Carles Domingo Enrich | DeepAI

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∙ 06/27/2023

Length Generalization in Arithmetic Transformers

We examine how transformers cope with two challenges: learning basic int...

0 Samy Jelassi, et al. ∙

∙ 06/20/2023

Open Problem: Learning with Variational Objectives on Measures

The theory of statistical learning has focused on variational objectives...

0 Vivien Cabannes, et al. ∙

∙ 04/28/2023

Multisample Flow Matching: Straightening Flows with Minibatch Couplings

Simulation-free methods for training continuous-time generative models c...

1 Aram-Alexandre Pooladian, et al. ∙

∙ 02/23/2023

An Explicit Expansion of the Kullback-Leibler Divergence along its Fisher-Rao Gradient Flow

Let V_* : ℝ^d →ℝ be some (possibly non-convex) potential function, and c...

0 Carles Domingo Enrich, et al. ∙

∙ 01/14/2023

Compress Then Test: Powerful Kernel Testing in Near-linear Time

Kernel two-sample testing provides a powerful framework for distinguishi...

0 Carles Domingo Enrich, et al. ∙

∙ 06/01/2022

Computing the Variance of Shuffling Stochastic Gradient Algorithms via Power Spectral Density Analysis

When solving finite-sum minimization problems, two common alternatives t...

0 Carles Domingo Enrich, et al. ∙

∙ 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...

0 Carles Domingo Enrich, et al. ∙

∙ 05/27/2022

Learning with Stochastic Orders

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

5 Carles Domingo Enrich, et al. ∙

∙ 02/14/2022

Simultaneous Transport Evolution for Minimax Equilibria on Measures

Min-max optimization problems arise in several key machine learning setu...

0 Carles Domingo Enrich, et al. ∙

∙ 12/27/2021

Depth and Feature Learning are Provably Beneficial for Neural Network Discriminators

We construct pairs of distributions μ_d, ν_d on ℝ^d such that the quanti...

0 Carles Domingo Enrich, et al. ∙

∙ 10/07/2021

Tighter Sparse Approximation Bounds for ReLU Neural Networks

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

0 Carles Domingo Enrich, et al. ∙

∙ 07/11/2021

Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks

Energy-based models (EBMs) are generative models that are usually traine...

6 Carles Domingo Enrich, et al. ∙

∙ 06/10/2021

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

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

0 Carles Domingo Enrich, et al. ∙

∙ 04/15/2021

On Energy-Based Models with Overparametrized Shallow Neural Networks

Energy-based models (EBMs) are a simple yet powerful framework for gener...

15 Carles Domingo Enrich, et al. ∙

∙ 10/05/2020

Average-case Acceleration for Bilinear Games and Normal Matrices

Advances in generative modeling and adversarial learning have given rise...

0 Carles Domingo Enrich, et al. ∙

∙ 02/14/2020

A mean-field analysis of two-player zero-sum games

Finding Nash equilibria in two-player zero-sum continuous games is a cen...

5 Carles Domingo Enrich, et al. ∙

∙ 05/29/2019

Extra-gradient with player sampling for provable fast convergence in n-player games

Data-driven model training is increasingly relying on finding Nash equil...

0 Samy Jelassi, et al. ∙

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