Learning Accurate Cutset Networks by Exploiting Decomposability. - Nicola Di Mauro; Antonio Vergari; Floriana Esposito (AI*IA, 2015)

Learning Bayesian Random Cutset Forests. - Nicola Di Mauro; Antonio Vergari; Teresa Maria Altomare Basile (ISMIS, 2015)

Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning. - Antonio Vergari; Nicola Di Mauro; Floriana Esposito (ECML/PKDD (2), 2015)

Multi-Label Classification with Cutset Networks. - Nicola Di Mauro; Antonio Vergari; Floriana Esposito (Probabilistic Graphical Models, 2016)

Towards Representation Learning with Tractable Probabilistic Models. - Antonio Vergari; Nicola Di Mauro; Floriana Esposito (CoRR, 2016)

Alternative Variable Splitting Methods to Learn Sum-Product Networks. - Nicola Di Mauro; Floriana Esposito; Fabrizio Giuseppe Ventola; Antonio Vergari (AI*IA, 2017)

Encoding and Decoding Representations with Sum- and Max-Product Networks. - Antonio Vergari; Robert Peharz; Nicola Di Mauro; Floriana Esposito (ICLR (Workshop), 2017)

Density Estimators for Positive-Unlabeled Learning. - Teresa Maria Altomare Basile; Nicola Di Mauro; Floriana Esposito; Stefano Ferilli; Antonio Vergari (NFMCP@PKDD/ECML, 2017)

Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks. - Nicola Di Mauro; Antonio Vergari; Teresa Maria Altomare Basile; Floriana Esposito (ECML/PKDD (1), 2017)

End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification. - Nicola Di Mauro; Antonio Vergari; Teresa Maria Altomare Basile; Fabrizio Giuseppe Ventola; Floriana Esposito (DC@PKDD/ECML, 2017)

Sum-Product Networks for Hybrid Domains. - Alejandro Molina 001; Antonio Vergari; Nicola Di Mauro; Sriraam Natarajan; Floriana Esposito; Kristian Kersting (CoRR, 2017)

Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. - Alejandro Molina 001; Antonio Vergari; Nicola Di Mauro; Sriraam Natarajan; Floriana Esposito; Kristian Kersting (AAAI, 2018)

Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. - Antonio Vergari; Robert Peharz; Nicola Di Mauro; Alejandro Molina 001; Kristian Kersting; Floriana Esposito (AAAI, 2018)

Probabilistic Deep Learning using Random Sum-Product Networks. - Robert Peharz; Antonio Vergari; Karl Stelzner; Alejandro Molina 001; Martin Trapp 001; Kristian Kersting; Zoubin Ghahramani (CoRR, 2018)

Sum-Product Network structure learning by efficient product nodes discovery. - Nicola Di Mauro; Floriana Esposito; Fabrizio Giuseppe Ventola; Antonio Vergari (Intelligenza Artificiale, 2018)

Automatic Bayesian Density Analysis. - Antonio Vergari; Alejandro Molina 001; Robert Peharz; Zoubin Ghahramani; Kristian Kersting; Isabel Valera (AAAI, 2019)

On Tractable Computation of Expected Predictions. - Pasha Khosravi; YooJung Choi 001; Yitao Liang; Antonio Vergari; Guy Van den Broeck (NeurIPS, 2019)

Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. - Robert Peharz; Antonio Vergari; Karl Stelzner; Alejandro Molina 001; Martin Trapp 001; Xiaoting Shao; Kristian Kersting; Zoubin Ghahramani (UAI, 2019)

SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks. - Alejandro Molina 001; Antonio Vergari; Karl Stelzner; Robert Peharz; Pranav Subramani; Nicola Di Mauro; Pascal Poupart; Kristian Kersting (CoRR, 2019)

Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing. - Zhe Zeng; Fanqi Yan; Paolo Morettin; Antonio Vergari; Guy Van den Broeck (CoRR, 2019)

Ensembles of density estimators for positive-unlabeled learning. - Teresa Maria Altomare Basile; Nicola Di Mauro; Floriana Esposito; Stefano Ferilli; Antonio Vergari (J. Intell. Inf. Syst., 2019)

Visualizing and understanding Sum-Product Networks. - Antonio Vergari; Nicola Di Mauro; Floriana Esposito (Mach. Learn., 2019)

Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games. - Alessandro Suglia; Antonio Vergari; Ioannis Konstas; Yonatan Bisk; Emanuele Bastianelli; Andrea Vanzo; Oliver Lemon (COLING, 2020)

From Variational to Deterministic Autoencoders. - Partha Ghosh; Mehdi S. M. Sajjadi; Antonio Vergari; Michael J. Black; Bernhard Schölkopf (ICLR, 2020)

Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. - Robert Peharz; Steven Lang; Antonio Vergari; Karl Stelzner; Alejandro Molina 001; Martin Trapp 001; Guy Van den Broeck; Kristian Kersting; Zoubin Ghahramani (ICML, 2020)

Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. - Zhe Zeng; Paolo Morettin; Fanqi Yan; Antonio Vergari; Guy Van den Broeck (ICML, 2020)

Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. - Zhe Zeng; Paolo Morettin; Fanqi Yan; Antonio Vergari; Guy Van den Broeck (NeurIPS, 2020)

Strudel: Learning Structured-Decomposable Probabilistic Circuits. - Meihua Dang; Antonio Vergari; Guy Van den Broeck (PGM, 2020)

Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. - Xiaoting Shao; Alejandro Molina 001; Antonio Vergari; Karl Stelzner; Robert Peharz; Thomas Liebig; Kristian Kersting (PGM, 2020)

Handling Missing Data in Decision Trees: A Probabilistic Approach. - Pasha Khosravi; Antonio Vergari; YooJung Choi 001; Yitao Liang; Guy Van den Broeck (CoRR, 2020)

Juice: A Julia Package for Logic and Probabilistic Circuits. - Meihua Dang; Pasha Khosravi; Yitao Liang; Antonio Vergari; Guy Van den Broeck (AAAI, 2021)

Neural Concept Formation in Knowledge Graphs. - Agnieszka Dobrowolska; Antonio Vergari; Pasquale Minervini (AKBC, 2021)

An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games. - Alessandro Suglia; Yonatan Bisk; Ioannis Konstas; Antonio Vergari; Emanuele Bastianelli; Andrea Vanzo; Oliver Lemon (EACL, 2021)

A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. - Antonio Vergari; YooJung Choi 001; Anji Liu; Stefano Teso; Guy Van den Broeck (NeurIPS, 2021)

Tractable computation of expected kernels. - Wenzhe Li; Zhe Zeng; Antonio Vergari; Guy Van den Broeck (UAI, 2021)

A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. - Antonio Vergari; YooJung Choi 001; Anji Liu; Stefano Teso; Guy Van den Broeck (CoRR, 2021)

Tractable Computation of Expected Kernels by Circuits. - Wenzhe Li; Zhe Zeng; Antonio Vergari; Guy Van den Broeck (CoRR, 2021)

Semantic Probabilistic Layers for Neuro-Symbolic Learning. - Kareem Ahmed; Stefano Teso; Kai-Wei Chang; Guy Van den Broeck; Antonio Vergari (NeurIPS, 2022)

Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs. - Stefano Teso; Antonio Vergari (CoRR, 2022)

Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161). - Priyank Jaini; Kristian Kersting; Antonio Vergari; Max Welling (Dagstuhl Reports, 2022)

Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits. - Meihua Dang; Antonio Vergari; Guy Van den Broeck (Int. J. Approx. Reason., 2022)

Conditional sum-product networks: Modular probabilistic circuits via gate functions. - Xiaoting Shao; Alejandro Molina 001; Antonio Vergari; Karl Stelzner; Robert Peharz; Thomas Liebig; Kristian Kersting (Int. J. Approx. Reason., 2022)

How to Turn Your Knowledge Graph Embeddings into Generative Models. - Lorenzo Loconte; Nicola Di Mauro; Robert Peharz; Antonio Vergari (NeurIPS, 2023)

Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. - Emanuele Marconato; Stefano Teso; Antonio Vergari; Andrea Passerini (NeurIPS, 2023)

How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits. - Lorenzo Loconte; Nicola Di Mauro; Robert Peharz; Antonio Vergari (CoRR, 2023)

Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks. - Aryo Pradipta Gema; Dominik Grabarczyk; Wolf De Wulf; Piyush Borole; Javier Antonio Alfaro; Pasquale Minervini; Antonio Vergari; Ajitha Rajan (CoRR, 2023)

Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning. - Filippo Corponi; Bryan M. Li; Gerard Anmella; Clàudia Valenzuela-Pascual; Ariadna Mas; Isabella Pacchiarotti; Marc Valentí; Iria Grande; Antonio Benabarre; Marina Garriga; Eduard Vieta; Allan H. Young; et al. (CoRR, 2023)

Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. - Andreas Grivas; Antonio Vergari; Adam Lopez (AAAI, 2024)

PIXAR: Auto-Regressive Language Modeling in Pixel Space. - Yintao Tai; Xiyang Liao; Alessandro Suglia; Antonio Vergari (ACL (Findings), 2024)

