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ORCIDAntonio Vergari
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- affiliation: University of Edinburgh, UK
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2020 – today
- 2024
[j6]Kamil Faber, Dominik Zurek, Marcin Pietron, Nathalie Japkowicz, Antonio Vergari, Roberto Corizzo
:
From MNIST to ImageNet and back: benchmarking continual curriculum learning. Mach. Learn. 113(10): 8137-8164 (2024)- [c36]Andreas Grivas, Antonio Vergari, Adam Lopez:
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. AAAI 2024: 12208-12216 - [c35]Yintao Tai, Xiyang Liao, Alessandro Suglia, Antonio Vergari:
PIXAR: Auto-Regressive Language Modeling in Pixel Space. ACL (Findings) 2024: 14673-14695 - [c34]Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur:
Probabilistic Integral Circuits. AISTATS 2024: 2143-2151 - [c33]Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, Antonio Vergari:
Subtractive Mixture Models via Squaring: Representation and Learning. ICLR 2024 - [c32]Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari:
On the Independence Assumption in Neurosymbolic Learning. ICML 2024 - [c31]Andrea Valenti, Davide Bacciu, Antonio Vergari:
ChemAlgebra: Algebraic Reasoning on Chemical Reactions. IJCNN 2024: 1-8 - [i40]Yintao Tai, Xiyang Liao, Alessandro Suglia, Antonio Vergari:
PIXAR: Auto-Regressive Language Modeling in Pixel Space. CoRR abs/2401.03321 (2024) - [i39]Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso:
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. CoRR abs/2402.12240 (2024) - [i38]Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari:
On the Independence Assumption in Neurosymbolic Learning. CoRR abs/2404.08458 (2024) - [i37]Diego Calanzone, Stefano Teso, Antonio Vergari:
Towards Logically Consistent Language Models via Probabilistic Reasoning. CoRR abs/2404.12843 (2024) - [i36]Gennaro Gala, Cassio de Campos, Antonio Vergari, Erik Quaeghebeur:
Scaling Continuous Latent Variable Models as Probabilistic Integral Circuits. CoRR abs/2406.06494 (2024) - [i35]Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini:
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. CoRR abs/2406.10368 (2024) - [i34]Nickil Maveli, Antonio Vergari, Shay B. Cohen:
What can Large Language Models Capture about Code Functional Equivalence? CoRR abs/2408.11081 (2024) - [i33]Lorenzo Loconte, Stefan Mengel, Antonio Vergari:
Sum of Squares Circuits. CoRR abs/2408.11778 (2024) - [i32]Lorenzo Loconte, Antonio Mari, Gennaro Gala, Robert Peharz, Cassio de Campos, Erik Quaeghebeur, Gennaro Vessio, Antonio Vergari:
What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)? CoRR abs/2409.07953 (2024) - [i31]Diego Calanzone, Stefano Teso, Antonio Vergari:
Logically Consistent Language Models via Neuro-Symbolic Integration. CoRR abs/2409.13724 (2024) - 2023
- [c30]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeSy 2023: 413 - [c29]Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari:
How to Turn Your Knowledge Graph Embeddings into Generative Models. NeurIPS 2023 - [c28]Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini:
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. NeurIPS 2023 - [i30]Kamil Faber, Dominik Zurek, Marcin Pietron, Nathalie Japkowicz, Antonio Vergari, Roberto Corizzo:
From MNIST to ImageNet and Back: Benchmarking Continual Curriculum Learning. CoRR abs/2303.11076 (2023) - [i29]Lorenzo Loconte, Nicola Di Mauro, Robert Peharz, Antonio Vergari:
How to Turn Your Knowledge Graph Embeddings into Generative Models via Probabilistic Circuits. CoRR abs/2305.15944 (2023) - [i28]Emanuele Marconato, Stefano Teso, Antonio Vergari, Andrea Passerini:
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts. CoRR abs/2305.19951 (2023) - [i27]Aryo Pradipta Gema, Dominik Grabarczyk, Wolf De Wulf, Piyush Borole, Javier Antonio Alfaro, Pasquale Minervini, Antonio Vergari, Ajitha Rajan:
Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks. CoRR abs/2305.19979 (2023) - [i26]Lorenzo Loconte, Aleksanteri M. Sladek, Stefan Mengel, Martin Trapp, Arno Solin, Nicolas Gillis, Antonio Vergari:
Subtractive Mixture Models via Squaring: Representation and Learning. CoRR abs/2310.00724 (2023) - [i25]Andreas Grivas, Antonio Vergari, Adam Lopez:
Taming the Sigmoid Bottleneck: Provably Argmaxable Sparse Multi-Label Classification. CoRR abs/2310.10443 (2023) - [i24]Gennaro Gala, Cassio P. de Campos, Robert Peharz, Antonio Vergari, Erik Quaeghebeur:
Probabilistic Integral Circuits. CoRR abs/2310.16986 (2023) - [i23]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, Stephen M. Lawrie, Heather C. Whalley, Diego Hidalgo-Mazzei, Antonio Vergari:
Wearable data from subjects playing Super Mario, sitting university exams, or performing physical exercise help detect acute mood episodes via self-supervised learning. CoRR abs/2311.04215 (2023) - 2022
- [j5]Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: A fast and accurate learner of structured-decomposable probabilistic circuits. Int. J. Approx. Reason. 140: 92-115 (2022) - [j4]Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional sum-product networks: Modular probabilistic circuits via gate functions. Int. J. Approx. Reason. 140: 298-313 (2022) - [c27]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. NeurIPS 2022 - [i22]Stefano Teso, Antonio Vergari:
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs. CoRR abs/2202.08566 (2022) - [i21]Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. CoRR abs/2206.00426 (2022) - [i20]Andrea Valenti, Davide Bacciu, Antonio Vergari:
ChemAlgebra: Algebraic Reasoning on Chemical Reactions. CoRR abs/2210.02095 (2022) - [i19]Priyank Jaini, Kristian Kersting, Antonio Vergari, Max Welling:
Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161). Dagstuhl Reports 12(4): 13-25 (2022) - 2021
- [c26]Meihua Dang, Pasha Khosravi, Yitao Liang, Antonio Vergari, Guy Van den Broeck:
Juice: A Julia Package for Logic and Probabilistic Circuits. AAAI 2021: 16020-16023 - [c25]Agnieszka Dobrowolska, Antonio Vergari, Pasquale Minervini:
Neural Concept Formation in Knowledge Graphs. AKBC 2021 - [c24]Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon:
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games. EACL 2021: 2135-2144 - [c23]Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck:
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference. NeurIPS 2021: 13189-13201 - [c22]Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck:
Tractable computation of expected kernels. UAI 2021: 1163-1173 - [i18]Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon:
An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games. CoRR abs/2102.00424 (2021) - [i17]Antonio Vergari, YooJung Choi, Anji Liu, Stefano Teso, Guy Van den Broeck:
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries. CoRR abs/2102.06137 (2021) - [i16]Wenzhe Li, Zhe Zeng, Antonio Vergari, Guy Van den Broeck:
Tractable Computation of Expected Kernels by Circuits. CoRR abs/2102.10562 (2021) - 2020
- [c21]Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon:
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games. COLING 2020: 1090-1102 - [c20]Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf:
From Variational to Deterministic Autoencoders. ICLR 2020 - [c19]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. ICML 2020: 7563-7574 - [c18]Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. ICML 2020: 10990-11000 - [c17]Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations. NeurIPS 2020 - [c16]Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: Learning Structured-Decomposable Probabilistic Circuits. PGM 2020: 137-148 - [c15]Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. PGM 2020: 401-412 - [i15]Zhe Zeng, Paolo Morettin, Fanqi Yan, Antonio Vergari, Guy Van den Broeck:
Scaling up Hybrid Probabilistic Inference with Logical and Arithmetic Constraints via Message Passing. CoRR abs/2003.00126 (2020) - [i14]Robert Peharz, Steven Lang, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Guy Van den Broeck, Kristian Kersting, Zoubin Ghahramani:
Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuits. CoRR abs/2004.06231 (2020) - [i13]Pasha Khosravi, Antonio Vergari, YooJung Choi, Yitao Liang, Guy Van den Broeck:
Handling Missing Data in Decision Trees: A Probabilistic Approach. CoRR abs/2006.16341 (2020) - [i12]Meihua Dang, Antonio Vergari, Guy Van den Broeck:
Strudel: Learning Structured-Decomposable Probabilistic Circuits. CoRR abs/2007.09331 (2020) - [i11]Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon:
Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games. CoRR abs/2011.02917 (2020)
2010 – 2019
- 2019
- [j3]Teresa Maria Altomare Basile, Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, Antonio Vergari:
Ensembles of density estimators for positive-unlabeled learning. J. Intell. Inf. Syst. 53(2): 199-217 (2019) - [j2]Antonio Vergari, Nicola Di Mauro, Floriana Esposito:
Visualizing and understanding Sum-Product Networks. Mach. Learn. 108(4): 551-573 (2019) - [c14]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. AAAI 2019: 5207-5215 - [c13]Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck:
