21 citations · 45 across the 14 of their papers we have counts for
19 papers
Strategic Distribution Shift of Interacting Agents via Coupled Gradient Flows
Lauren Conger, Franca Hoffmann, Eric Mazumdar +1
We propose a novel framework for analyzing the dynamics of distribution shift in real-world systems that captures the feedback loop between learning algorithms and the distribution…
Bayesian Posterior Perturbation Analysis with Integral Probability Metrics
Alfredo Garbuno-Inigo, Tapio Helin, Franca Hoffmann +1
In recent years, Bayesian inference in large-scale inverse problems found in science, engineering and machine learning has gained significant attention. This paper examines the rob…
Covariance-modulated optimal transport and gradient flows
Martin Burger, Matthias Erbar, Franca Hoffmann +2
We study a variant of the dynamical optimal transport problem in which the energy to be minimised is modulated by the covariance matrix of the distribution. Such transport metrics…
Convergence rates for ansatz-free data-driven inference in physically constrained problems
Sergio Conti, Franca Hoffmann, Michael Ortiz
We study a Data-Driven approach to inference in physical systems in a measure-theoretic framework. The systems under consideration are characterized by two measures defined over th…
Model-free Data-Driven Inference
Sergio Conti, Franca Hoffmann, Michael Ortiz
We present a model-free data-driven inference method that enables inferences on system outcomes to be derived directly from empirical data without the need for intervening modeling…
Consensus Based Sampling
J. A. Carrillo, F. Hoffmann, A. M. Stuart +1
We propose a novel method for sampling and optimization tasks based on a stochastic interacting particle system. We explain how this method can be used for the following two goals:…