Mario Lucic

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Google ∙ ETH Zurich

Research Scientist in the Google Brain team

A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is...
07/28/2020 ∙ by Francesco Locatello, et al. ∙ 15 ∙ share

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On Robustness and Transferability of Convolutional Neural Networks

Modern deep convolutional networks (CNNs) are often criticized for not g...
07/16/2020 ∙ by Josip Djolonga, et al. ∙ 15 ∙ share

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Self-Supervised Learning of Video-Induced Visual Invariances

We propose a general framework for self-supervised learning of transfera...
12/05/2019 ∙ by Michael Tschannen, et al. ∙ 14 ∙ share

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Semantic Bottleneck Scene Generation

Coupling the high-fidelity generation capabilities of label-conditional ...
11/26/2019 ∙ by Samaneh Azadi, et al. ∙ 33 ∙ share

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On Mutual Information Maximization for Representation Learning

Many recent methods for unsupervised or self-supervised representation l...
07/31/2019 ∙ by Michael Tschannen, et al. ∙ 0 ∙ share

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Evaluating Generative Models Using Divergence Frontiers

Despite the tremendous progress in the estimation of generative models, ...
05/26/2019 ∙ by Josip Djolonga, et al. ∙ 0 ∙ share

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High-Fidelity Image Generation With Fewer Labels

Deep generative models are becoming a cornerstone of modern machine lear...
03/06/2019 ∙ by Mario Lucic, et al. ∙ 14 ∙ share

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Recent Advances in Autoencoder-Based Representation Learning

Learning useful representations with little or no supervision is a key c...
12/12/2018 ∙ by Michael Tschannen, et al. ∙ 0 ∙ share

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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

In recent years, the interest in unsupervised learning of disentangled r...
11/29/2018 ∙ by Francesco Locatello, et al. ∙ 4 ∙ share

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Self-Supervised Generative Adversarial Networks

Conditional GANs are at the forefront of natural image synthesis. The ma...
11/27/2018 ∙ by Ting Chen, et al. ∙ 0 ∙ share

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On Self Modulation for Generative Adversarial Networks

Training Generative Adversarial Networks (GANs) is notoriously challengi...
10/02/2018 ∙ by Ting Chen, et al. ∙ 0 ∙ share

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The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Generative Adversarial Networks (GANs) are a class of deep generative mo...
07/12/2018 ∙ by Karol Kurach, et al. ∙ 6 ∙ share

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Assessing Generative Models via Precision and Recall

Recent advances in generative modeling have led to an increased interest...
05/31/2018 ∙ by Mehdi S. M. Sajjadi, et al. ∙ 0 ∙ share

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Deep Generative Models for Distribution-Preserving Lossy Compression

We propose and study the problem of distribution-preserving lossy compre...
05/28/2018 ∙ by Michael Tschannen, et al. ∙ 2 ∙ share

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Are GANs Created Equal? A Large-Scale Study

Generative adversarial networks (GAN) are a powerful subclass of generat...
11/28/2017 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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One-Shot Coresets: The Case of k-Clustering

Scaling clustering algorithms to massive data sets is a challenging task...
11/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Stochastic Submodular Maximization: The Case of Coverage Functions

Stochastic optimization of continuous objectives is at the heart of mode...
11/05/2017 ∙ by Mohammad Reza Karimi, et al. ∙ 0 ∙ share

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Training Mixture Models at Scale via Coresets

How can we train a statistical mixture model on a massive data set? In t...
03/23/2017 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Practical Coreset Constructions for Machine Learning

We investigate coresets - succinct, small summaries of large data sets -...
03/19/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Uniform deviation bounds limit the difference between a model's expected...
02/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Scalable and Distributed Clustering via Lightweight Coresets

Coresets are compact representations of data sets such that models train...
02/27/2017 ∙ by Olivier Bachem, et al. ∙ 0 ∙ share

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Horizontally Scalable Submodular Maximization

A variety of large-scale machine learning problems can be cast as instan...
05/31/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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

Faced with massive data, is it possible to trade off (statistical) risk,...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Linear-time Outlier Detection via Sensitivity

Outliers are ubiquitous in modern data sets. Distance-based techniques a...
05/02/2016 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

Coresets are efficient representations of data sets such that models tra...
08/21/2015 ∙ by Mario Lucic, et al. ∙ 0 ∙ share

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Mario Lucic

Featured Co-authors

Bernhard Schölkopf

Trevor Darrell

Andreas Krause

Marco Cuturi

Amin Karbasi

Sylvain Gelly

Stefan Bauer

Hamed Hassani

Michael Tschannen

Gunnar Rätsch

Ting Chen

A Commentary on the Unsupervised Learning of Disentangled Representations

On Robustness and Transferability of Convolutional Neural Networks

Self-Supervised Learning of Video-Induced Visual Invariances

Semantic Bottleneck Scene Generation

On Mutual Information Maximization for Representation Learning

Evaluating Generative Models Using Divergence Frontiers

High-Fidelity Image Generation With Fewer Labels

Recent Advances in Autoencoder-Based Representation Learning

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Self-Supervised Generative Adversarial Networks

On Self Modulation for Generative Adversarial Networks

The GAN Landscape: Losses, Architectures, Regularization, and Normalization

Assessing Generative Models via Precision and Recall

Deep Generative Models for Distribution-Preserving Lossy Compression

Are GANs Created Equal? A Large-Scale Study

One-Shot Coresets: The Case of k-Clustering

Stochastic Submodular Maximization: The Case of Coverage Functions

Training Mixture Models at Scale via Coresets

Practical Coreset Constructions for Machine Learning

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

Scalable and Distributed Clustering via Lightweight Coresets

Horizontally Scalable Submodular Maximization

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

Linear-time Outlier Detection via Sensitivity

Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures