Mario Lucic

Publication Activity (10 Years)

Years Active: 2014-2023
Publications (10 Years): 92

Top Topics

Generative Model

Nonparametric Estimation

Supervised Learning

Top Venues

Publications

Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer, Mathilde Caron, Andreas Steiner, Joan Puigcerver, Robert Geirhos, Ibrahim M. Alabdulmohsin, Avital Oliver, Piotr Padlewski, Alexey A. Gritsenko, Mario Lucic, Neil Houlsby
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. NeurIPS (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. CoRR (2023)
Daniel Duckworth, Peter Hedman, Christian Reiser, Peter Zhizhin, Jean-François Thibert, Mario Lucic, Richard Szeliski, Jonathan T. Barron
SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration. CoRR (2023)
Mehdi S. M. Sajjadi, Aravindh Mahendran, Thomas Kipf, Etienne Pot, Daniel Duckworth, Mario Lucic, Klaus Greff
RUST: Latent Neural Scene Representations from Unposed Imagery. CVPR (2023)
Georg Heigold, Matthias Minderer, Alexey A. Gritsenko, Alex Bewley, Daniel Keysers, Mario Lucic, Fisher Yu, Thomas Kipf
Video OWL-ViT: Temporally-consistent open-world localization in video. CoRR (2023)
Valerii Likhosherstov, Anurag Arnab, Krzysztof Marcin Choromanski, Mario Lucic, Yi Tay, Mostafa Dehghani
PolyViT: Co-training Vision Transformers on Images, Videos and Audio. Trans. Mach. Learn. Res. 2023 (2023)
Alexey A. Gritsenko, Xuehan Xiong, Josip Djolonga, Mostafa Dehghani, Chen Sun, Mario Lucic, Cordelia Schmid, Anurag Arnab
End-to-End Spatio-Temporal Action Localisation with Video Transformers. CoRR (2023)
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Peter Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme Ruiz, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Collier, Alexey A. Gritsenko, Vighnesh Birodkar, Cristina Nader Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetic, Dustin Tran, Thomas Kipf, Mario Lucic, Xiaohua Zhai, Daniel Keysers, Jeremiah J. Harmsen, Neil Houlsby
Scaling Vision Transformers to 22 Billion Parameters. ICML (2023)
Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer, Mathilde Caron, Andreas Steiner, Joan Puigcerver, Robert Geirhos, Ibrahim Alabdulmohsin, Avital Oliver, Piotr Padlewski, Alexey A. Gritsenko, Mario Lucic, Neil Houlsby
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution. CoRR (2023)
Xi Chen, Josip Djolonga, Piotr Padlewski, Basil Mustafa, Soravit Changpinyo, Jialin Wu, Carlos Riquelme Ruiz, Sebastian Goodman, Xiao Wang, Yi Tay, Siamak Shakeri, Mostafa Dehghani, Daniel Salz, Mario Lucic, Michael Tschannen, Arsha Nagrani, Hexiang Hu, Mandar Joshi, Bo Pang, Ceslee Montgomery, Paulina Pietrzyk, Marvin Ritter, A. J. Piergiovanni, Matthias Minderer, Filip Pavetic, Austin Waters, Gang Li, Ibrahim Alabdulmohsin, Lucas Beyer, Julien Amelot, Kenton Lee, Andreas Peter Steiner, Yang Li, Daniel Keysers, Anurag Arnab, Yuanzhong Xu, Keran Rong, Alexander Kolesnikov, Mojtaba Seyedhosseini, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut
PaLI-X: On Scaling up a Multilingual Vision and Language Model. CoRR (2023)
Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab
Audiovisual Masked Autoencoders. ICCV (2023)
Georg Heigold, Daniel Keysers, Matthias Minderer, Mario Lucic, Alexey A. Gritsenko, Fisher Yu, Alex Bewley, Thomas Kipf
Video OWL-ViT: Temporally-consistent open-world localization in video. ICCV (2023)
Mariana-Iuliana Georgescu, Eduardo Fonseca, Radu Tudor Ionescu, Mario Lucic, Cordelia Schmid, Anurag Arnab
Audiovisual Masked Autoencoders. CoRR (2022)
Fabian Mentzer, George Toderici, David Minnen, Sergi Caelles, Sung Jin Hwang, Mario Lucic, Eirikur Agustsson
VCT: A Video Compression Transformer. NeurIPS (2022)
Fabian Mentzer, George Toderici, David Minnen, Sung Jin Hwang, Sergi Caelles, Mario Lucic, Eirikur Agustsson
VCT: A Video Compression Transformer. CoRR (2022)
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, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23 (2022)
Mehdi S. M. Sajjadi, Aravindh Mahendran, Thomas Kipf, Etienne Pot, Daniel Duckworth, Mario Lucic, Klaus Greff
RUST: Latent Neural Scene Representations from Unposed Imagery. CoRR (2022)
Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetic, Mario Lucic, Leonidas J. Guibas, Klaus Greff, Thomas Kipf
Object Scene Representation Transformer. NeurIPS (2022)
Mehdi S. M. Sajjadi, Daniel Duckworth, Aravindh Mahendran, Sjoerd van Steenkiste, Filip Pavetic, Mario Lucic, Leonidas J. Guibas, Klaus Greff, Thomas Kipf
Object Scene Representation Transformer. CoRR (2022)
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, Andrea Tagliasacchi
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations. CVPR (2022)
Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic
Which Model to Transfer? Finding the Needle in the Growing Haystack. CVPR (2022)
Anurag Arnab, Xuehan Xiong, Alexey A. Gritsenko, Rob Romijnders, Josip Djolonga, Mostafa Dehghani, Chen Sun, Mario Lucic, Cordelia Schmid
Beyond Transfer Learning: Co-finetuning for Action Localisation. CoRR (2022)
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
Revisiting the Calibration of Modern Neural Networks. NeurIPS (2021)
Valerii Likhosherstov, Anurag Arnab, Krzysztof Choromanski, Mario Lucic, Yi Tay, Adrian Weller, Mostafa Dehghani
PolyViT: Co-training Vision Transformers on Images, Videos and Audio. CoRR (2021)
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy
MLP-Mixer: An all-MLP Architecture for Vision. NeurIPS (2021)
Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic
Representation learning from videos in-the-wild: An object-centric approach. WACV (2021)
Ibrahim M. Alabdulmohsin, Mario Lucic
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models. NeurIPS (2021)
Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai
SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size. CoRR (2021)
Ibrahim M. Alabdulmohsin, Mario Lucic
A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models. CoRR (2021)
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, Andrea Tagliasacchi
Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations. CoRR (2021)
Matthias Minderer, Josip Djolonga, Rob Romijnders, Frances Hubis, Xiaohua Zhai, Neil Houlsby, Dustin Tran, Mario Lucic
Revisiting the Calibration of Modern Neural Networks. CoRR (2021)
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, Cordelia Schmid
ViViT: A Video Vision Transformer. CoRR (2021)
Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, Cordelia Schmid
ViViT: A Video Vision Transformer. ICCV (2021)
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
On Robustness and Transferability of Convolutional Neural Networks. CVPR (2021)
Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy
MLP-Mixer: An all-MLP Architecture for Vision. CoRR (2021)
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, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Underspecification Presents Challenges for Credibility in Modern Machine Learning. CoRR (2020)
Cédric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic
Which Model to Transfer? Finding the Needle in the Growing Haystack. CoRR (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. CoRR (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. J. Mach. Learn. Res. 21 (2020)
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic
On Mutual Information Maximization for Representation Learning. ICLR (2020)
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
Precision-Recall Curves Using Information Divergence Frontiers. AISTATS (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Commentary on the Unsupervised Learning of Disentangled Representations. AAAI (2020)
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic
On Robustness and Transferability of Convolutional Neural Networks. CoRR (2020)
Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic
Representation learning from videos in-the-wild: An object-centric approach. CoRR (2020)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
A Commentary on the Unsupervised Learning of Disentangled Representations. CoRR (2020)
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic
Self-Supervised Learning of Video-Induced Visual Invariances. CVPR (2020)
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly
Evaluating Generative Models Using Divergence Frontiers. CoRR (2019)
Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly
On Self Modulation for Generative Adversarial Networks. ICLR (Poster) (2019)
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly
A Large-Scale Study on Regularization and Normalization in GANs. ICML (2019)
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
High-Fidelity Image Generation With Fewer Labels. ICML (2019)
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly
High-Fidelity Image Generation With Fewer Labels. CoRR (2019)
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, André Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby
The Visual Task Adaptation Benchmark. CoRR (2019)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. RML@ICLR (2019)
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil Houlsby
Self-Supervised GANs via Auxiliary Rotation Loss. CVPR (2019)
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic
On Mutual Information Maximization for Representation Learning. CoRR (2019)
