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- Cen Wan and Alex A. Freitas. Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces
- David Kinney and David Watson. Causal Feature Learning for Utility-Maximizing Agents
- Verónica Rodríguez-López and Luis Enrique Sucar. Knowledge Transfer for Learning Markov Equivalence Classes
- Karine Chubarian and Gyorgy Turan. Approximating bounded tree-width Bayesian network classifiers with OBDD
- Tjebbe Bodewes and Marco Scutari. Identifiability and Consistency of Bayesian Network Structure Learning from Incomplete Data
- Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Perharz, Thomas Liebig and Kristian Kersting. Conditional Sum-Product Networks: Composing Neural Networks into Probabilistic Tractable Models
- Radim Jiroušek. On a Possibility of Gradual Model-Learning
- Veronica Tozzo, Davide Garbarino and Annalisa Barla. Missing Values in Multiple Joint Inference of Gaussian Graphical Models
- Nils Finke, Marcel Gehrke, Tanya Braun, Tristan Potten and Ralf Möller. Investigating Matureness of Probabilistic Graphical Models for Dry-Bulk Shipping
- Nazanin Tehrani, Nimar Arora, Yucen Li, Kinjal Shah, David Noursi, Michael Tingley, Narjes Torabi, Sepehr Masouleh, Eric Lippert and Erik Meijer. Bean Machine: A Declarative Probabilistic Programming Language For Efficient Programmable Inference
- Linda C. van der Gaag, Silja Renooij and Alessandro Facchini. Building Causal Interaction Models by Recursive Unfolding
- Cory Butz, Jhonatan Oliveira and Robert Peharz. Sum-Product Network Decompilation
- Mattis Hartwig and Ralf Möller. Lifted Query Answering in Gaussian Bayesian Networks
- Evan Dufraisse, Philippe Leray, Raphaël Nedellec and Tarek Benkhelif. Anomaly Detection using Bayesian Network
- Milan Studeny, James Cussens and Vaclav Kratochvil. Dual Formulation of the Chordal Graph Conjecture
- Fabrizio Ventola, Karl Stelzner, Alejandro Molina and Kristian Kersting. Residual Sum-Product Networks
- Pierre Clavier, Olivier Bouaziz and Grégory Nuel. Gaussian Sum-Product Networks Learning in the Presence of Interval Censored Data
- Alessandro Bregoli, Marco Scutari and Fabio Stella. Constraint-Based Learning for Continuous-Time Bayesian Networks
- Tomas Pevny, Vasek Smidl, Martin Trapp, Ondrej Polacek and Tomas Oberhuber. Sum-Product-Transform Networks: Exploiting Symmetries using Invertible Transformations
- George Orfanides and Aritz Pérez. Learning decomposable models by coarsening
- Kiattikun Chobtham and Anthony C. Constantinou. Bayesian network structure learning with causal effects in the presence of latent variables
- Pierre Gillot and Pekka Parviainen. Scalable Bayesian Network Structure Learning via Maximum Acyclic Subgraph
- Topi Talvitie and Pekka Parviainen. Learning Bayesian Networks with Cops and Robbers
- Denis Maua, Heitor Ribeiro, Gustavo Katague and Alessandro Antonucci. Two Reformulation Approaches to Maximum-A-Posteriori Inference in Sum-Product Networks
- Charupriya Sharma, Zhenyu Liao, James Cussens and Peter van Beek. A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations
- Meihua Dang, Antonio Vergari and Guy Van den Broeck. Strudel: Learning Structured-Decomposable Probabilistic Circuits
- Xiufan Yu, Karthikeyan Shanmugam, Debarun Bhattacharjya, Tian Gao, Dharmashankar Subramanian and Lingzhou Xue. Hawkesian Graphical Event Models
- Fan Ding and Yexiang Xue. Contrastive Divergence Learning with Chained Belief Propagation
- Jos van de Wolfshaar and Andrzej Pronobis. Deep Generalized Convolutional Sum-Product Networks
- Aditi Shenvi and Jim Q. Smith. Constructing a Chain Event Graph from a Staged Tree
- Wolfgang Roth and Franz Pernkopf. Differentiable TAN Structure Learning for Bayesian Network Classifiers
- Shouta Sugahara, Itsuki Aomi and Maomi Ueno. Bayesian Network Model Averaging Classifiers by Bagging
- Kari Rantanen, Antti Hyttinen and Matti Järvisalo. Learning Optimal Cyclic Causal Graphs from Interventional Data
- Konrad P. Mielke, Tom Claassen, Mark A.J. Huijbregts, Aafke M. Schipper and Tom M. Heskes. Discovering cause-effect relationships in spatial systems with a known direction based on observational data
- Ondřej Kuželka, Vyacheslav Kungurtsev and Yuyi Wang. Lifted Weight Learning of Markov Logic Networks (Revisited Once More Time)
- Gaspard Ducamp, Philippe Bonnard, Anthony Nouy and Pierre-Henri Wuillemin. An Efficient Low-Rank Tensors Representations for Algorithms in Complex Probabilistic Graphical Models
- Konstantina Biza, Ioannis Tsamardinos and Sofia Triantafillou. Tuning Causal Discovery Algorithms
- Laura Azzimonti, Giorgio Corani and Marco Scutari. Structure Learning from Related Data Sets with a Hierarchical Bayesian Score
- Marco Zaffalon, Alessandro Antonucci and Rafael Cabañas de Paz. Structural Causal Models Are Credal Networks
- Cong Chen, Jiaqi Yang, Chao Chen and Changhe Yuan. Solving Multiple Inference by Minimizing Expected Loss
- Cong Chen, Changhe Yuan and Chao Chen. Efficient Heuristic Search for M-Modes Inference
- Cassio P. de Campos. Almost No News on the Complexity of MAP in Bayesian Networks
- Linda C. van der Gaag and Janneke Bolt. Poset Representations for Sets of Elementary Triplets
- Nandini Ramanan, Mayukh Das, Kristian Kersting and Sriraam Natarajan. Discriminative Non-Parametric Learning of Arithmetic Circuits
- Yizuo Chen, Arthur Choi and Adnan Darwiche. Supervised Learning with Background Knowledge
- Luis Ortiz, Boshen Wang and Ze Gong. Correlated Equilibria for Approximate Variational Inference in MRFs
- Yujia Shen, Arthur Choi and Adnan Darwiche. A New Perspective on Learning Context-Specific Independence