| 2025 | ICLR | Designing Mechanical Meta-Materials by Learning Equivariant Flows. | Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams |
| 2025 | ICML | Efficiently Vectorized MCMC on Modern Accelerators. | Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P. Adams |
| 2025 | ICML | Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrdinger Equation. | Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams |
| 2025 | ICML | Distinguishing Cause from Effect with Causal Velocity Models. | Johnny Xi, Hugh Dance, Peter Orbanz, Benjamin Bloem-Reddy |
| 2019 | AISTATS | Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. | Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz |
| 2019 | ICLR | Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. | Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz |
| 2008 | ICASSP | Music preference learning with partial information. | Yvonne Moh, Peter Orbanz, Joachim M. Buhmann |
| 2007 | ICML | Cluster analysis of heterogeneous rank data. | Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann |
| 2006 | ECCV | Smooth Image Segmentation by Nonparametric Bayesian Inference. | Peter Orbanz, Joachim M. Buhmann |
| 2005 | ICIP | SAR images as mixtures of Gaussian mixtures. | Peter Orbanz, Joachim M. Buhmann |