Probabilistic Integral Circuits. - Gennaro Gala; Cassio P. de Campos; Robert Peharz; Antonio Vergari; Erik Quaeghebeur (AISTATS, 2024)

Subtractive Mixture Models via Squaring: Representation and Learning. - Lorenzo Loconte; Aleksanteri M. Sladek; Stefan Mengel; Martin Trapp 001; Arno Solin; Nicolas Gillis; Antonio Vergari (ICLR, 2024)

On the Independence Assumption in Neurosymbolic Learning. - Emile van Krieken; Pasquale Minervini; Edoardo M. Ponti; Antonio Vergari (ICML, 2024)

ChemAlgebra: Algebraic Reasoning on Chemical Reactions. - Andrea Valenti; Davide Bacciu; Antonio Vergari (IJCNN, 2024)

A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcuts. - Samuele Bortolotti; Emanuele Marconato; Tommaso Carraro; Paolo Morettin; Emile van Krieken; Antonio Vergari; Stefano Teso; Andrea Passerini (NeurIPS, 2024)

Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits. - Gennaro Gala; Cassio P. de Campos; Antonio Vergari; Erik Quaeghebeur (NeurIPS, 2024)

BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. - Emanuele Marconato; Samuele Bortolotti; Emile van Krieken; Antonio Vergari; Andrea Passerini; Stefano Teso (UAI, 2024)

Towards Logically Consistent Language Models via Probabilistic Reasoning. - Diego Calanzone; Stefano Teso; Antonio Vergari (CoRR, 2024)

A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. - Samuele Bortolotti; Emanuele Marconato; Tommaso Carraro; Paolo Morettin; Emile van Krieken; Antonio Vergari; Stefano Teso; Andrea Passerini (CoRR, 2024)

Is Complex Query Answering Really Complex? - Cosimo Gregucci; Bo Xiong; Daniel Hernández 002; Lorenzo Loconte; Pasquale Minervini; Steffen Staab; Antonio Vergari (CoRR, 2024)

On Faster Marginalization with Squared Circuits via Orthonormalization. - Lorenzo Loconte; Antonio Vergari (CoRR, 2024)

A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. - Samuele Bortolotti; Emanuele Marconato; Tommaso Carraro; Paolo Morettin; Emile van Krieken; Antonio Vergari; Stefano Teso; Andrea Passerini (Zenodo, 2024)

From MNIST to ImageNet and back: benchmarking continual curriculum learning. - Kamil Faber; Dominik Zurek; Marcin Pietron; Nathalie Japkowicz; Antonio Vergari; Roberto Corizzo (Mach. Learn., 2024)

Sum of Squares Circuits. - Lorenzo Loconte; Stefan Mengel; Antonio Vergari (AAAI, 2025)

Logically Consistent Language Models via Neuro-Symbolic Integration. - Diego Calanzone; Stefano Teso; Antonio Vergari (ICLR, 2025)

What can Large Language Models Capture about Code Functional Equivalence? - Nickil Maveli; Antonio Vergari; Shay B. Cohen (NAACL (Findings), 2025)

A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction. - Leander Kurscheidt; Paolo Morettin; Roberto Sebastiani; Andrea Passerini; Antonio Vergari (UAI, 2025)

What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? - Lorenzo Loconte; Antonio Mari; Gennaro Gala; Robert Peharz; Cassio de Campos; Erik Quaeghebeur; Gennaro Vessio; Antonio Vergari (Trans. Mach. Learn. Res., 2025)

COPA: Comparing the Incomparable to Explore the Pareto Front. - Adrián Javaloy; Antonio Vergari; Isabel Valera (CoRR, 2025)

Scalable Expectation Estimation with Subtractive Mixture Models. - Lena Zellinger; Nicola Branchini; Víctor Elvira; Antonio Vergari (CoRR, 2025)

Neurosymbolic Diffusion Models. - Emile van Krieken; Pasquale Minervini; Edoardo M. Ponti; Antonio Vergari (CoRR, 2025)

SEMMA: A Semantic Aware Knowledge Graph Foundation Model. - Arvindh Arun; Sumit Kumar; Mojtaba Nayyeri; Bo Xiong; Ponnurangam Kumaraguru; Antonio Vergari; Steffen Staab (CoRR, 2025)

Tractable Representation Learning with Probabilistic Circuits. - Steven Braun; Sahil Sidheekh; Antonio Vergari; Martin Mundt; Sriraam Natarajan; Kristian Kersting (CoRR, 2025)

Neurosymbolic Reasoning Shortcuts under the Independence Assumption. - Emile van Krieken; Pasquale Minervini; Edoardo Maria Ponti; Antonio Vergari (CoRR, 2025)