Fast and Robust Least Squares Estimation in Corrupted Linear Models. - Brian McWilliams; Gabriel Krummenacher; Mario Lucic; Joachim M. Buhmann (NIPS, 2014)

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning. - Mario Lucic; Mesrob I. Ohannessian; Amin Karbasi; Andreas Krause 001 (AISTATS, 2015)

Coresets for Nonparametric Estimation - the Case of DP-Means. - Olivier Bachem; Mario Lucic; Andreas Krause 001 (ICML, 2015)

Quantifying Location Privacy Leakage from Transaction Prices. - Arthur Gervais; Hubert Ritzdorf; Mario Lucic; Srdjan Capkun (IACR Cryptol. ePrint Arch., 2015)

Approximate K-Means++ in Sublinear Time. - Olivier Bachem; Mario Lucic; S. Hamed Hassani; Andreas Krause 001 (AAAI, 2016)

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. - Mario Lucic; Olivier Bachem; Andreas Krause 001 (AISTATS, 2016)

Quantifying Location Privacy Leakage from Transaction Prices. - Arthur Gervais; Hubert Ritzdorf; Mario Lucic; Vincent Lenders; Srdjan Capkun (ESORICS (2), 2016)

Horizontally Scalable Submodular Maximization. - Mario Lucic; Olivier Bachem; Morteza Zadimoghaddam; Andreas Krause 001 (ICML, 2016)

Linear-Time Outlier Detection via Sensitivity. - Mario Lucic; Olivier Bachem; Andreas Krause 001 (IJCAI, 2016)

Fast and Provably Good Seedings for k-Means. - Olivier Bachem; Mario Lucic; Seyed Hamed Hassani; Andreas Krause 001 (NIPS, 2016)

Distributed and Provably Good Seedings for k-Means in Constant Rounds. - Olivier Bachem; Mario Lucic; Andreas Krause 001 (ICML, 2017)

Uniform Deviation Bounds for k-Means Clustering. - Olivier Bachem; Mario Lucic; S. Hamed Hassani; Andreas Krause 001 (ICML, 2017)

Stochastic Submodular Maximization: The Case of Coverage Functions. - Mohammad Reza Karimi; Mario Lucic; S. Hamed Hassani; Andreas Krause 001 (NIPS, 2017)

Scalable and Distributed Clustering via Lightweight Coresets. - Olivier Bachem; Mario Lucic; Andreas Krause 001 (CoRR, 2017)

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means. - Olivier Bachem; Mario Lucic; S. Hamed Hassani; Andreas Krause 001 (CoRR, 2017)

Training Gaussian Mixture Models at Scale via Coresets. - Mario Lucic; Matthew Faulkner 001; Andreas Krause 001; Dan Feldman (J. Mach. Learn. Res., 2017)

Computational and Statistical Tradeoffs via Data Summarization. - Mario Lucic (2017)

One-shot Coresets: The Case of k-Clustering. - Olivier Bachem; Mario Lucic; Silvio Lattanzi (AISTATS, 2018)

Scalable k -Means Clustering via Lightweight Coresets. - Olivier Bachem; Mario Lucic; Andreas Krause 001 (KDD, 2018)

Are GANs Created Equal? A Large-Scale Study. - Mario Lucic; Karol Kurach; Marcin Michalski; Sylvain Gelly; Olivier Bousquet (NeurIPS, 2018)

Assessing Generative Models via Precision and Recall. - Mehdi S. M. Sajjadi; Olivier Bachem; Mario Lucic; Olivier Bousquet; Sylvain Gelly (NeurIPS, 2018)

Deep Generative Models for Distribution-Preserving Lossy Compression. - Michael Tschannen; Eirikur Agustsson; Mario Lucic (NeurIPS, 2018)

The GAN Landscape: Losses, Architectures, Regularization, and Normalization. - Karol Kurach; Mario Lucic; Xiaohua Zhai; Marcin Michalski; Sylvain Gelly (CoRR, 2018)

Self-Supervised Generative Adversarial Networks. - Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby (CoRR, 2018)

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (CoRR, 2018)

Recent Advances in Autoencoder-Based Representation Learning. - Michael Tschannen; Olivier Bachem; Mario Lucic (CoRR, 2018)

Self-Supervised GANs via Auxiliary Rotation Loss. - Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby (CVPR, 2019)

On Self Modulation for Generative Adversarial Networks. - Ting Chen; Mario Lucic; Neil Houlsby; Sylvain Gelly (ICLR (Poster), 2019)

