EPFL Papers at NeurIPS 2019

List of Accepted EPFL Papers:

• Reducing Noise in GAN Training with Variance Reduced Extragradient
  Tatjana Chavdarova (Mila & Idiap & EPFL) · Gauthier Gidel (Mila) · François Fleuret (Idiap Research Institute & EPFL) · Simon Lacoste-Julien (Mila, Université de Montréal)
• A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
  Sulaiman Alghunaim (UCLA) · Kun Yuan (UCLA) · Ali H Sayed (EPFL)
• Approximate Inference Turns Deep Networks into Gaussian Processes
  Mohammad Emtiyaz Khan (RIKEN) · Alexander Immer (EPFL, RIKEN) · Ehsan Abedi (EPFL) · Maciej Korzepa (Technical University of Denmark)
• Backpropagation-Friendly Eigendecomposition
  Wei Wang (EPFL) · Zheng Dang (Xi'an Jiaotong University) · Yinlin Hu (EPFL) · Pascal Fua (EPFL, Switzerland) · Mathieu Salzmann (EPFL)
• Full-Gradient Representation for Neural Network Visualization
  Suraj Srinivas (Idiap Research Institute & EPFL) · François Fleuret (Idiap Research Institute & EPFL)
• Limitations of the Empirical Fisher Approximation
  Frederik Kunstner (EPFL) · Philipp Hennig (University of Tübingen and MPI for Intelligent Systems Tübingen) · Lukas Balles (University of Tuebingen)
• Unsupervised Scalable Representation Learning for Multivariate Time Series
  Jean-Yves Franceschi (Sorbonne Université) · Aymeric Dieuleveut (Ecole Polytechnique, IPP Paris) · Martin Jaggi (EPFL)
• UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization
  Ali Kavis (EPFL) · Yehuda Kfir Levy (ETH) · Francis Bach (INRIA - Ecole Normale Superieure) · Volkan Cevher (EPFL)
• Escaping from saddle points on Riemannian manifolds
  Yue Sun (University of Washington) · Nicolas Flammarion (EPFL) · Maryam Fazel (University of Washington)
• Coresets for Clustering with Fairness Constraints
  Lingxiao Huang (EPFL) · Shaofeng Jiang (Weizmann Institute of Science) · Nisheeth Vishnoi (Yale University)
• A General Theory of Equivariant CNNs on Homogeneous Spaces
  Taco S Cohen (Qualcomm AI Research) · Mario Geiger (EPFL) · Maurice Weiler (University of Amsterdam)
• GNNExplainer: Generating Explanations for Graph Neural Networks
  Zhitao Ying (Stanford University) · Dylan Bourgeois (EPFL) · Jiaxuan You (Stanford University) · Marinka Zitnik (Stanford University) · Jure Leskovec (Stanford University and Pinterest)
• Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias
  Stéphane d'Ascoli (ENS) · Levent Sagun (EPFL) · Giulio Biroli (ENS) · Joan Bruna (NYU)
• Fast and Provable ADMM for Learning with Generative Priors
  Fabian Latorre Gomez (EPFL) · Armin Eftekhari (EPFL) · Volkan Cevher (EPFL)
• A Domain Agnostic Measure for Monitoring and Evaluating GANs
  Paulina Grnarova (ETH Zurich) · Yehuda Kfir Levy (ETH) · Aurelien Lucchi (ETH Zurich) · Nathanael Perraudin (Swiss Data Science Center - EPFL / ETH Zurich) · Ian Goodfellow (Google) · Thomas Hofmann (ETH Zurich) · Andreas Krause (ETH Zurich)
• Learning Hawkes Processes from a handful of events
  Farnood Salehi (EPFL) · William Trouleau (EPFL) · Matthias Grossglauser (EPFL) · Patrick Thiran (EPFL)
• Provably robust boosted decision stumps and trees against adversarial attacks
  Maksym Andriushchenko (University of Tübingen / EPFL) · Matthias Hein (University of Tübingen)
• GOT: An Optimal Transport framework for Graph comparison
  Hermina Petric Maretic (EPFL) · Mireille El Gheche (EPFL) · Giovanni Chierchia (ESIEE Paris) · Pascal Frossard (EPFL)
• Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization
  Viet Anh Nguyen (EPFL) · Soroosh Shafieezadeh Abadeh (EPFL) · Man-Chung Yue (The Hong Kong Polytechnic University) · Daniel Kuhn (EPFL) · Wolfram Wiesemann (Imperial College)
• An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints
  Mehmet Fatih SAHIN (EPFL) · Armin eftekhari (EPFL) · Ahmet Alacaoglu (EPFL) · Fabian Latorre Gomez (EPFL) · Volkan Cevher (EPFL)
• PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
  Thijs Vogels (EPFL) · Sai Praneeth Reddy Karimireddy (EPFL) · Martin Jaggi (EPFL)
• Stochastic Frank-Wolfe for Composite Convex Minimization
  Francesco Locatello (ETH Zürich - MPI Tübingen) · Alp Yurtsever (EPFL) · Olivier Fercoq (Telecom ParisTech) · Volkan Cevher (EPFL)
• Efficiently Learning Fourier Sparse Set Functions
  Andisheh Amrollahi (ETH Zurich) · Amir Zandieh (EPFL) · Michael Kapralov (EPFL) · Andreas Krause (ETH Zurich)
• DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging
  Matthieu Simeoni (IBM Research / EPFL) · Sepand Kashani (EPFL) · Paul Hurley (Western Sydney University) · Martin Vetterli (EPFL)
• Distributionally Optimistic Optimization Approach to Nonparametric Likelihood Approximation
  Viet Anh Nguyen (EPFL) · Soroosh Shafieezadeh Abadeh (EPFL) · Man-Chung Yue (The Hong Kong Polytechnic University) · Daniel Kuhn (EPFL) · Wolfram Wiesemann (Imperial College)

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