Peter Orbanz - RankMe

32 papers A* 6A 2Misc 1Journal 15Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Single Architecture for Representing Invariance Under Any Space Group. CoRR Cindy Y. Zhang, Elif Ertekin, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Designing Mechanical Meta-Materials by Learning Equivariant Flows. ICLR Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. ICML Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams
2025	J	`jnl`	Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation. CoRR Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams
2025	A*	`conf`	Distinguishing Cause from Effect with Causal Velocity Models. ICML Johnny Xi, Hugh Dance, Peter Orbanz, Benjamin Bloem-Reddy
2025	J	`jnl`	Distinguishing Cause from Effect with Causal Velocity Models. CoRR Johnny Xi, Hugh Dance, Peter Orbanz, Benjamin Bloem-Reddy
2025	A*	`conf`	Efficiently Vectorized MCMC on Modern Accelerators. ICML Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P. Adams
2025	J	`jnl`	Efficiently Vectorized MCMC on Modern Accelerators. CoRR Hugh Dance, Pierre Glaser, Peter Orbanz, Ryan P. Adams
2024	J	`jnl`	Designing Mechanical Meta-Materials by Learning Equivariant Flows. CoRR Mehran Mirramezani, Anne S. Meeussen, Katia Bertoldi, Peter Orbanz, Ryan P. Adams
2024	J	`jnl`	Global optimality under amenable symmetry constraints. CoRR Peter Orbanz
2024	J	`jnl`	Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes. CoRR Hugh Dance, Peter Orbanz, Arthur Gretton
2024	J	`jnl`	The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models. CoRR Lee M. Gunderson, Gecia Bravo Hermsdorff, Peter Orbanz
2023	J	`jnl`	Representing and Learning Functions Invariant Under Crystallographic Groups. CoRR Ryan P. Adams, Peter Orbanz
2023	A*	`conf`	The Graph Pencil Method: Mapping Subgraph Densities to Stochastic Block Models. NeurIPS Lee M. Gunderson, Gecia Bravo Hermsdorff, Peter Orbanz
2022	J	`jnl`	Quantifying the Effects of Data Augmentation. CoRR Kevin Han Huang, Peter Orbanz, Morgane Austern
2019	A	`conf`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2019	—	`conf`	Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. ICLR (Poster) Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2018	J	`jnl`	Compressibility and Generalization in Large-Scale Deep Learning. CoRR Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz
2018	J	`jnl`	Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz
2017	—	`ch.`	Bayesian Nonparametric Models. Encyclopedia of Machine Learning and Data Mining Peter Orbanz, Yee Whye Teh
2017	J	`jnl`	Preferential Attachment and Vertex Arrival Times. CoRR Benjamin Bloem-Reddy, Peter Orbanz
2015	J	`jnl`	Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures. IEEE Trans. Pattern Anal. Mach. Intell. Peter Orbanz, Daniel M. Roy
2012	—	`conf`	Random function priors for exchangeable arrays with applications to graphs and relational data. NIPS James Robert Lloyd, Peter Orbanz, Zoubin Ghahramani, Daniel M. Roy
2010	—	`ch.`	Bayesian Nonparametric Models. Encyclopedia of Machine Learning Peter Orbanz, Yee Whye Teh
2010	A	`conf`	Dependent Indian Buffet Processes. AISTATS Sinead Williamson, Peter Orbanz, Zoubin Ghahramani
2009	—	`conf`	Construction of Nonparametric Bayesian Models from Parametric Bayes Equations. NIPS Peter Orbanz
2008	Misc	`conf`	Music preference learning with partial information. ICASSP Yvonne Moh, Peter Orbanz, Joachim M. Buhmann
2008	J	`jnl`	Nonparametric Bayesian Image Segmentation. Int. J. Comput. Vis. Peter Orbanz, Joachim M. Buhmann
2007	—	`conf`	Bayesian Order-Adaptive Clustering for Video Segmentation. EMMCVPR Peter Orbanz, Samuel Braendle, Joachim M. Buhmann
2007	A*	`conf`	Cluster analysis of heterogeneous rank data. ICML Ludwig M. Busse, Peter Orbanz, Joachim M. Buhmann
2006	—	`conf`	Smooth Image Segmentation by Nonparametric Bayesian Inference. ECCV (1) Peter Orbanz, Joachim M. Buhmann
2005	—	`conf`	SAR images as mixtures of Gaussian mixtures. ICIP (2) Peter Orbanz, Joachim M. Buhmann