A Large-Scale Study on Regularization and Normalization in GANs. - Karol Kurach; Mario Lucic; Xiaohua Zhai; Marcin Michalski; Sylvain Gelly (ICML, 2019)

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (ICML, 2019)

High-Fidelity Image Generation With Fewer Labels. - Mario Lucic; Michael Tschannen; Marvin Ritter; Xiaohua Zhai; Olivier Bachem; Sylvain Gelly (ICML, 2019)

Evaluating Generative Models Using Divergence Frontiers. - Josip Djolonga; Mario Lucic; Marco Cuturi; Olivier Bachem; Olivier Bousquet; Sylvain Gelly (CoRR, 2019)

The Visual Task Adaptation Benchmark. - Xiaohua Zhai; Joan Puigcerver; Alexander Kolesnikov 003; Pierre Ruyssen; Carlos Riquelme; Mario Lucic; Josip Djolonga; André Susano Pinto; Maxim Neumann; Alexey Dosovitskiy; Lucas Beyer; Olivier Bachem; et al. (CoRR, 2019)

Semantic Bottleneck Scene Generation. - Samaneh Azadi; Michael Tschannen; Eric Tzeng; Sylvain Gelly; Trevor Darrell; Mario Lucic (CoRR, 2019)

A Commentary on the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (AAAI, 2020)

Precision-Recall Curves Using Information Divergence Frontiers. - Josip Djolonga; Mario Lucic; Marco Cuturi; Olivier Bachem; Olivier Bousquet; Sylvain Gelly (AISTATS, 2020)

Self-Supervised Learning of Video-Induced Visual Invariances. - Michael Tschannen; Josip Djolonga; Marvin Ritter; Aravindh Mahendran; Neil Houlsby; Sylvain Gelly; Mario Lucic (CVPR, 2020)

On Mutual Information Maximization for Representation Learning. - Michael Tschannen; Josip Djolonga; Paul K. Rubenstein; Sylvain Gelly; Mario Lucic (ICLR, 2020)

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (J. Mach. Learn. Res., 2020)

On Robustness and Transferability of Convolutional Neural Networks. - Josip Djolonga; Jessica Yung; Michael Tschannen; Rob Romijnders; Lucas Beyer; Alexander Kolesnikov 003; Joan Puigcerver; Matthias Minderer; Alexander D'Amour; Dan Moldovan; Sylvain Gelly; Neil Houlsby; et al. (CVPR, 2021)

ViViT: A Video Vision Transformer. - Anurag Arnab; Mostafa Dehghani 001; Georg Heigold; Chen Sun 002; Mario Lucic; Cordelia Schmid (ICCV, 2021)

A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models. - Ibrahim M. Alabdulmohsin; Mario Lucic (NeurIPS, 2021)

Revisiting the Calibration of Modern Neural Networks. - Matthias Minderer; Josip Djolonga; Rob Romijnders; Frances Hubis; Xiaohua Zhai; Neil Houlsby; Dustin Tran; Mario Lucic (NeurIPS, 2021)

MLP-Mixer: An all-MLP Architecture for Vision. - Ilya O. Tolstikhin; Neil Houlsby; Alexander Kolesnikov 003; Lucas Beyer; Xiaohua Zhai; Thomas Unterthiner; Jessica Yung; Andreas Steiner 001; Daniel Keysers; Jakob Uszkoreit; Mario Lucic; Alexey Dosovitskiy (NeurIPS, 2021)

Representation learning from videos in-the-wild: An object-centric approach. - Rob Romijnders; Aravindh Mahendran; Michael Tschannen; Josip Djolonga; Marvin Ritter; Neil Houlsby; Mario Lucic (WACV, 2021)

SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. - Jessica Yung; Rob Romijnders; Alexander Kolesnikov 003; Lucas Beyer; Josip Djolonga; Neil Houlsby; Sylvain Gelly; Mario Lucic; Xiaohua Zhai (CoRR, 2021)

PolyViT: Co-training Vision Transformers on Images, Videos and Audio. - Valerii Likhosherstov; Anurag Arnab; Krzysztof Choromanski; Mario Lucic; Yi Tay; Adrian Weller; Mostafa Dehghani 001 (CoRR, 2021)

Which Model to Transfer? Finding the Needle in the Growing Haystack. - Cédric Renggli; André Susano Pinto; Luka Rimanic; Joan Puigcerver; Carlos Riquelme; Ce Zhang 001; Mario Lucic (CVPR, 2022)

