8 citations · 33 across the 38 of their papers we have counts for
10 papers · 1 filter
Free energy Estimation on Any State Space
Jiajun He, Zijing Ou, Francisco Vargas +4
Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformations, ranging from direct estimat…
A unified perspective on fine-tuning and sampling with diffusion and flow models
Carles Domingo-Enrich, Yuanqi Du, Michael S. Albergo
We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base density; a formulation that su…
Rare Event Analysis via Stochastic Optimal Control
Yuanqi Du, Jiajun He, Dinghuai Zhang +2
Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely d…
Conditioning Diffusions Using Malliavin Calculus
Jakiw Pidstrigach, Elizabeth Baker, Carles Domingo-Enrich +2
In generative modelling and stochastic optimal control, a central computational task is to modify a reference diffusion process to maximise a given terminal-time reward. Most exist…
Open Problem: Learning with Variational Objectives on Measures
Vivien Cabannes, Carles Domingo-Enrich
The theory of statistical learning has focused on variational objectives expressed on functions. In this note, we discuss motivations to write similar objectives on measures, in pa…
Compress Then Test: Powerful Kernel Testing in Near-linear Time
Carles Domingo-Enrich, Raaz Dwivedi, Lester Mackey
Kernel two-sample testing provides a powerful framework for distinguishing any pair of distributions based on sample points. However, existing kernel tests either run in …
Learning with Stochastic Orders
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). In this paper, we e…
Tighter Sparse Approximation Bounds for ReLU Neural Networks
Carles Domingo-Enrich, Youssef Mroueh
A well-known line of work (Barron, 1993; Breiman, 1993; Klusowski & Barron, 2018) provides bounds on the width of a ReLU two-layer neural network needed to approximate a functi…
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics
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 q…
Extragradient with player sampling for faster Nash equilibrium finding
Carles Domingo Enrich, Samy Jelassi, Carles Domingo-Enrich +3
Data-driven modeling increasingly requires to find a Nash equilibrium in multi-player games, e.g. when training GANs. In this paper, we analyse a new extra-gradient method for Nash…