation of Logic Programs Using Approximation Fixpoint Theory, In Theory and Practice of Logic Programming, volume 15, .  [doi]
[85], , , and . Tractable Learning for Complex Probability Queries, In Advances in Neural Information Processing Systems 28 (NIPS), .
[84], , , 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%
[83], 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
[82], , , and . Inducing Probabilistic Relational Rules from Probabilistic Examples, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[81], 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), .
[80], , , and . Anytime Inference in Probabilistic Logic Programs with Tp-compilation, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[79], and . Probabilistic Inference in Hybrid Domains by Weighted Model Integration, In Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI), .
[78], , and . Symmetric Weighted First-Order Model Counting, In Proceedings of the 34th ACM Symposium on Principles of Database Systems (PODS), .
[77], and . Probability Distributions over Structured Spaces, In Proceedings of the AAAI Spring Symposium on KRR, .
[76]. Towards High-Level Probabilistic Reasoning with Lifted Inference, In Proceedings of the AAAI Spring Symposium on KRR, .
[75] and . On the Role of Canonicity in Knowledge Compilation, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), .  
[74] and . Lifted Probabilistic Inference for Asymmetric Graphical Models, In Proceedings of the 29th Conference on Artificial Intelligence (AAAI), .  
[73], , , , , , , , and . Innovation Lab @ KU Leuven: Education, Engineering and Artificial Intelligence, In , .
[72], , , , , and . ProbLog2: Probabilistic logic programming, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track, .

2014

[71], and . Lifted probabilistic inference: A guide for the database researcher, In Bulletin of the Technical Committee on Data Engineering, volume 37, .
[70], and . Understanding the complexity of lifted inference and asymmetric weighted model counting, In Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI), .
[69], and . The most probable database problem, In Proceedings of the First International Workshop on Big Uncertain Data (BUDA), .
[68], , and . Probabilistic sentential decision diagrams, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), .
[67], , and . Probabilistic sentential decision diagrams: Learning with massive logical constraints, In ICML Workshop on Learning Tractable Probabilistic Models (LTPM), .
[66], and . Lifted inference for probabilistic logic programs, In Workshop on Probabilistic Logic Programming (PLP), .
[65] 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 honorable mention
[64], , and . Explanation-based approximate weighted model counting for probabilistic logics, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, AAAI, .
[63], and . Skolemization for weighted first-order model counting, In Proceedings of the 14th International Conference on Principles of Knowledge Representation and Reasoning (KR), .
[62], , and . Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), .
[61], , and . Efficient probabilistic inference for dynamic relational models, . International Workshop on Statistical Relational AI
[60], , and . Compiling probabilistic logic programs into sentential decision diagrams, In Workshop on Probabilistic Logic Programming (PLP), .

2013

[59], , , 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.), .
[58], , , and . On the completeness of lifted variable elimination, In International Workshop on Statistical Relational AI (StarAI-13), Bellevue, Washington, 15 July 2013, .
[57]. Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, . ECCAI Artificial Intelligence Dissertation Award Scientific prize IBM Belgium for Informatics
[56]. On the complexity and approximation of binary evidence for lifted inference, In Proceedings of StaRAI, Statistical Relational AI workshop, Bellevue, Washington, USA, .
[55] 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%
[54], and . Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, .
[53], , , , , and . Machine learning and data mining for sports analytics, . LStat 25th Anniversary Scientific Event

2012

[52], and . k-optimal: A novel approximate inference algorithm for ProbLog, In Machine Learning, volume 89, .  [doi] ILP best student paper award
[51], and . Algebraic Model Counting, In CoRR, volume abs/1211.4475, .
[50], , and . Constraints for probabilistic logic programming, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), .
[49], , , , , and . Lifted inference for probabilistic programming, In Proceedings of the NIPS Probabilistic Programming Workshop,, .
[48], , , , , , and . ProbLog2: From probabilistic programming to statistical relational learning, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), .
[47], , , and . Lifted Variable Elimination: A Novel Operator and Completeness Results, In ArXiv e-prints, .
[46] and . Relational learning for football-related predictions, In Proceedings of the 21st Belgian-Dutch Conference on Machine Learning, .
[45] and . Liftability of probabilistic inference: Upper and lower bounds, In Proceedings of the 2nd International Workshop on Statistical Relational AI,, .
[44], 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.), .
[43] 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, .

2011

[42], , , 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, .
[41]. 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%
[40], , , 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%
[39], 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, .
[38], and . Probabilistic logic in dynamic domains: Particle filter with distributional clauses, In Preliminary Papers ILP, .
[37], and . k-Optimal: A novel approximate inference algorithm for ProbLog, In Preliminary Papers ILP, .
[36] 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.), .
[35], , , , , and . ProbLog, Association for Logic Programming, . ALP Newsletter
[34] and . Relational learning for football-related predictions, In Preliminary Papers ILP, .

2010

[33], 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.), .
[32], , 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, .  

2009

[31], 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]
[30], , , , , , , , , , , , , , , , , , , , , and . An exercise with statistical relational learning systems, In International Workshop on Statistical Relational Learning (Pedro Domingos, Kristian Kersting, eds.), .
[29]. Algorithms and assessment in no-limit computer poker, Master's thesis, KU Leuven, . Alcatel-Lucent Innovation Award

Other Versions of Published Work

2023

[28] and . Collapsed Inference for Bayesian Deep Learning, In ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling, .
[27], , , and . Semantic Probabilistic Layers for Neuro-Symbolic Learning, In 17th International Workshop on Neuro-Symbolic Learning and Reasoning, .
[26], and . A Pseudo-Semantic Loss for Deep Generative Models with Logical Constraints, In Knowledge and Logical Reasoning in the Era of Data-driven Learning Workshop, .
[25], , and . A Unified Approach to Count-Based Weakly-Supervised Learning, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, .
[24], , and . SIMPLE: A Gradient Estimator for k-subset sampling, In ICML 2023 Workshop on Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators, .

2022

[23], and . Certifying Fairness of Probabilistic Circuits, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), .

2021

[22], and . Probabilistic Sufficient Explanations, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), .
[21] and . Tractable Regularization of Probabilistic Circuits, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), .
[20], , , and . A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference, In Proceedings of the UAI Workshop on Tractable Probabilistic Modeling (TPM), .

2020

[19], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Algorithmic Fairness through the Lens of Causality and Interpretability Workshop at NeurIPS (AFCI), .

2019

[18], , and . Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns, In NeurIPS 2019 Workshop on Machine Learning with Guarantees, .
[17], , , and . Tractable Computation of the Moments of Predictive Models, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[16] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[15], , and . Towards Hardware-Aware Tractable Learning of Probabilistic Models, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[14], , and . Smoothing Structured Decomposable Circuits, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[13] and . Learning Logistic Circuits, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[12], , and . What to Expect of Classifiers? Reasoning about Logistic Regression with Missing Features, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .  
[11] and . On Constrained Open-World Probabilistic Databases, In Proceedings of the ICML Workshop on Tractable Probabilistic Modeling (TPM), .
[10] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the IJCAI Workshop on Declarative Learning Based Programming (DeLBP), .  

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.), .