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations. - Mehdi S. M. Sajjadi; Henning Meyer; Etienne Pot; Urs Bergmann; Klaus Greff; Noha Radwan; Suhani Vora; Mario Lucic; Daniel Duckworth; Alexey Dosovitskiy; Jakob Uszkoreit; Thomas A. Funkhouser; et al. (CVPR, 2022)

VCT: A Video Compression Transformer. - Fabian Mentzer; George Toderici; David Minnen; Sergi Caelles; Sung Jin Hwang; Mario Lucic; Eirikur Agustsson (NeurIPS, 2022)

Object Scene Representation Transformer. - Mehdi S. M. Sajjadi; Daniel Duckworth; Aravindh Mahendran; Sjoerd van Steenkiste; Filip Pavetic; Mario Lucic; Leonidas J. Guibas; Klaus Greff; Thomas Kipf (NeurIPS, 2022)

Beyond Transfer Learning: Co-finetuning for Action Localisation. - Anurag Arnab; Xuehan Xiong; Alexey A. Gritsenko; Rob Romijnders; Josip Djolonga; Mostafa Dehghani 001; Chen Sun 002; Mario Lucic; Cordelia Schmid (CoRR, 2022)

Underspecification Presents Challenges for Credibility in Modern Machine Learning. - Alexander D'Amour; Katherine A. Heller; Dan Moldovan; Ben Adlam; Babak Alipanahi; Alex Beutel; Christina Chen; Jonathan Deaton; Jacob Eisenstein; Matthew D. Hoffman; Farhad Hormozdiari; Neil Houlsby; et al. (J. Mach. Learn. Res., 2022)

RUST: Latent Neural Scene Representations from Unposed Imagery. - Mehdi S. M. Sajjadi; Aravindh Mahendran; Thomas Kipf; Etienne Pot; Daniel Duckworth; Mario Lucic; Klaus Greff (CVPR, 2023)

Audiovisual Masked Autoencoders. - Mariana-Iuliana Georgescu; Eduardo Fonseca; Radu Tudor Ionescu; Mario Lucic; Cordelia Schmid; Anurag Arnab (ICCV, 2023)

Video OWL-ViT: Temporally-consistent open-world localization in video. - Georg Heigold; Daniel Keysers; Matthias Minderer; Mario Lucic; Alexey A. Gritsenko; Fisher Yu 001; Alex Bewley; Thomas Kipf (ICCV, 2023)

Scaling Vision Transformers to 22 Billion Parameters. - Mostafa Dehghani 001; Josip Djolonga; Basil Mustafa; Piotr Padlewski; Jonathan Heek; Justin Gilmer; Andreas Peter Steiner; Mathilde Caron; Robert Geirhos; Ibrahim Alabdulmohsin; Rodolphe Jenatton; Lucas Beyer; et al. (ICML, 2023)

Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. - Mostafa Dehghani 001; Basil Mustafa; Josip Djolonga; Jonathan Heek; Matthias Minderer; Mathilde Caron; Andreas Steiner 001; Joan Puigcerver; Robert Geirhos; Ibrahim M. Alabdulmohsin; Avital Oliver; Piotr Padlewski; et al. (NeurIPS, 2023)

PaLI-X: On Scaling up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CoRR, 2023)

PolyViT: Co-training Vision Transformers on Images, Videos and Audio. - Valerii Likhosherstov; Anurag Arnab; Krzysztof Marcin Choromanski; Mario Lucic; Yi Tay; Mostafa Dehghani 001 (Trans. Mach. Learn. Res., 2023)

On Scaling Up a Multilingual Vision and Language Model. - Xi Chen 071; Josip Djolonga; Piotr Padlewski; Basil Mustafa; Soravit Changpinyo; Jialin Wu; Carlos Riquelme Ruiz; Sebastian Goodman; Xiao Wang 038; Yi Tay; Siamak Shakeri; Mostafa Dehghani 001; et al. (CVPR, 2024)

End-to-End Spatio-Temporal Action Localisation with Video Transformers. - Alexey A. Gritsenko; Xuehan Xiong; Josip Djolonga; Mostafa Dehghani 001; Chen Sun 002; Mario Lucic; Cordelia Schmid; Anurag Arnab (CVPR, 2024)

SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration. - Daniel Duckworth; Peter Hedman; Christian Reiser; Peter Zhizhin; Jean-François Thibert; Mario Lucic; Richard Szeliski; Jonathan T. Barron (ACM Trans. Graph., 2024)