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Peter Orbanz
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2020 – today
- 2024
- [i9]Lee M. Gunderson, Gecia Bravo Hermsdorff, Peter Orbanz:
The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models. CoRR abs/2402.00188 (2024) - [i8]Peter Orbanz:
Global optimality under amenable symmetry constraints. CoRR abs/2402.07613 (2024) - [i7]Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams:
Designing Mechanical Meta-Materials by Learning Equivariant Flows. CoRR abs/2410.02385 (2024) - [i6]Hugh Dance, Peter Orbanz, Arthur Gretton:
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes. CoRR abs/2410.14483 (2024) - 2023
- [c11]Lee M. Gunderson, Gecia Bravo Hermsdorff, Peter Orbanz:
The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models. NeurIPS 2023 - [i5]Ryan P. Adams, Peter Orbanz:
Representing and Learning Functions Invariant Under Crystallographic Groups. CoRR abs/2306.05261 (2023) - 2022
- [i4]Kevin H. Huang, Peter Orbanz, Morgane Austern:
Quantifying the Effects of Data Augmentation. CoRR abs/2202.09134 (2022)
2010 – 2019
- 2019
- [c10]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS 2019: 1733-1742 - [c9]Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) 2019 - 2018
- [i3]Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz:
Compressibility and Generalization in Large-Scale Deep Learning. CoRR abs/1804.05862 (2018) - [i2]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR abs/1806.10701 (2018) - 2017
- [r2]Peter Orbanz, Yee Whye Teh:
Bayesian Nonparametric Models. Encyclopedia of Machine Learning and Data Mining 2017: 107-116 - [i1]Benjamin Bloem-Reddy, Peter Orbanz:
Preferential Attachment and Vertex Arrival Times. CoRR abs/1710.02159 (2017) - 2015
- [j2]Peter Orbanz, Daniel M. Roy:
Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 437-461 (2015) - 2012
- [c8]James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy:
Random function priors for exchangeable arrays with applications to graphs and relational data. NIPS 2012: 1007-1015 - 2010
- [c7]Sinead Williamson, Peter Orbanz, Zoubin Ghahramani:
Dependent Indian Buffet Processes. AISTATS 2010: 924-931 - [r1]Peter Orbanz, Yee Whye Teh:
Bayesian Nonparametric Models. Encyclopedia of Machine Learning 2010: 81-89
2000 – 2009
- 2009
- [c6]Peter Orbanz:
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations. NIPS 2009: 1392-1400 - 2008
- [j1]Peter Orbanz, Joachim M. Buhmann:
Nonparametric Bayesian Image Segmentation. Int. J. Comput. Vis. 77(1-3): 25-45 (2008) - [c5]Yvonne Moh, Peter Orbanz, Joachim M. Buhmann:
Music preference learning with partial information. ICASSP 2008: 2021-2024 - 2007
- [c4]Peter Orbanz, Samuel Braendle, Joachim M. Buhmann:
Bayesian Order-Adaptive Clustering for Video Segmentation. EMMCVPR 2007: 334-349 - [c3]Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann:
Cluster analysis of heterogeneous rank data. ICML 2007: 113-120 - 2006
- [c2]Peter Orbanz, Joachim M. Buhmann:
Smooth Image Segmentation by Nonparametric Bayesian Inference. ECCV (1) 2006: 444-457 - 2005
- [c1]Peter Orbanz, Joachim M. Buhmann:
SAR images as mixtures of Gaussian mixtures. ICIP (2) 2005: 209-212
Coauthor Index
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