A factorial sparse coder model for single channel source separation. - Robert Peharz; Michael Stark 004; Franz Pernkopf; Yannis Stylianou (INTERSPEECH, 2010)

Gain-robust multi-pitch tracking using sparse nonnegative matrix factorization. - Robert Peharz; Michael Wohlmayr; Franz Pernkopf (ICASSP, 2011)

Efficient implementation of probabilistic multi-pitch tracking. - Michael Wohlmayr; Robert Peharz; Franz Pernkopf (ICASSP, 2011)

On linear and mixmax interaction models for single channel source separation. - Robert Peharz; Franz Pernkopf (ICASSP, 2012)

Exact Maximum Margin Structure Learning of Bayesian Networks. - Robert Peharz; Franz Pernkopf (ICML, 2012)

Sparse nonnegative matrix factorization with ℓ0-constraints. - Robert Peharz; Franz Pernkopf (Neurocomputing, 2012)

The Most Generative Maximum Margin Bayesian Networks. - Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf (ICML (3), 2013)

Greedy Part-Wise Learning of Sum-Product Networks. - Robert Peharz; Bernhard C. Geiger; Franz Pernkopf (ECML/PKDD (2), 2013)

Modeling speech with sum-product networks: Application to bandwidth extension. - Robert Peharz; Georg Kapeller; Pejman Mowlaee; Franz Pernkopf (ICASSP, 2014)

On Theoretical Properties of Sum-Product Networks. - Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf; Pedro M. Domingos (AISTATS, 2015)

On representation learning for artificial bandwidth extension. - Matthias Zöhrer; Robert Peharz; Franz Pernkopf (INTERSPEECH, 2015)

Representation Learning for Single-Channel Source Separation and Bandwidth Extension. - Matthias Zöhrer; Robert Peharz; Franz Pernkopf (IEEE ACM Trans. Audio Speech Lang. Process., 2015)

Manual versus Automated: The Challenging Routine of Infant Vocalisation Segmentation in Home Videos to Study Neuro(mal)development. - Florian B. Pokorny; Robert Peharz; Wolfgang Roth; Matthias Zöhrer; Franz Pernkopf; Peter B. Marschik; Björn W. Schuller (INTERSPEECH, 2016)

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

Safe Semi-Supervised Learning of Sum-Product Networks. - Martin Trapp 001; Tamas Madl; Robert Peharz; Franz Pernkopf; Robert Trappl (UAI, 2017)

On the Latent Variable Interpretation in Sum-Product Networks. - Robert Peharz; Robert Gens; Franz Pernkopf; Pedro M. Domingos (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

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)

Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks. - Martin Trapp 001; Robert Peharz; Carl E. Rasmussen; Franz Pernkopf (CoRR, 2018)

Efficient and Robust Machine Learning for Real-World Systems. - Franz Pernkopf; Wolfgang Roth; Matthias Zöhrer; Lukas Pfeifenberger; Günther Schindler; Holger Fröning; Sebastian Tschiatschek; Robert Peharz; Matthew Mattina; Zoubin Ghahramani (CoRR, 2018)

Hybrid generative-discriminative training of Gaussian mixture models. - Wolfgang Roth; Robert Peharz; Sebastian Tschiatschek; Franz Pernkopf (Pattern Recognit. Lett., 2018)

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

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters. - Marton Havasi; Robert Peharz; José Miguel Hernández-Lobato (ICLR (Poster), 2019)

Faster Attend-Infer-Repeat with Tractable Probabilistic Models. - Karl Stelzner; Robert Peharz; Kristian Kersting (ICML, 2019)

Hierarchical Decompositional Mixtures of Variational Autoencoders. - Ping Liang Tan; Robert Peharz (ICML, 2019)

Bayesian Learning of Sum-Product Networks. - Martin Trapp 001; Robert Peharz; Hong Ge; Franz Pernkopf; Zoubin Ghahramani (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)

Optimisation of Overparametrized Sum-Product Networks. - Martin Trapp 001; Robert Peharz; Franz Pernkopf (CoRR, 2019)

Deep Structured Mixtures of Gaussian Processes. - Martin Trapp 001; Robert Peharz; Franz Pernkopf; Carl Edward Rasmussen (AISTATS, 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)

Joints in Random Forests. - Alvaro H. C. Correia; Robert Peharz; Cassio P. de Campos (NeurIPS, 2020)

Sum-Product Network Decompilation. - Cory J. Butz; Jhonatan de S. Oliveira; Robert Peharz (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)

PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data. - Ricky Maulana Fajri; Samaneh Khoshrou; Robert Peharz; Mykola Pechenizkiy (ECML/PKDD (5), 2020)

Resource-Efficient Neural Networks for Embedded Systems. - Wolfgang Roth; Günther Schindler; Matthias Zöhrer; Lukas Pfeifenberger; Robert Peharz; Sebastian Tschiatschek; Holger Fröning; Franz Pernkopf; Zoubin Ghahramani (CoRR, 2020)

Towards Robust Classification with Deep Generative Forests. - Alvaro H. C. Correia; Robert Peharz; Cassio P. de Campos (CoRR, 2020)

Elevating Perceptual Sample Quality in PCs through Differentiable Sampling. - Steven Lang; Martin Mundt; Fabrizio Ventola; Robert Peharz; Kristian Kersting (Pre-Registration Workshop @ NeurIPS, 2021)

Active Bayesian Causal Inference. - Christian Toth; Lars Lorch; Christian Knoll 002; Andreas Krause 001; Franz Pernkopf; Robert Peharz; Julius von Kügelgen (NeurIPS, 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)

Continuous Mixtures of Tractable Probabilistic Models. - Alvaro H. C. Correia; Gennaro Gala; Erik Quaeghebeur; Cassio P. de Campos; Robert Peharz (AAAI, 2023)

Bayesian Structure Scores for Probabilistic Circuits. - Yang Yang; Gennaro Gala; Robert Peharz (AISTATS, 2023)

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

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

Exact Soft Analytical Side-Channel Attacks using Tractable Circuits. - Thomas Wedenig; Rishub Nagpal; Gaëtan Cassiers; Stefan Mangard; Robert Peharz (ICML, 2024)

Rao-Blackwellising Bayesian Causal Inference. - Christian Toth; Christian Knoll 002; Franz Pernkopf; Robert Peharz (CoRR, 2024)

One-Shot Federated Learning with Bayesian Pseudocoresets. - Tim D'Hondt; Mykola Pechenizkiy; Robert Peharz (CoRR, 2024)

Resource-Efficient Neural Networks for Embedded Systems. - Wolfgang Roth; Günther Schindler; Bernhard Klein; Robert Peharz; Sebastian Tschiatschek; Holger Fröning; Franz Pernkopf; Zoubin Ghahramani (J. Mach. Learn. Res., 2024)

Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orders. - Christian Toth; Christian Knoll 002; Franz Pernkopf; Robert Peharz (AISTATS, 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)

Deep Polynomial Chaos Expansion. - Johannes Exenberger; Sascha Ranftl; Robert Peharz (CoRR, 2025)