Peter Orbanz

According to our database, Peter Orbanz authored at least 22 papers between 2005 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes.

Hugh Dance, Peter Orbanz, Arthur Gretton

CoRR, 2024

Designing Mechanical Meta-Materials by Learning Equivariant Flows.

Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams

CoRR, 2024

Global optimality under amenable symmetry constraints.

Peter Orbanz

CoRR, 2024

2023

Representing and Learning Functions Invariant Under Crystallographic Groups.

Ryan P. Adams, Peter Orbanz

CoRR, 2023

The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models.

Lee M. Gunderson, Gecia Bravo Hermsdorff, Peter Orbanz

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Quantifying the Effects of Data Augmentation.

Kevin H. Huang, Peter Orbanz, Morgane Austern

CoRR, 2022

2019

Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

Proceedings of the 7th International Conference on Learning Representations, 2019

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data.

Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Compressibility and Generalization in Large-Scale Deep Learning.

Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

CoRR, 2018

2017

Bayesian Nonparametric Models.

Peter Orbanz, Yee Whye Teh

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Preferential Attachment and Vertex Arrival Times.

Benjamin Bloem-Reddy, Peter Orbanz

CoRR, 2017

2015

Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures.

Peter Orbanz, Daniel M. Roy

IEEE Trans. Pattern Anal. Mach. Intell., 2015

2012

Random function priors for exchangeable arrays with applications to graphs and relational data.

James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2010

Bayesian Nonparametric Models.

Peter Orbanz, Yee Whye Teh

Proceedings of the Encyclopedia of Machine Learning, 2010

Dependent Indian Buffet Processes.

Sinead Williamson, Peter Orbanz, Zoubin Ghahramani

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

2009

Construction of Nonparametric Bayesian Models from Parametric Bayes Equations.

Peter Orbanz

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2008

Nonparametric Bayesian Image Segmentation.

Peter Orbanz, Joachim M. Buhmann

Int. J. Comput. Vis., 2008

Music preference learning with partial information.

Yvonne Moh, Peter Orbanz, Joachim M. Buhmann

Proceedings of the IEEE International Conference on Acoustics, 2008

2007

Cluster analysis of heterogeneous rank data.

Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann

Proceedings of the Machine Learning, 2007

Bayesian Order-Adaptive Clustering for Video Segmentation.

Peter Orbanz, Samuel Braendle, Joachim M. Buhmann

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007

2006

Smooth Image Segmentation by Nonparametric Bayesian Inference.

Peter Orbanz, Joachim M. Buhmann

Proceedings of the Computer Vision, 2006

2005

SAR images as mixtures of Gaussian mixtures.

Peter Orbanz, Joachim M. Buhmann

Proceedings of the 2005 International Conference on Image Processing, 2005