18 citations · 77 across the 20 of their papers we have counts for
24 papers
Scalable estimation of VARMA models
Daniel Paulin, Victor Elvira
Vector autoregressive moving-average (VARMA) models have long been considered impractical beyond moderate dimensions: the likelihood is non-convex, the parametrization is identifie…
Contraction Rate Estimates of Stochastic Gradient Kinetic Langevin Integrators
Benedict Leimkuhler, Daniel Paulin, Peter A. Whalley
In previous work, we introduced a method for determining convergence rates for integration methods for the kinetic Langevin equation for -Lipschitz -log-concave densi…
Estimation of Multivariate Discrete Hawkes Processes: An Application to Incident Monitoring
Trinnhallen Brisley, Gordon Ross, Daniel Paulin +1
Hawkes processes are a class of self-exciting point processes that are used to model complex phenomena. While most applications of Hawkes processes assume that event data occurs in…
Contraction and Convergence Rates for Discretized Kinetic Langevin Dynamics
Benedict Leimkuhler, Daniel Paulin, Peter A. Whalley
We provide a framework to analyze the convergence of discretized kinetic Langevin dynamics for -Lipschitz, -convex potentials. Our approach gives convergence rates of…
Randomized Time Riemannian Manifold Hamiltonian Monte Carlo
Peter A. Whalley, Daniel Paulin, Benedict Leimkuhler
Hamiltonian Monte Carlo (HMC) algorithms which combine numerical approximation of Hamiltonian dynamics on finite intervals with stochastic refreshment and Metropolis correction are…
Geo-political conflicts, economic sanctions and international knowledge flows
Teemu Makkonen, Timo Mitze
We address the question how sensitive international knowledge flows respond to geo-political conflicts taking the politico-economic tensions between EU-Russia since the Ukraine cri…
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting
Maxime Vono, Daniel Paulin, Arnaud Doucet
Performing exact Bayesian inference for complex models is computationally intractable. Markov chain Monte Carlo (MCMC) algorithms can provide reliable approximations of the posteri…
Dual Space Preconditioning for Gradient Descent
Chris J. Maddison, Daniel Paulin, Yee Whye Teh +1
The conditions of relative smoothness and relative strong convexity were recently introduced for the analysis of Bregman gradient methods for convex optimization. We introduce a ge…
Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-Free Convergence Rates
George Deligiannidis, Daniel Paulin, Alexandre Bouchard-Côté +1
The Bouncy Particle Sampler is a Markov chain Monte Carlo method based on a nonreversible piecewise deterministic Markov process. In this scheme, a particle explores the state spac…
A 4D-Var Method with Flow-Dependent Background Covariances for the Shallow-Water Equations
Daniel Paulin, Ajay Jasra, Alexandros Beskos +1
The 4D-Var method for filtering partially observed nonlinear chaotic dynamical systems consists of finding the maximum a-posteriori (MAP) estimator of the initial condition of the…
Optimization Based Methods for Partially Observed Chaotic Systems
Daniel Paulin, Ajay Jasra, Dan Crisan +1
In this paper we consider filtering and smoothing of partially observed chaotic dynamical systems that are discretely observed, with an additive Gaussian noise in the observation.…
On Concentration Properties of Partially Observed Chaotic Systems
Daniel Paulin, Ajay Jasra, Dan Crisan +1
This article presents results on the concentration properties of the smoothing and filtering distributions of some partially observed chaotic dynamical systems. We show that, rathe…
Error Bounds for Sequential Monte Carlo Samplers for Multimodal Distributions
Daniel Paulin, Ajay Jasra, Alexandre Thiery
In this paper, we provide bounds on the asymptotic variance for a class of sequential Monte Carlo (SMC) samplers designed for approximating multimodal distributions. Such methods c…
Probabilistic verification of partially observable dynamical systems
Benjamin M. Gyori, Daniel Paulin, Sucheendra K. Palaniappan
The construction and formal verification of dynamical models is important in engineering, biology and other disciplines. We focus on non-linear models containing a set of parameter…
Hypothesis testing for Markov chain Monte Carlo
Benjamin M. Gyori, Daniel Paulin
Testing between hypotheses, when independent sampling is possible, is a well developed subject. In this paper, we propose hypothesis tests that are applicable when the samples are…
Efron-Stein Inequalities for Random Matrices
Daniel Paulin, Lester Mackey, Joel A. Tropp
This paper establishes new concentration inequalities for random matrices constructed from independent random variables. These results are analogous with the generalized Efron-Stei…
Mixing and Concentration by Ricci Curvature
Daniel Paulin
We generalise the coarse Ricci curvature method of Ollivier by considering the coarse Ricci curvature of multiple steps in the Markov chain. This implies new spectral bounds and co…
Deriving Matrix Concentration Inequalities from Kernel Couplings
Daniel Paulin, Lester Mackey, Joel A. Tropp
This paper derives exponential tail bounds and polynomial moment inequalities for the spectral norm deviation of a random matrix from its mean value. The argument depends on a matr…
Concentration Inequalities in Locally Dependent Spaces
Daniel Paulin
This paper studies concentration inequalities for functions of locally dependent random variables. We show that the usual definition of local dependence does not imply concentratio…
Non-asymptotic confidence intervals for MCMC in practice
Benjamin M. Gyori, Daniel Paulin
Using concentration inequalities, we give non-asymptotic confidence intervals for estimates obtained by Markov chain Monte Carlo (MCMC) simulations, when using the approximation $\…
Concentration inequalities for Markov chains by Marton couplings and spectral methods
Daniel Paulin
We prove a version of McDiarmid's bounded differences inequality for Markov chains, with constants proportional to the mixing time of the chain. We also show variance bounds and Be…
The convex distance inequality for dependent random variables, with applications to the stochastic travelling salesman and other problems
Daniel Paulin
We prove concentration inequalities for general functions of weakly dependent random variables satisfying the Dobrushin condition. In particular, we show Talagrand's convex distanc…
A Note on Matrix Concentration Inequalities via the Method of Exchangeable Pairs
Daniel Paulin
The aim of this paper is to prove an improved version of the bounded differences inequality for matrix valued functions, by developing the methods of Mackey et al.: "Matrix Concent…
Locally Perturbed Random Walks with Unbounded Jumps
Daniel Paulin, Domokos Szász
In \cite{SzT}, D. Szász and A. Telcs have shown that for the diffusively scaled, simple symmetric random walk, weak convergence to the Brownian motion holds even in the case of loc…