On Tractable Computation of Expected Predictions. NeurIPS 2019: 11167-11178 - [c12]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Xiaoting Shao, Kristian Kersting, Zoubin Ghahramani:
Random Sum-Product Networks: A Simple and Effective Approach to Probabilistic Deep Learning. UAI 2019: 334-344 - [i10]Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Pranav Subramani, Nicola Di Mauro, Pascal Poupart, Kristian Kersting:
SPFlow: An Easy and Extensible Library for Deep Probabilistic Learning using Sum-Product Networks. CoRR abs/1901.03704 (2019) - [i9]Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael J. Black, Bernhard Schölkopf:
From Variational to Deterministic Autoencoders. CoRR abs/1903.12436 (2019) - [i8]Xiaoting Shao, Alejandro Molina, Antonio Vergari, Karl Stelzner, Robert Peharz, Thomas Liebig, Kristian Kersting:
Conditional Sum-Product Networks: Imposing Structure on Deep Probabilistic Architectures. CoRR abs/1905.08550 (2019) - [i7]Zhe Zeng, Fanqi Yan, Paolo Morettin, Antonio Vergari, Guy Van den Broeck:
Hybrid Probabilistic Inference with Logical Constraints: Tractability and Message Passing. CoRR abs/1909.09362 (2019) - [i6]Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck:
On Tractable Computation of Expected Predictions. CoRR abs/1910.02182 (2019) - 2018
- [j1]Nicola Di Mauro, Floriana Esposito, Fabrizio Giuseppe Ventola, Antonio Vergari:
Sum-Product Network structure learning by efficient product nodes discovery. Intelligenza Artificiale 12(2): 143-159 (2018) - [c11]Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting:
Mixed Sum-Product Networks: A Deep Architecture for Hybrid Domains. AAAI 2018: 3828-3835 - [c10]Antonio Vergari, Robert Peharz, Nicola Di Mauro, Alejandro Molina, Kristian Kersting, Floriana Esposito:
Sum-Product Autoencoding: Encoding and Decoding Representations Using Sum-Product Networks. AAAI 2018: 4163-4170 - [i5]Robert Peharz, Antonio Vergari, Karl Stelzner, Alejandro Molina, Martin Trapp, Kristian Kersting, Zoubin Ghahramani:
Probabilistic Deep Learning using Random Sum-Product Networks. CoRR abs/1806.01910 (2018) - [i4]Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera:
Automatic Bayesian Density Analysis. CoRR abs/1807.09306 (2018) - 2017
- [c9]Nicola Di Mauro, Floriana Esposito, Fabrizio Giuseppe Ventola, Antonio Vergari:
Alternative Variable Splitting Methods to Learn Sum-Product Networks. AI*IA 2017: 334-346 - [c8]Antonio Vergari, Robert Peharz, Nicola Di Mauro, Floriana Esposito:
Encoding and Decoding Representations with Sum- and Max-Product Networks. ICLR (Workshop) 2017 - [c7]Teresa Maria Altomare Basile, Nicola Di Mauro, Floriana Esposito, Stefano Ferilli, Antonio Vergari:
Density Estimators for Positive-Unlabeled Learning. NFMCP@PKDD/ECML 2017: 49-64 - [c6]Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Floriana Esposito:
Fast and Accurate Density Estimation with Extremely Randomized Cutset Networks. ECML/PKDD (1) 2017: 203-219 - [c5]Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile, Fabrizio Giuseppe Ventola, Floriana Esposito:
End-to-end Learning of Deep Spatio-temporal Representations for Satellite Image Time Series Classification. DC@PKDD/ECML 2017 - [i3]Alejandro Molina, Antonio Vergari, Nicola Di Mauro, Sriraam Natarajan, Floriana Esposito, Kristian Kersting:
Sum-Product Networks for Hybrid Domains. CoRR abs/1710.03297 (2017) - 2016
- [c4]Nicola Di Mauro, Antonio Vergari, Floriana Esposito:
Multi-Label Classification with Cutset Networks. Probabilistic Graphical Models 2016: 147-158 - [i2]Antonio Vergari, Nicola Di Mauro, Floriana Esposito:
Towards Representation Learning with Tractable Probabilistic Models. CoRR abs/1608.02341 (2016) - [i1]Antonio Vergari, Nicola Di Mauro, Floriana Esposito:
Visualizing and Understanding Sum-Product Networks. CoRR abs/1608.08266 (2016) - 2015
- [c3]Nicola Di Mauro, Antonio Vergari, Floriana Esposito:
Learning Accurate Cutset Networks by Exploiting Decomposability. AI*IA 2015: 221-232 - [c2]Nicola Di Mauro, Antonio Vergari, Teresa Maria Altomare Basile:
Learning Bayesian Random Cutset Forests. ISMIS 2015: 122-132 - [c1]Antonio Vergari, Nicola Di Mauro, Floriana Esposito:
Simplifying, Regularizing and Strengthening Sum-Product Network Structure Learning. ECML/PKDD (2) 2015: 343-358
Coauthor Index
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last updated on 2024-11-07 20:33 CET by the dblp team
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