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic
Self-Supervised Learning of Video-Induced Visual Invariances. CoRR (2019)
Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic
Semantic Bottleneck Scene Generation. CoRR (2019)
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. ICML (2019)
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Assessing Generative Models via Precision and Recall. CoRR (2018)
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Are GANs Created Equal? A Large-Scale Study. NeurIPS (2018)
Michael Tschannen, Eirikur Agustsson, Mario Lucic
Deep Generative Models for Distribution-Preserving Lossy Compression. CoRR (2018)
Olivier Bachem, Mario Lucic, Silvio Lattanzi
One-shot Coresets: The Case of k-Clustering. AISTATS (2018)
Michael Tschannen, Olivier Bachem, Mario Lucic
Recent Advances in Autoencoder-Based Representation Learning. CoRR (2018)
Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly
On Self Modulation for Generative Adversarial Networks. CoRR (2018)
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly
Assessing Generative Models via Precision and Recall. NeurIPS (2018)
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly
The GAN Landscape: Losses, Architectures, Regularization, and Normalization. CoRR (2018)
Michael Tschannen, Eirikur Agustsson, Mario Lucic
Deep Generative Models for Distribution-Preserving Lossy Compression. NeurIPS (2018)
Olivier Bachem, Mario Lucic, Andreas Krause
Scalable k -Means Clustering via Lightweight Coresets. KDD (2018)
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem
Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. CoRR (2018)
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil Houlsby
Self-Supervised Generative Adversarial Networks. CoRR (2018)
Olivier Bachem, Mario Lucic, Silvio Lattanzi
One-Shot Coresets: The Case of k-Clustering. CoRR (2017)
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
Uniform Deviation Bounds for Unbounded Loss Functions like k-Means. CoRR (2017)
Olivier Bachem, Mario Lucic, Andreas Krause
Distributed and Provably Good Seedings for k-Means in Constant Rounds. ICML (2017)
Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman
Training Gaussian Mixture Models at Scale via Coresets. J. Mach. Learn. Res. 18 (2017)
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet
Are GANs Created Equal? A Large-Scale Study. CoRR (2017)
Olivier Bachem, Mario Lucic, Andreas Krause
Scalable and Distributed Clustering via Lightweight Coresets. CoRR (2017)
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
Uniform Deviation Bounds for k-Means Clustering. ICML (2017)
Mohammad Reza Karimi, Mario Lucic, S. Hamed Hassani, Andreas Krause
Stochastic Submodular Maximization: The Case of Coverage Functions. NIPS (2017)
Mohammad Reza Karimi, Mario Lucic, S. Hamed Hassani, Andreas Krause
Stochastic Submodular Maximization: The Case of Coverage Functions. CoRR (2017)
Mario Lucic, Olivier Bachem, Andreas Krause
Linear-Time Outlier Detection via Sensitivity. IJCAI (2016)
Olivier Bachem, Mario Lucic, Seyed Hamed Hassani, Andreas Krause
Fast and Provably Good Seedings for k-Means. NIPS (2016)
Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi, Andreas Krause
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning. CoRR (2016)
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
Horizontally Scalable Submodular Maximization. CoRR (2016)
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause
Approximate K-Means++ in Sublinear Time. AAAI (2016)
Arthur Gervais, Hubert Ritzdorf, Mario Lucic, Vincent Lenders, Srdjan Capkun
Quantifying Location Privacy Leakage from Transaction Prices. ESORICS (2) (2016)
Mario Lucic, Olivier Bachem, Andreas Krause
Linear-time Outlier Detection via Sensitivity. CoRR (2016)
Mario Lucic, Olivier Bachem, Andreas Krause
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. AISTATS (2016)
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause
Horizontally Scalable Submodular Maximization. ICML (2016)
Arthur Gervais, Hubert Ritzdorf, Mario Lucic, Srdjan Capkun
Quantifying Location Privacy Leakage from Transaction Prices. IACR Cryptol. ePrint Arch. 2015 (2015)
Mario Lucic, Olivier Bachem, Andreas Krause
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures. CoRR (2015)
Olivier Bachem, Mario Lucic, Andreas Krause
Coresets for Nonparametric Estimation - the Case of DP-Means. ICML (2015)
Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi, Andreas Krause
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning. AISTATS (2015)
Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
Fast and Robust Least Squares Estimation in Corrupted Linear Models. NIPS (2014)