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Mario Lucic

MLP-Mixer: An all-MLP Architecture for Vision


May 04, 2021
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, Alexey Dosovitskiy

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SI-Score: An image dataset for fine-grained analysis of robustness to object location, rotation and size


Apr 09, 2021
Jessica Yung, Rob Romijnders, Alexander Kolesnikov, Lucas Beyer, Josip Djolonga, Neil Houlsby, Sylvain Gelly, Mario Lucic, Xiaohua Zhai

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* 4 pages (10 pages including references and appendix), 10 figures. Accepted at the ICLR 2021 RobustML Workshop. arXiv admin note: text overlap with arXiv:2007.08558 
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Underspecification Presents Challenges for Credibility in Modern Machine Learning


Nov 06, 2020
Alexander D'Amour, Katherine 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, Yian Ma, Cory 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

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A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation


Oct 27, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

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* Journal of Machine Learning Research 2020, Volume 21, Number 209 
 * arXiv admin note: substantial text overlap with arXiv:1811.12359 
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Which Model to Transfer? Finding the Needle in the Growing Haystack


Oct 13, 2020
Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lucic

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Representation learning from videos in-the-wild: An object-centric approach


Oct 06, 2020
Rob Romijnders, Aravindh Mahendran, Michael Tschannen, Josip Djolonga, Marvin Ritter, Neil Houlsby, Mario Lucic

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A Commentary on the Unsupervised Learning of Disentangled Representations


Jul 28, 2020
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

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* The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020 (AAAI-20) 
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On Robustness and Transferability of Convolutional Neural Networks


Jul 16, 2020
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvan Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

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Self-Supervised Learning of Video-Induced Visual Invariances


Dec 05, 2019
Michael Tschannen, Josip Djolonga, Marvin Ritter, Aravindh Mahendran, Neil Houlsby, Sylvain Gelly, Mario Lucic

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Semantic Bottleneck Scene Generation


Nov 26, 2019
Samaneh Azadi, Michael Tschannen, Eric Tzeng, Sylvain Gelly, Trevor Darrell, Mario Lucic

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The Visual Task Adaptation Benchmark


Oct 01, 2019
Xiaohua Zhai, Joan Puigcerver, Alexander Kolesnikov, Pierre Ruyssen, Carlos Riquelme, Mario Lucic, Josip Djolonga, Andre Susano Pinto, Maxim Neumann, Alexey Dosovitskiy, Lucas Beyer, Olivier Bachem, Michael Tschannen, Marcin Michalski, Olivier Bousquet, Sylvain Gelly, Neil Houlsby

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On Mutual Information Maximization for Representation Learning


Jul 31, 2019
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic

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* Michael Tschannen and Josip Djolonga contributed equally 
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Evaluating Generative Models Using Divergence Frontiers


May 26, 2019
Josip Djolonga, Mario Lucic, Marco Cuturi, Olivier Bachem, Olivier Bousquet, Sylvain Gelly

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High-Fidelity Image Generation With Fewer Labels


Mar 06, 2019
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly

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* Mario Lucic, Michael Tschannen, and Marvin Ritter contributed equally to this work 
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Recent Advances in Autoencoder-Based Representation Learning


Dec 12, 2018
Michael Tschannen, Olivier Bachem, Mario Lucic

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* Presented at the third workshop on Bayesian Deep Learning (NeurIPS 2018) 
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations


Dec 02, 2018
Francesco Locatello, Stefan Bauer, Mario Lucic, Sylvain Gelly, Bernhard Schölkopf, Olivier Bachem

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* This is a preliminary preprint based on our initial experimental results 
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Self-Supervised Generative Adversarial Networks


Nov 27, 2018
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, Neil Houlsby

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Are GANs Created Equal? A Large-Scale Study


Oct 29, 2018
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, Olivier Bousquet

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* NIPS'18: Added a section on the limitations of the study and additional empirical results 
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Deep Generative Models for Distribution-Preserving Lossy Compression


Oct 28, 2018
Michael Tschannen, Eirikur Agustsson, Mario Lucic

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* NIPS 2018. Code: https://github.com/mitscha/dplc . Changes w.r.t. v1: Some clarifications in the text and additional numerical results 
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Assessing Generative Models via Precision and Recall


Oct 28, 2018
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, Sylvain Gelly

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* NIPS 2018 
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The GAN Landscape: Losses, Architectures, Regularization, and Normalization


Oct 26, 2018
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, Sylvain Gelly

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* Changed formatting from ICML workshop to ICLR. We added additional resnet ablation studies, hinge loss, and an empirical comparison between KID and FID 
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On Self Modulation for Generative Adversarial Networks


Oct 02, 2018
Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly

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Scalable k-Means Clustering via Lightweight Coresets


Jun 06, 2018
Olivier Bachem, Mario Lucic, Andreas Krause

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* To appear in the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) 
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One-Shot Coresets: The Case of k-Clustering


Feb 20, 2018
Olivier Bachem, Mario Lucic, Silvio Lattanzi

* To Appear In AISTATS 2018 
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Training Gaussian Mixture Models at Scale via Coresets


Jan 15, 2018
Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman

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Stochastic Submodular Maximization: The Case of Coverage Functions


Nov 05, 2017
Mohammad Reza Karimi, Mario Lucic, Hamed Hassani, Andreas Krause

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* 31st Conference on Neural Information Processing Systems (NIPS 2017) 
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Practical Coreset Constructions for Machine Learning


Jun 04, 2017
Olivier Bachem, Mario Lucic, Andreas Krause

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Uniform Deviation Bounds for Unbounded Loss Functions like k-Means


Feb 27, 2017
Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause


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Horizontally Scalable Submodular Maximization


May 31, 2016
Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause

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Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning


May 02, 2016
Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi, Andreas Krause

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* Conference version 
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