Group Preferences in Social Network Services. - Florian Wenzel; Werner Kießling (Grundlagen von Datenbanken, 2010)

The Preference SQL System - An Overview. - Werner Kießling; Markus Endres; Florian Wenzel (IEEE Data Eng. Bull., 2011)

Complex Preference Queries Supporting Spatial Applications for User Groups. - Florian Wenzel; Markus Endres; Stefan Mandl; Werner Kießling (Proc. VLDB Endow., 2012)

Interactive Toolbox for Spatial-Textual Preference Queries. - Florian Wenzel; Dominik Köppl; Werner Kießling (SSTD, 2013)

Aggregation and Analysis of Enriched Spatial User Models from Location-Based Social Networks. - Florian Wenzel; Werner Kießling (GeoRich@SIGMOD, 2014)

Sparse Estimation in a Correlated Probit Model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (CoRR, 2015)

A Preference-Driven Database Approach to Reciprocal User Recommendations in Online Social Networks. - Florian Wenzel; Werner Kießling (DEXA (2), 2016)

Separating Sparse Signals from Correlated Noise in Binary Classification. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; Christoph Lippert; Marius Kloft (CFA@UAI, 2016)

Bayesian Nonlinear Support Vector Machines for Big Data. - Florian Wenzel; Théo Galy-Fajou; Matthäus Deutsch; Marius Kloft (ECML/PKDD (1), 2017)

Sparse probit linear mixed model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (Mach. Learn., 2017)

Scalable Generalized Dynamic Topic Models. - Patrick Jähnichen; Florian Wenzel; Marius Kloft; Stephan Mandt (AISTATS, 2018)

Quasi-Monte Carlo Variational Inference. - Alexander Buchholz; Florian Wenzel; Stephan Mandt (ICML, 2018)

Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. - Florian Wenzel; Théo Galy-Fajou; Christian Donner; Marius Kloft; Manfred Opper (AAAI, 2019)

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. - Théo Galy-Fajou; Florian Wenzel; Christian Donner; Manfred Opper (UAI, 2019)

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. - Théo Galy-Fajou; Florian Wenzel; Manfred Opper (AISTATS, 2020)

How Good is the Bayes Posterior in Deep Neural Networks Really? - Florian Wenzel; Kevin Roth; Bastiaan S. Veeling; Jakub Swiatkowski; Linh Tran; Stephan Mandt; Jasper Snoek; Tim Salimans; Rodolphe Jenatton; Sebastian Nowozin (ICML, 2020)

Hyperparameter Ensembles for Robustness and Uncertainty Quantification. - Florian Wenzel; Jasper Snoek; Dustin Tran; Rodolphe Jenatton (NeurIPS, 2020)

Scalable Inference in Latent Gaussian Process Models. - Florian Wenzel (2020)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (CoRR, 2021)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - Zachary Nado; Neil Band; Mark Collier; Josip Djolonga; Michael W. Dusenberry; Sebastian Farquhar; Angelos Filos; Marton Havasi; Rodolphe Jenatton; Ghassen Jerfel; Jeremiah Z. Liu; Zelda Mariet; et al. (CoRR, 2021)

On Stein Variational Neural Network Ensembles. - Francesco D'Angelo; Vincent Fortuin; Florian Wenzel (CoRR, 2021)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Sebastian W. Ober; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (ICLR, 2022)

Assaying Out-Of-Distribution Generalization in Transfer Learning. - Florian Wenzel; Andrea Dittadi; Peter V. Gehler; Carl-Johann Simon-Gabriel; Max Horn; Dominik Zietlow; David Kernert; Chris Russell 001; Thomas Brox; Bernt Schiele; Bernhard Schölkopf; Francesco Locatello (NeurIPS, 2022)

Are Multimodal Models Robust to Image and Text Perturbations? - Jielin Qiu; Yi Zhu 001; Xingjian Shi; Florian Wenzel; Zhiqiang Tang 001; Ding Zhao; Bo Li 026; Mu Li 003 (CoRR, 2022)

Sparse MoEs meet Efficient Ensembles. - James Urquhart Allingham; Florian Wenzel; Zelda E. Mariet; Basil Mustafa; Joan Puigcerver; Neil Houlsby; Ghassen Jerfel; Vincent Fortuin; Balaji Lakshminarayanan; Jasper Snoek; Dustin Tran; Carlos Riquelme Ruiz; et al. (Trans. Mach. Learn. Res., 2022)

Deep Classifiers with Label Noise Modeling and Distance Awareness. - Vincent Fortuin; Mark Collier; Florian Wenzel; James Urquhart Allingham; Jeremiah Zhe Liu; Dustin Tran; Balaji Lakshminarayanan; Jesse Berent; Rodolphe Jenatton; Effrosyni Kokiopoulou (Trans. Mach. Learn. Res., 2022)

Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. - Junaid Ali 001; Matthäus Kleindessner; Florian Wenzel; Kailash Budhathoki; Volkan Cevher; Chris Russell 001 (AIES, 2023)

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. - Charlotte Loh; Seungwook Han; Shivchander Sudalairaj; Rumen Dangovski; Kai Xu 016; Florian Wenzel; Marin Soljacic; Akash Srivastava (ICML, 2023)

Leveraging sparse and shared feature activations for disentangled representation learning. - Marco Fumero; Florian Wenzel; Luca Zancato; Alessandro Achille; Emanuele Rodolà; Stefano Soatto; Bernhard Schölkopf; Francesco Locatello (NeurIPS, 2023)

A data augmentation perspective on diffusion models and retrieval. - Max F. Burg; Florian Wenzel; Dominik Zietlow; Max Horn; Osama Makansi; Francesco Locatello; Chris Russell 001 (CoRR, 2023)

Image retrieval outperforms diffusion models on data augmentation. - Max F. Burg; Florian Wenzel; Dominik Zietlow; Max Horn; Osama Makansi; Francesco Locatello; Chris Russell 001 (Trans. Mach. Learn. Res., 2023)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)