Youssef Mroueh

research

∙ 12/25/2022

Gromov-Wasserstein Distances: Entropic Regularization, Duality, and Sample Complexity

The Gromov-Wasserstein (GW) distance quantifies dissimilarity between me...

Zhengxin Zhang, et al. ∙

research

∙ 12/08/2022

Effective Dynamics of Generative Adversarial Networks

Generative adversarial networks (GANs) are a class of machine-learning m...

Steven Durr, et al. ∙

research

∙ 08/13/2022

Cloud-Based Real-Time Molecular Screening Platform with MolFormer

With the prospect of automating a number of chemical tasks with high fid...

Brian Belgodere, et al. ∙

research

∙ 05/27/2022

Auditing Differential Privacy in High Dimensions with the Kernel Quantum Rényi Divergence

Differential privacy (DP) is the de facto standard for private data rele...

Carles Domingo Enrich, et al. ∙

research

∙ 05/27/2022

Learning with Stochastic Orders

Learning high-dimensional distributions is often done with explicit like...

Carles Domingo Enrich, et al. ∙

research

∙ 11/22/2021

Cycle Consistent Probability Divergences Across Different Spaces

Discrepancy measures between probability distributions are at the core o...

Zhengxin Zhang, et al. ∙

research

∙ 11/10/2021

Physics-enhanced deep surrogates for PDEs

We present a "physics-enhanced deep-surrogate ("PEDS") approach towards ...

Raphaël Pestourie, et al. ∙

research

∙ 10/07/2021

Tighter Sparse Approximation Bounds for ReLU Neural Networks

A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski Ba...

Carles Domingo Enrich, et al. ∙

research

∙ 06/17/2021

Do Large Scale Molecular Language Representations Capture Important Structural Information?

Predicting chemical properties from the structure of a molecule is of gr...

Jerret Ross, et al. ∙

research

∙ 06/10/2021

Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics

Several works in implicit and explicit generative modeling empirically o...

Carles Domingo Enrich, et al. ∙

research

∙ 06/07/2021

Measuring Generalization with Optimal Transport

Understanding the generalization of deep neural networks is one of the m...

Ching-Yao Chuang, et al. ∙

research

∙ 06/01/2021

Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks

Gradient flows are a powerful tool for optimizing functionals in general...

David Alvarez-Melis, et al. ∙

research

∙ 03/11/2021

Fair Mixup: Fairness via Interpolation

Training classifiers under fairness constraints such as group fairness, ...

Ching-Yao Chuang, et al. ∙

research

∙ 12/21/2020

Image Captioning as an Assistive Technology: Lessons Learned from VizWiz 2020 Challenge

Image captioning has recently demonstrated impressive progress largely o...

Pierre Dognin, et al. ∙

research

∙ 12/21/2020

Alleviating Noisy Data in Image Captioning with Cooperative Distillation

Image captioning systems have made substantial progress, largely due to ...

Pierre Dognin, et al. ∙

research

∙ 11/04/2020

On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow

We consider the maximum mean discrepancy (MMD) GAN problem and propose a...

Youssef Mroueh, et al. ∙

research

∙ 11/03/2020

Tabular Transformers for Modeling Multivariate Time Series

Tabular datasets are ubiquitous in data science applications. Given thei...

Inkit Padhi, et al. ∙

research

∙ 09/29/2020

Unbalanced Sobolev Descent

We introduce Unbalanced Sobolev Descent (USD), a particle descent algori...

Youssef Mroueh, et al. ∙

research

∙ 08/24/2020

Active learning of deep surrogates for PDEs: Application to metasurface design

Surrogate models for partial-differential equations are widely used in t...

Raphaël Pestourie, et al. ∙

research

∙ 07/06/2020

Kernel Stein Generative Modeling

We are interested in gradient-based Explicit Generative Modeling where s...

Wei-Cheng Chang, et al. ∙

research

∙ 06/19/2020

Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis

Recently, the task of image generation has attracted much attention. In ...

Adam Block, et al. ∙

research

∙ 06/19/2020

Fast Mixing of Multi-Scale Langevin Dynamics underthe Manifold Hypothesis

Recently, the task of image generation has attracted much attention. In ...

Adam Block, et al. ∙

research

∙ 05/07/2020

Learning Implicit Text Generation via Feature Matching

Generative feature matching network (GFMN) is an approach for training i...

