research
∙
04/21/2023
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
Data collected from the real world tends to be biased, unbalanced, and a...
share
research
∙
12/25/2022
Gromov-Wasserstein Distances: Entropic Regularization, Duality, and Sample Complexity
The Gromov-Wasserstein (GW) distance quantifies dissimilarity between me...
research
∙
12/08/2022
Effective Dynamics of Generative Adversarial Networks
Generative adversarial networks (GANs) are a class of machine-learning m...
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...
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...
research
∙
05/27/2022
Learning with Stochastic Orders
Learning high-dimensional distributions is often done with explicit like...
research
∙
11/22/2021
Cycle Consistent Probability Divergences Across Different Spaces
Discrepancy measures between probability distributions are at the core o...
research
∙
11/10/2021
Physics-enhanced deep surrogates for PDEs
We present a "physics-enhanced deep-surrogate ("PEDS") approach towards ...
research
∙
10/07/2021
Tighter Sparse Approximation Bounds for ReLU Neural Networks
A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski Ba...
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...
research
∙
06/10/2021
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
Several works in implicit and explicit generative modeling empirically o...
research
∙
06/07/2021
Measuring Generalization with Optimal Transport
Understanding the generalization of deep neural networks is one of the m...
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...
research
∙
03/11/2021
Fair Mixup: Fairness via Interpolation
Training classifiers under fairness constraints such as group fairness, ...
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...
research
∙
12/21/2020
Alleviating Noisy Data in Image Captioning with Cooperative Distillation
Image captioning systems have made substantial progress, largely due to ...
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...
research
∙
11/03/2020
Tabular Transformers for Modeling Multivariate Time Series
Tabular datasets are ubiquitous in data science applications. Given thei...
research
∙
09/29/2020
Unbalanced Sobolev Descent
We introduce Unbalanced Sobolev Descent (USD), a particle descent algori...
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...
research
∙
07/06/2020
Kernel Stein Generative Modeling
We are interested in gradient-based Explicit Generative Modeling where s...
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 ...
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 ...
research
∙
05/07/2020
Learning Implicit Text Generation via Feature Matching
Generative feature matching network (GFMN) is an approach for training i...
research
∙
02/04/2020
Improving Efficiency in Large-Scale Decentralized Distributed Training
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchr...
research
∙
01/31/2020
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling
We study convergence of a generative modeling method that first estimate...
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...
research
∙
11/06/2019
Unsupervised Hierarchy Matching with Optimal Transport over Hyperbolic Spaces
This paper focuses on the problem of unsupervised alignment of hierarchi...
research
∙
10/31/2019
Sobolev Independence Criterion
We propose the Sobolev Independence Criterion (SIC), an interpretable de...
research
∙
10/28/2019
Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Generative Adversarial Networks (GANs) are powerful class of generative ...
research
∙
05/30/2019
Wasserstein Style Transfer
We propose Gaussian optimal transport for Image style transfer in an Enc...
research
∙
04/04/2019
Learning Implicit Generative Models by Matching Perceptual Features
Perceptual features (PFs) have been used with great success in tasks suc...
research
∙
02/26/2019
Implicit Kernel Learning
Kernels are powerful and versatile tools in machine learning and statist...
research
∙
02/13/2019
Wasserstein Barycenter Model Ensembling
In this paper we propose to perform model ensembling in a multiclass or ...
research
∙
05/30/2018
Regularized Kernel and Neural Sobolev Descent: Dynamic MMD Transport
We introduce Regularized Kernel and Neural Sobolev Descent for transport...
research
∙
05/16/2018
Regularized Finite Dimensional Kernel Sobolev Discrepancy
We show in this note that the Sobolev Discrepancy introduced in Mroueh e...
research
∙
04/30/2018
Improved Image Captioning with Adversarial Semantic Alignment
In this paper we propose a new conditional GAN for image captioning that...
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...
research
∙
11/14/2017
Sobolev GAN
We propose a new Integral Probability Metric (IPM) between distributions...
research
∙
05/26/2017
Fisher GAN
Generative Adversarial Networks (GANs) are powerful models for learning ...
research
∙
02/27/2017
McGan: Mean and Covariance Feature Matching GAN
We introduce new families of Integral Probability Metrics (IPM) for trai...
research
∙
12/02/2016
Self-critical Sequence Training for Image Captioning
Recently it has been shown that policy-gradient methods for reinforcemen...
research
∙
06/11/2015
Random Maxout Features
In this paper, we propose and study random maxout features, which are co...
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...
research
∙
01/22/2015
Deep Multimodal Learning for Audio-Visual Speech Recognition
In this paper, we present methods in deep multimodal learning for fusing...
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...
research
∙
09/06/2012
