Recent Papers with Code

2025

[244], and . Accelerating Diffusion LLMs via Adaptive Parallel Decoding, In Advances in Neural Information Processing Systems 38 (NeurIPS), . Oral spotlight presentation, acceptance rate 688/21575 = 3.1%
[243], , , , and . Tuning Random Generators: Property-Based Testing as Probabilistic Programming, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi]
[242], and . Adversarial Tokenization, In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics, .  

2024

[241], , , and . Adaptable Logical Control for Large Language Models, In Advances in Neural Information Processing Systems 37 (NeurIPS), .
[240], , , and . Where is the signal in tokenization space?, In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), .   Oral full presentation, acceptance rate 198/6105 = 3.2%
[239], , and . Bit Blasting Probabilistic Programs, In Proc. ACM Program. Lang. (PLDI), Association for Computing Machinery, .  [doi]

2023

[238] and . Collapsed Inference for Bayesian Deep Learning, In Advances in Neural Information Processing Systems 36 (NeurIPS), .
[237], , , and . On the Paradox of Learning to Reason from Data, In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI), .
[236], and . Mixtures of All Trees, In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[235], 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%
[234], , and . SIMPLE: A Gradient Estimator for k-subset sampling, In Proceedings of the International Conference on Learning Representations (ICLR), .
[233], and . Semantic Strengthening of Neuro-Symbolic Learning, In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[232], , and . Out-of-Distribution Generalization by Neural-Symbolic Joint Training, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, .
[231], and . Certifying Fairness of Probabilistic Circuits, In Proceedings of the 37th AAAI Conference on Artificial Intelligence, .

2022

[230], 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%
[229], , , and . Semantic Probabilistic Layers for Neuro-Symbolic Learning, In Advances in Neural Information Processing Systems 35 (NeurIPS), .
[228], , 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%
[227], 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%
[226], and . Solving Marginal MAP Exactly by Probabilistic Circuit Transformations, In Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), .
[225], , , , , , , and . PYLON: A PyTorch Framework for Learning with Constraints, In Proceedings of the 36th AAAI Conference on Artificial Intelligence (Demo Track), .

2021

[224] and . Tractable Regularization of Probabilistic Circuits, In Advances in Neural Information Processing Systems 34 (NeurIPS), . Oral spotlight presentation, acceptance rate 340/9122 = 3.7%
[223], , , 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%
[222], , and . Tractable Computation of Expected Kernels, In Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI), .
[221], and . Probabilistic Generating Circuits, In Proceedings of the 38th International Conference on Machine Learning (ICML), . Long presentation, acceptance rate 166/5513 = 3%
[220], and . Group Fairness by Probabilistic Modeling with Latent Fair Decisions, In Proceedings of the 35th AAAI Conference on Artificial Intelligence, .
[219], , , and . Juice: A Julia Package for Logic and Probabilistic Circuits, In Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track), .
[218], , , 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

2020

[217], , , 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%
[216], , and . Counterexample-Guided Learning of Monotonic Neural Networks, In Advances in Neural Information Processing Systems 33 (NeurIPS), .
[215], and . Scaling Exact Inference for Discrete Probabilistic Programs, In Proc. ACM Program. Lang. (OOPSLA), ACM, .  [doi] ACM SIGPLAN distinguished paper award
[214], , , and . Relax, compensate and then integrate, In Proceedings of the ECML-PKDD Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), .  
[213], , , 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), .  
[212], 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), .  
[211], , and . Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns, In Proceedings of the 34th AAAI Conference on Artificial Intelligence, .

2019

[210], , , and . On Tractable Computation of Expected Predictions, In Advances in Neural Information Processing Systems 32 (NeurIPS), .
[209], , and . Smoothing Structured Decomposable Circuits, In Advances in Neural Information Processing Systems 32 (NeurIPS), .   Oral spotlight presentation, acceptance rate 164/6743 = 2.4%
[208] and . Efficient Search-Based Weighted Model Integration, In Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI), .
[207], 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%
[206], , 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), .  
[205], , and . Active Inductive Logic Programming for Code Search, In The 41st ACM/IEEE International Conference on Software Engineering (ICSE), .  
[204], , , and . Scalable Rule Learning in Probabilistic Knowledge Bases, In The 1st Conference On Automated Knowledge Base Construction (AKBC), .
[203] and . Learning Logistic Circuits, In Proceedings of the 33rd Conference on Artificial Intelligence (AAAI), .   Oral full presentation, acceptance rate 460/7700 = 6%

2018

[202] 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%
[201], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In Proceedings of the 35th International Conference on Machine Learning (ICML), .

2017

[200], , , 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
[199], , , and . A Semantic Loss Function for Deep Learning with Symbolic Knowledge, In CoRR, volume abs/1711.11157, .
[198], 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%

2015

[197], , and . Lifted Generative Learning of Markov Logic Networks, In Machine Learning, volume 103, .  [doi]
[196], , , and . Tractable Learning for Complex Probability Queries, In Advances in Neural Information Processing Systems 28 (NIPS), .
[195], , , 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%
[194], , , , , and . ProbLog2: Probabilistic logic programming, In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Demo Track, .

2014

[193], , and . Tractable learning of liftable Markov logic networks, In Proceedings of the ICML-14 Workshop on Learning Tractable Probabilistic Models (LTPM), .

2013

[192]. Lifted Inference and Learning in Statistical Relational Models, PhD thesis, KU Leuven, . ECCAI Artificial Intelligence Dissertation Award Scientific prize IBM Belgium for Informatics
[191] 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%
[190], and . Lifted generative parameter learning, In Statistical Relational AI (StaRAI) workshop, .

2012

[189], , , , , , and . ProbLog2: From probabilistic programming to statistical relational learning, In Proceedings of the NIPS Probabilistic Programming Workshop, (Daniel Roy, Vikash Mansinghka, Noah Goodman, eds.), .
[188], 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.), .
[187] 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

[186], , , 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, .
[185]. 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%
[184], , , 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%
[183], , , , , and . ProbLog, Association for Logic Programming, . ALP Newsletter

2010

[182], , 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, .