Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, ...

The new era of shared economy has raised our expectations to make mobility more sustainable through better utilization of existing resources and capacity. In this thesis, we focus on the design of transport systems that stimulate multi-purpose trips with t ...

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market. When the vehicles are idle, they can choose whether to continue cruising to search for passengers, or move a charging station to recharge. The ...

We study the performance of Markov chains for the q-state ferromagnetic Potts model on random regular graphs. While the cases of the grid and the complete graph are by now well-understood, the case of random regular graphs has resisted a detailed analysis ...

In this work, we present, analyze, and implement a class of multilevel Markov chain Monte Carlo(ML-MCMC) algorithms based on independent Metropolis--Hastings proposals for Bayesian inverse problems. In this context, the likelihood function involves solving ...

In this thesis, we propose and analyze novel numerical algorithms for solving three different high-dimensional problems involving tensors. The commonality of these problems is that the tensors can potentially be well approximated in low-rank formats. Ident ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In ...

Population equations for infinitely large networks of spiking neurons have a long tradition in theoret-ical neuroscience. In this work, we analyze a recent generalization of these equations to populations of finite size, which takes the form of a nonlinear ...

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

This thesis is devoted to the construction, analysis, and implementation of two types of hierarchical Markov Chain Monte Carlo (MCMC) methods for the solution of large-scale Bayesian Inverse Problems (BIP). The first hierarchical method we present is base ...

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by ...

Consider a linear elliptic PDE defined over a stochastic stochastic geometry a function of N random variables. In many application, quantify the uncertainty propagated to a quantity of interest (QoI) is an important problem. The random domain is split into ...

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with d states. We propose a data-driven d ...

Car-sharing systems are an attractive alternative to private vehicles due to their benefits in terms of mobility and sustainability. However, the distribution of vehicles throughout the network in one-way systems is disturbed due to asymmetry and stochasti ...

Adaptability and ease of programming are key features necessary for a wider spread of robotics in factories and everyday assistance. Learning from demonstration (LfD) is an approach to address this problem. It aims to develop algorithms and interfaces such ...

Finding Steady States of Communicating Markov Processes Combining Aggregation/Disaggregation with Tensor Techniques

Chat with Graph Search

On distributional autoregression and iterated transportation

Quantitative Methods for Omnichannel Decision-Making

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Optimizing the utilization of existing vehicle flows in last-mile passenger transport and logistics systems

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Routing and charging game in ride-hailing service with electric vehicles

Metastability of the Potts Ferromagnet on Random Regular Graphs

Analysis of a Class of Multilevel Markov Chain Monte Carlo Algorithms Based on Independent Metropolis–Hastings

Low-Rank Tensor Methods for High-Dimensional Problems

Distributional Regression and Autoregression via Optimal Transport

Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

On a Finite-Size Neuronal Population Equation

Hidden Markov Modeling Over Graphs

A note on universal inference

HIERARCHICAL MARKOV CHAIN MONTE CARLO METHODS FOR BAYESIAN INVERSE PROBLEMS

PDE-Aware Deep Learning for Inverse Problems in Cardiac Electrophysiology

A hybrid collocation-perturbation approach for PDEs with random domains

Distributionally Robust Optimization with Markovian Data

Predictive user-based relocation through incentives in one-way car-sharing systems

Product of experts for robot learning from demonstration