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Found 20 papers, 5 papers with code
Dual Ensemble Kalman Filter for Stochastic Optimal Control
In this paper, stochastic optimal control problems in continuous time and space are considered.
Time-Reversal of Stochastic Maximum Principle
The condition involves a coupled system of forward and backward stochastic differential equations (FBSDE) for the state and the adjoint processes.
Conditional Optimal Transport on Function Spaces
We present a systematic study of conditional triangular transport maps in function spaces from the perspective of optimal transportation and with a view towards amortized Bayesian inference.
Nonlinear Filtering with Brenier Optimal Transport Maps
This paper is concerned with the problem of nonlinear filtering, i. e., computing the conditional distribution of the state of a stochastic dynamical system given a history of noisy partial observations.
A matching principle for power transfer in Stochastic Thermodynamics
Gradients in temperature and particle concentration fuel many processes in the physical and biological world.
A Survey of Feedback Particle Filter and related Controlled Interacting Particle Systems (CIPS)
In this survey, we describe controlled interacting particle systems (CIPS) to approximate the solution of the optimal filtering and the optimal control problems.
Duality-Based Stochastic Policy Optimization for Estimation with Unknown Noise Covariances
Specifically, building on the duality between synthesizing optimal control and estimation gains, the filter design problem is formalized as direct policy learning.
Geometry of finite-time thermodynamic cycles with anisotropic thermal fluctuations
In contrast to the classical concept of a Carnot engine that alternates contact between heat baths of different temperatures, naturally occurring processes usually harvest energy from anisotropy, being exposed simultaneously to chemical and thermal fluctuations of different intensities.
An Optimal Transport Formulation of Bayes' Law for Nonlinear Filtering Algorithms
This paper presents a variational representation of the Bayes' law using optimal transportation theory.
Variational Wasserstein gradient flow
Wasserstein gradient flow has emerged as a promising approach to solve optimization problems over the space of probability distributions.
Controlled Interacting Particle Algorithms for Simulation-based Reinforcement Learning
This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions.
Deep FPF: Gain function approximation in high-dimensional setting
The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian.
Scalable Computations of Wasserstein Barycenter via Input Convex Neural Networks
Wasserstein Barycenter is a principled approach to represent the weighted mean of a given set of probability distributions, utilizing the geometry induced by optimal transport.
An Optimal Transport Formulation of the Ensemble Kalman Filter
For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter.
Optimal transport mapping via input convex neural networks
Building upon recent advances in the field of input convex neural networks, we propose a new framework where the gradient of one convex function represents the optimal transport mapping.
2-Wasserstein Approximation via Restricted Convex Potentials with Application to Improved Training for GANs
We provide a framework to approximate the 2-Wasserstein distance and the optimal transport map, amenable to efficient training as well as statistical and geometric analysis.
Accelerated Flow for Probability Distributions
al. 2016) from vector valued variables to probability distributions.
Accelerated Gradient Flow for Probability Distributions
In particular, we extend the recent variational formulation of accelerated gradient methods in wibisono2016 from vector valued variables to probability distributions.
How regularization affects the critical points in linear networks
The formulation is used to provide a complete characterization of the critical points in terms of the solutions of a nonlinear matrix-valued equation, referred to as the characteristic equation.
Adversarial Delays in Online Strongly-Convex Optimization
We consider the problem of strongly-convex online optimization in presence of adversarial delays; in a T-iteration online game, the feedback of the player's query at time t is arbitrarily delayed by an adversary for d_t rounds and delivered before the game ends, at iteration t+d_t-1.
