no code implementations • ICML 2020 • Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna

Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e. g. when training GANs.

no code implementations • 2 Oct 2024 • Chentong Wang, Sarah Alamdari, Carles Domingo-Enrich, Ava Amini, Kevin K. Yang

Deep generative models that learn from the distribution of natural protein sequences and structures may enable the design of new proteins with valuable functions.

no code implementations • 1 Oct 2024 • Carles Domingo-Enrich

Stochastic optimal control (SOC) aims to direct the behavior of noisy systems and has widespread applications in science, engineering, and artificial intelligence.

no code implementations • 13 Sep 2024 • Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, Ricky T. Q. Chen

Dynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not been many theoretically-sound methods for improving these models with reward fine-tuning.

1 code implementation • 1 Jun 2024 • Aram-Alexandre Pooladian, Carles Domingo-Enrich, Ricky T. Q. Chen, Brandon Amos

We investigate the optimal transport problem between probability measures when the underlying cost function is understood to satisfy a least action principle, also known as a Lagrangian cost.

1 code implementation • 4 Dec 2023 • Carles Domingo-Enrich, Jiequn Han, Brandon Amos, Joan Bruna, Ricky T. Q. Chen

Our work introduces Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control that stems from the same philosophy as the conditional score matching loss for diffusion models.

no code implementations • 27 Jun 2023 • Samy Jelassi, Stéphane d'Ascoli, Carles Domingo-Enrich, Yuhuai Wu, Yuanzhi Li, François Charton

We find that relative position embeddings enable length generalization for simple tasks, such as addition: models trained on $5$-digit numbers can perform $15$-digit sums.

no code implementations • 20 Jun 2023 • Vivien Cabannes, Carles Domingo-Enrich

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

Out-of-Distribution Generalization Weakly-supervised Learning

no code implementations • 28 Apr 2023 • Aram-Alexandre Pooladian, Heli Ben-Hamu, Carles Domingo-Enrich, Brandon Amos, Yaron Lipman, Ricky T. Q. Chen

Simulation-free methods for training continuous-time generative models construct probability paths that go between noise distributions and individual data samples.

no code implementations • 23 Feb 2023 • Carles Domingo-Enrich, Aram-Alexandre Pooladian

In this short note, we complement these existing results in the literature by providing an explicit expansion of $\text{KL}(\rho_t^{\text{FR}}\|\pi)$ in terms of $e^{-t}$, where $(\rho_t^{\text{FR}})_{t\geq 0}$ is the FR gradient flow of the KL divergence.

1 code implementation • 14 Jan 2023 • Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey

To address these shortcomings, we introduce Compress Then Test (CTT), a new framework for high-powered kernel testing based on sample compression.

1 code implementation • 1 Jun 2022 • Carles Domingo-Enrich

When solving finite-sum minimization problems, two common alternatives to stochastic gradient descent (SGD) with theoretical benefits are random reshuffling (SGD-RR) and shuffle-once (SGD-SO), in which functions are sampled in cycles without replacement.

1 code implementation • 27 May 2022 • Carles Domingo-Enrich, Yair Schiff, Youssef Mroueh

Learning high-dimensional distributions is often done with explicit likelihood modeling or implicit modeling via minimizing integral probability metrics (IPMs).

1 code implementation • 27 May 2022 • Carles Domingo-Enrich, Youssef Mroueh

Differential privacy (DP) is the de facto standard for private data release and private machine learning.

no code implementations • 14 Feb 2022 • Carles Domingo-Enrich, Joan Bruna

Min-max optimization problems arise in several key machine learning setups, including adversarial learning and generative modeling.

no code implementations • 27 Dec 2021 • Carles Domingo-Enrich

We construct pairs of distributions $\mu_d, \nu_d$ on $\mathbb{R}^d$ such that the quantity $|\mathbb{E}_{x \sim \mu_d} [F(x)] - \mathbb{E}_{x \sim \nu_d} [F(x)]|$ decreases as $\Omega(1/d^2)$ for some three-layer ReLU network $F$ with polynomial width and weights, while declining exponentially in $d$ if $F$ is any two-layer network with polynomial weights.

no code implementations • ICLR 2022 • Carles Domingo-Enrich, Youssef Mroueh

A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski & Barron, 2018) provides bounds on the width $n$ of a ReLU two-layer neural network needed to approximate a function $f$ over the ball $\mathcal{B}_R(\mathbb{R}^d)$ up to error $\epsilon$, when the Fourier based quantity $C_f = \frac{1}{(2\pi)^{d/2}} \int_{\mathbb{R}^d} \|\xi\|^2 |\hat{f}(\xi)| \ d\xi$ is finite.

no code implementations • 11 Jul 2021 • Carles Domingo-Enrich, Alberto Bietti, Marylou Gabrié, Joan Bruna, Eric Vanden-Eijnden

In the feature-learning regime, this dual formulation justifies using a two time-scale gradient ascent-descent (GDA) training algorithm in which one updates concurrently the particles in the sample space and the neurons in the parameter space of the energy.

no code implementations • NeurIPS 2021 • Carles Domingo-Enrich, Youssef Mroueh

Several works in implicit and explicit generative modeling empirically observed that feature-learning discriminators outperform fixed-kernel discriminators in terms of the sample quality of the models.

1 code implementation • 15 Apr 2021 • Carles Domingo-Enrich, Alberto Bietti, Eric Vanden-Eijnden, Joan Bruna

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

no code implementations • ICLR 2021 • Carles Domingo-Enrich, Fabian Pedregosa, Damien Scieur

First, we show that for zero-sum bilinear games the average-case optimal method is the optimal method for the minimization of the Hamiltonian.

no code implementations • NeurIPS 2020 • Carles Domingo-Enrich, Samy Jelassi, Arthur Mensch, Grant Rotskoff, Joan Bruna

Our method identifies mixed equilibria in high dimensions and is demonstrably effective for training mixtures of GANs.

1 code implementation • 29 May 2019 • Carles Domingo Enrich, Samy Jelassi, Carles Domingo-Enrich, Damien Scieur, Arthur Mensch, Joan Bruna

Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e. g. when training GANs.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.