Inkit Padhi, et al. ∙

research

∙ 02/04/2020

Improving Efficiency in Large-Scale Decentralized Distributed Training

Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchr...

Wei Zhang, et al. ∙

research

∙ 01/31/2020

Generative Modeling with Denoising Auto-Encoders and Langevin Sampling

We study convergence of a generative modeling method that first estimate...

Adam Block, et al. ∙

research

∙ 12/26/2019

Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets

Adaptive gradient algorithms perform gradient-based updates using the hi...

Mingrui Liu, et al. ∙

research

∙ 11/06/2019

Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces

This paper focuses on the problem of unsupervised alignment of hierarchi...

David Alvarez-Melis, et al. ∙

research

∙ 10/31/2019

Sobolev Independence Criterion

We propose the Sobolev Independence Criterion (SIC), an interpretable de...

Youssef Mroueh, et al. ∙

research

∙ 10/28/2019

Decentralized Parallel Algorithm for Training Generative Adversarial Nets

Generative Adversarial Networks (GANs) are powerful class of generative ...

Mingrui Liu, et al. ∙

research

∙ 05/30/2019

Wasserstein Style Transfer

We propose Gaussian optimal transport for Image style transfer in an Enc...

Youssef Mroueh, et al. ∙

research

∙ 04/04/2019

Learning Implicit Generative Models by Matching Perceptual Features

Perceptual features (PFs) have been used with great success in tasks suc...

Cicero Nogueira dos Santos, et al. ∙

research

∙ 02/26/2019

Implicit Kernel Learning

Kernels are powerful and versatile tools in machine learning and statist...

Chun-Liang Li, et al. ∙

research

∙ 02/13/2019

Wasserstein Barycenter Model Ensembling

In this paper we propose to perform model ensembling in a multiclass or ...

Pierre Dognin, et al. ∙

research

∙ 05/30/2018

Regularized Kernel and Neural Sobolev Descent: Dynamic MMD Transport

We introduce Regularized Kernel and Neural Sobolev Descent for transport...

Youssef Mroueh, et al. ∙

research

∙ 05/16/2018

Regularized Finite Dimensional Kernel Sobolev Discrepancy

We show in this note that the Sobolev Discrepancy introduced in Mroueh e...

Youssef Mroueh, et al. ∙

research

∙ 04/30/2018

Improved Image Captioning with Adversarial Semantic Alignment

In this paper we propose a new conditional GAN for image captioning that...

Igor Melnyk, et al. ∙

research

∙ 12/07/2017

Semi-Supervised Learning with IPM-based GANs: an Empirical Study

We present an empirical investigation of a recent class of Generative Ad...

Tom Sercu, et al. ∙

research

∙ 11/14/2017

Sobolev GAN

We propose a new Integral Probability Metric (IPM) between distributions...

Youssef Mroueh, et al. ∙

research

∙ 05/26/2017

Fisher GAN

Generative Adversarial Networks (GANs) are powerful models for learning ...

Youssef Mroueh, et al. ∙

research

∙ 02/27/2017

McGan: Mean and Covariance Feature Matching GAN

We introduce new families of Integral Probability Metrics (IPM) for trai...

Youssef Mroueh, et al. ∙

research

∙ 12/02/2016

Self-critical Sequence Training for Image Captioning

Recently it has been shown that policy-gradient methods for reinforcemen...

Steven J. Rennie, et al. ∙

research

∙ 06/11/2015

Random Maxout Features

In this paper, we propose and study random maxout features, which are co...

Youssef Mroueh, et al. ∙

research

∙ 06/08/2015

Learning with Group Invariant Features: A Kernel Perspective

We analyze in this paper a random feature map based on a theory of invar...

Youssef Mroueh, et al. ∙

research

∙ 01/22/2015

Deep Multimodal Learning for Audio-Visual Speech Recognition

In this paper, we present methods in deep multimodal learning for fusing...

Youssef Mroueh, et al. ∙

research

∙ 11/16/2013

Can a biologically-plausible hierarchy effectively replace face detection, alignment, and recognition pipelines?

The standard approach to unconstrained face recognition in natural photo...

Qianli Liao, et al. ∙

research

∙ 09/06/2012

Multiclass Learning with Simplex Coding

In this paper we discuss a novel framework for multiclass learning, defi...

Youssef Mroueh, et al. ∙

Youssef Mroueh

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