Mario Lučić
Mario Lučić
Senior Research Scientist, Google Brain
Verified email at google.com - Homepage
Title
Cited by
Cited by
Year
Are GANs Created Equal? A Large-Scale Study
M Lucic, K Kurach, M Michalski, S Gelly, O Bousquet
Advances in Neural Information Processing Systems, 2017
328*2017
Challenging common assumptions in the unsupervised learning of disentangled representations
F Locatello, S Bauer, M Lucic, S Gelly, B Schölkopf, O Bachem
International Conference on Machine Learning, 2019
1142019
A Large-Scale Study on Regularization and Normalization in GANs
K Kurach*, M Lucic*, X Zhai, M Michalski, S Gelly
International Conference on Machine Learning, 2018
94*2018
Fast and provably good seedings for k-means
O Bachem, M Lucic, H Hassani, A Krause
Advances in Neural Information Processing Systems, 2016
832016
Approximate K-Means++ in Sublinear Time
O Bachem, M Lucic, SH Hassani, A Krause
AAAI Conference on Artificial Intelligence, 2016
732016
Recent advances in autoencoder-based representation learning
M Tschannen, O Bachem, M Lucic
Workshop on Bayesian Deep Learning (NeurIPS 2018), 2018
582018
Assessing Generative Models via Precision and Recall
MSM Sajjadi, O Bachem, M Lucic, O Bousquet, S Gelly
Advances in Neural Information Processing Systems, 2018
572018
Coresets for Nonparametric Estimation - the Case of DP-Means
O Bachem, M Lucic, A Krause
International Conference on Machine Learning, 2015
442015
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures
M Lucic, O Bachem, A Krause
International Conference on Artificial Intelligence and Statistics, 2016
432016
Practical coreset constructions for machine learning
O Bachem, M Lucic, A Krause
arXiv preprint arXiv:1703.06476, 2017
402017
Scalable k-means clustering via lightweight coresets
O Bachem, M Lucic, A Krause
International Conference on Knowledge Discovery & Data Mining, 2018
372018
Fast and robust least squares estimation in corrupted linear models
B McWilliams, G Krummenacher, M Lucic, JM Buhmann
Advances in Neural Information Processing Systems, 2014
372014
Self-Supervised GANs via Auxiliary Rotation Loss
T Chen, X Zhai, M Ritter, M Lucic, N Houlsby
Conference on Computer Vision and Pattern Recognition, 2019
35*2019
Training Gaussian mixture models at scale via coresets
M Lucic, M Faulkner, A Krause, D Feldman
The Journal of Machine Learning Research, 2017
34*2017
High-Fidelity Image Generation With Fewer Labels
M Lučić, M Tschannen, M Ritter, X Zhai, O Bachem, S Gelly
International Conference on Machine Learning, 2019
29*2019
On self modulation for generative adversarial networks
T Chen, M Lucic, N Houlsby, S Gelly
International Conference on Learning Representations, 2019
232019
Stochastic Submodular Maximization: The Case of Coverage Functions
M Karimi, M Lucic, H Hassani, A Krause
Advances in Neural Information Processing Systems, 2017
212017
Automatic classification of change requests for improved it service quality
C Kadar, D Wiesmann, J Iria, D Husemann, M Lucic
2011 Annual SRII Global Conference, 430-439, 2011
202011
Deep Generative Models for Distribution-Preserving Lossy Compression
M Tschannen, E Agustsson, M Lucic
Advances in Neural Information Processing Systems, 2018
172018
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning
M Lucic, MI Ohannessian, A Karbasi, A Krause
International Conference on Artificial Intelligence and Statistics, 2015
172015
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