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2020 – today
- 2024
[i21]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024)- 2023
- [j6]Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell:
Image retrieval outperforms diffusion models on data augmentation. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Junaid Ali
, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell:
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. AIES 2023: 809-833 - [c17]Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. ICML 2023: 22614-22630 - [c16]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. NeurIPS 2023 - [i20]Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. CoRR abs/2303.02484 (2023) - [i19]Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello:
Leveraging sparse and shared feature activations for disentangled representation learning. CoRR abs/2304.07939 (2023) - [i18]Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell:
A data augmentation perspective on diffusion models and retrieval. CoRR abs/2304.10253 (2023) - [i17]Junaid Ali, Matthäus Kleindessner, Florian Wenzel, Kailash Budhathoki, Volkan Cevher, Chris Russell:
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision. CoRR abs/2310.11867 (2023) - 2022
- [j5]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, Rodolphe Jenatton:
Sparse MoEs meet Efficient Ensembles. Trans. Mach. Learn. Res. 2022 (2022) - [j4]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. Trans. Mach. Learn. Res. 2022 (2022) - [c15]Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. ICLR 2022 - [c14]Florian Wenzel, Andrea Dittadi, Peter V. Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. NeurIPS 2022 - [i16]Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello:
Assaying Out-Of-Distribution Generalization in Transfer Learning. CoRR abs/2207.09239 (2022) - [i15]Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li:
Are Multimodal Models Robust to Image and Text Perturbations? CoRR abs/2212.08044 (2022) - 2021
- [i14]Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. CoRR abs/2102.06571 (2021) - [i13]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, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i12]Francesco D'Angelo, Vincent Fortuin, Florian Wenzel:
On Stein Variational Neural Network Ensembles. CoRR abs/2106.10760 (2021) - [i11]Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Z. Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou:
Deep Classifiers with Label Noise Modeling and Distance Awareness. CoRR abs/2110.02609 (2021) - [i10]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, Rodolphe Jenatton:
Sparse MoEs meet Efficient Ensembles. CoRR abs/2110.03360 (2021) - 2020
- [b1]Florian Wenzel:
Scalable Inference in Latent Gaussian Process Models. Humboldt University of Berlin, Germany, 2020 - [c13]Théo Galy-Fajou, Florian Wenzel, Manfred Opper:
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. AISTATS 2020: 3025-3035 - [c12]Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? ICML 2020: 10248-10259 - [c11]Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. NeurIPS 2020 - [i9]Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Swiatkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin:
How Good is the Bayes Posterior in Deep Neural Networks Really? CoRR abs/2002.02405 (2020) - [i8]Théo Galy-Fajou, Florian Wenzel, Manfred Opper:
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models. CoRR abs/2002.11451 (2020) - [i7]Florian Wenzel, Jasper Snoek, Dustin Tran, Rodolphe Jenatton:
Hyperparameter Ensembles for Robustness and Uncertainty Quantification. CoRR abs/2006.13570 (2020)
2010 – 2019
- 2019
- [c10]Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper:
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. AAAI 2019: 5417-5424 - [c9]Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper:
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. UAI 2019: 755-765 - [i6]Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper:
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation. CoRR abs/1905.09670 (2019) - 2018
- [c8]Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. AISTATS 2018: 1427-1435 - [c7]Alexander Buchholz, Florian Wenzel, Stephan Mandt:
Quasi-Monte Carlo Variational Inference. ICML 2018: 667-676 - [i5]Florian Wenzel, Théo Galy-Fajou, Christian Donner, Marius Kloft, Manfred Opper:
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation. CoRR abs/1802.06383 (2018) - [i4]Patrick Jähnichen, Florian Wenzel, Marius Kloft, Stephan Mandt:
Scalable Generalized Dynamic Topic Models. CoRR abs/1803.07868 (2018) - [i3]Alexander Buchholz, Florian Wenzel, Stephan Mandt:
Quasi-Monte Carlo Variational Inference. CoRR abs/1807.01604 (2018) - 2017
- [j3]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse probit linear mixed model. Mach. Learn. 106(9-10): 1621-1642 (2017) - [c6]Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch, Marius Kloft:
Bayesian Nonlinear Support Vector Machines for Big Data. ECML/PKDD (1) 2017: 307-322 - [i2]Florian Wenzel, Théo Galy-Fajou, Matthäus Deutsch, Marius Kloft:
Bayesian Nonlinear Support Vector Machines for Big Data. CoRR abs/1707.05532 (2017) - 2016
- [c5]Florian Wenzel, Werner Kießling:
A Preference-Driven Database Approach to Reciprocal User Recommendations in Online Social Networks. DEXA (2) 2016: 3-10 - [c4]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, Christoph Lippert, Marius Kloft:
Separating Sparse Signals from Correlated Noise in Binary Classification. CFA@UAI 2016: 48-58 - 2015
- [i1]Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft:
Sparse Estimation in a Correlated Probit Model. CoRR abs/1507.04777 (2015) - 2014
- [c3]Florian Wenzel, Werner Kießling:
Aggregation and Analysis of Enriched Spatial User Models from Location-Based Social Networks. GeoRich@SIGMOD 2014: 8:1-8:6 - 2013
- [c2]Florian Wenzel, Dominik Köppl, Werner Kießling:
Interactive Toolbox for Spatial-Textual Preference Queries. SSTD 2013: 462-466 - 2012
- [j2]Florian Wenzel, Markus Endres, Stefan Mandl, Werner Kießling:
Complex Preference Queries Supporting Spatial Applications for User Groups. Proc. VLDB Endow. 5(12): 1946-1949 (2012) - 2011
- [j1]Werner Kießling, Markus Endres, Florian Wenzel:
The Preference SQL System - An Overview. IEEE Data Eng. Bull. 34(2): 11-18 (2011) - 2010
- [c1]Florian Wenzel, Werner Kießling:
Group Preferences in Social Network Services. Grundlagen von Datenbanken 2010
Coauthor Index
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last updated on 2024-08-05 21:13 CEST by the dblp team
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