SAR images as mixtures of Gaussian mixtures. - Peter Orbanz; Joachim M. Buhmann (ICIP (2), 2005)

Smooth Image Segmentation by Nonparametric Bayesian Inference. - Peter Orbanz; Joachim M. Buhmann (ECCV (1), 2006)

Bayesian Order-Adaptive Clustering for Video Segmentation. - Peter Orbanz; Samuel Braendle; Joachim M. Buhmann (EMMCVPR, 2007)

Cluster analysis of heterogeneous rank data. - Ludwig M. Busse; Peter Orbanz; Joachim M. Buhmann (ICML, 2007)

Music preference learning with partial information. - Yvonne Moh; Peter Orbanz; Joachim M. Buhmann (ICASSP, 2008)

Nonparametric Bayesian Image Segmentation. - Peter Orbanz; Joachim M. Buhmann (Int. J. Comput. Vis., 2008)

Construction of Nonparametric Bayesian Models from Parametric Bayes Equations. - Peter Orbanz (NIPS, 2009)

Dependent Indian Buffet Processes. - Sinead Williamson; Peter Orbanz; Zoubin Ghahramani (AISTATS, 2010)

Bayesian Nonparametric Models. - Peter Orbanz; Yee Whye Teh (Encyclopedia of Machine Learning, 2010)

Random function priors for exchangeable arrays with applications to graphs and relational data. - James Robert Lloyd; Peter Orbanz; Zoubin Ghahramani; Daniel M. Roy 001 (NIPS, 2012)

Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. - Peter Orbanz; Daniel M. Roy 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2015)

Preferential Attachment and Vertex Arrival Times. - Benjamin Bloem-Reddy; Peter Orbanz (CoRR, 2017)

Bayesian Nonparametric Models. - Peter Orbanz; Yee Whye Teh (Encyclopedia of Machine Learning and Data Mining, 2017)

Compressibility and Generalization in Large-Scale Deep Learning. - Wenda Zhou; Victor Veitch; Morgane Austern; Ryan P. Adams; Peter Orbanz (CoRR, 2018)

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. - Victor Veitch; Morgane Austern; Wenda Zhou; David M. Blei; Peter Orbanz (AISTATS, 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 (ICLR (Poster), 2019)

Quantifying the Effects of Data Augmentation. - Kevin Han Huang; Peter Orbanz; Morgane Austern (CoRR, 2022)

The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models. - Lee M. Gunderson; Gecia Bravo Hermsdorff; Peter Orbanz (NeurIPS, 2023)

Representing and Learning Functions Invariant Under Crystallographic Groups. - Ryan P. Adams; Peter Orbanz (CoRR, 2023)

Global optimality under amenable symmetry constraints. - Peter Orbanz (CoRR, 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 (ICLR, 2025)

Distinguishing Cause from Effect with Causal Velocity Models. - Johnny Xi; Hugh Dance; Peter Orbanz; Benjamin Bloem-Reddy (CoRR, 2025)

Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. - Kevin Han Huang; Ni Zhan 001; Elif Ertekin; Peter Orbanz; Ryan P. Adams (CoRR, 2025)

Efficiently Vectorized MCMC on Modern Accelerators. - Hugh Dance; Pierre Glaser; Peter Orbanz; Ryan P. Adams (CoRR, 2025)