Last update: June 21th

Minisymposium title session # Speaker's Name Talk Title      
On the power of iid information for (non-linear) approximation
organised by
Mathias Sonnleitner and Mario Ullrich
session 1 Mario Ullrich On recent advances in approximation based on iid data  
Matthieu Dolbeault Approximation with iid, reduced, or greedy sampling strategies  
Albert Cohen Nonlinear approximation spaces for inverse problems  
Sebastian Moraga Optimal learning of infinite-dimensional holomorphic functions from i.i.d. samples  
session 2 Art Owen Mean Dimension of Radial Basis Functions  
Robert J. Kunsch Uniform Approximation of Finite Sequences with Randomized Algorithms  
Fabian Taubert Dimension-Incremental Function Approximation Using Monte-Carlo Methods  
Numerics for SPDEs
organised by
Charles- Edouard Bréhier
session 1 Guillaume Dujardin Numerical methods for the nonlinear stochastic Manakov system  
Ludovic Goudenège Tamed Euler scheme for SPDE with distributional drift  
Gabriel Lord Adaptive/Tamed methods for SPDEs with additive noise  
Kerstin Schmitz Convergence of a finite-volume scheme for a stochastic heat equation with a multiplicative Lipschitz noise  
PDMP and related topics
organised by
Daniel Paulin
session 1 George Deligiannidis Non-Reversible Parallel Tempering: a Scalable Highly Parallel MCMC Scheme  
Kengo Kamatani Scaling of Piecewise Deterministic Monte Carlo for Anisotropic Targets  
Augustin Chevallier Adaptive Metropolized PDMP sampling using the No-U-Turn criterion  
Sebastiano Grazzi PDMP samplers with boundary conditions  
Recent advances in MCM for forward and inverse problems for stochastic reaction networks
organised by
Chiheb Ben Hammouda, Nadhir Ben Rached and Raul Tempone
session 1 Sophia Wiechert Markovian Projection for Efficient Importance Sampling of Stochastic Reaction Networks  
Fang Zhou A scalable approach for solving chemical master equations based on modularization and filtering  
Ankit Gupta Frequency domain methods for analysing stochastic reaction networks  
David Warne Multifidelity multilevel approximate Bayesian computation for stochastic biochemical reaction networks  
Slice sampling and adaptive MCMC
organised by
Daniel Rudolf
session 1 Mareike Hasenpflug Slice Sampling on the Sphere  
Philip Schär Making Polar Slice Sampling Efficiently Implementable  
Julian Hofstadler Adaptive MCMC for doubly intractable distributions  
Andi Wang Comparison theorems for Hybrid Slice Sampling  
Numerical methods in statistical physics
organised by
Alessandra Iacobucci and Gabriel Stoltz
session 1 Pierre Monmarché Quantitative convergence bounds for kinetic Langevin and HMC  
Régis Santet Unbiasing HMC Algorithms For General Hamiltonian Functions  
Pierre Illien Brownian dynamics simulations of colloids propelled by mesoscale phase separations  
Pierfrancesco Urbani Dynamical mean-field theory for stochastic gradient descent in high dimensions  
session 2 Gilles Vilmart Accelerated convergence to equilibrium and reduced asymptotic variance for Langevin dynamics using Stratonovich perturbations  
Petr Plechac Estimating linear response and sensitivity analysis of non-equilibrium steady states  
Dominic Phillips Coordinate Transforms for Efficient Brownian Dynamics Simulations  
Shiva Darshan Sticky Coupling as a Control Variate for Sensitivity Analysis  
session 3 Juliane U. Klamser Can Monte Carlo methods be used to simulate active-matter systems?  
Noé Blassel Stochastic Norton Dynamics  
Thomas Pigeon Adaptive multilevel splitting used to machine learn committor function  
Gideon Simpson Infinite Dimensional Nonlocal Diffusions with Additive Noise  
session 4 Michela Ottobre Uniform in time approximations of stochastic dynamics  
Matthew Dobson Steady-State Solutions for Nonequilibrium Langevin Dynamics  
Renato Spacek Extending the regime of linear response with synthetic forcings  
Grigorios Pavliotis  On the Diffusive-Mean Field Limit for Weakly Interacting Diffusions Exhibiting Phase Transitions  
Variance reduction techniques for rare events
organised by
Nadhir Ben Rached and Raul Tempone 
session 1 Nadhir Ben Rached Importance sampling via stochastic optimal control for McKean-Vlasov stochastic differential equation  
Shyam Mohan Multilevel and Multi-index Monte Carlo methods for rare events associated with McKean-Vlasov equation  
Bruno Tuffin Bounds, Assessment and Confidence Intervals for Exponential Approximations  
Charly Andral The Importance Markov Chain  
session 2 Eya Ben Amar State-dependent Importance Sampling for Estimating Expectations of Functionals of Sums of Independent Random Variables  
Gerardo Rubino Estimating network resilience, a performability metric  
Martin Chak Optimal friction in Langevin dynamics  
Stochastic Computation and Complexity
organised by
Stefan Heinrich, Thomas Müller-Gronbach and Larisa Yaroslavtseva
session 1 Andreas Neuenkirch The robustness of the Euler scheme for scalar SDEs with non-Lipschitz diffusion coefficients  
Konstantinos Dareiotis Asymptotic error distribution of the Euler method for stochastic differential equations with irregular drifts  
Verena Schwarz Randomized Milstein algorithm for approximation of solutions of jump-diffusion SDEs  
Andre Herzwurm On upper and lower bounds for strong approximation of scalar SDEs with reflecting boundary  
session 2 Randolf Altmeyer Approximation of occupation time functionals and related approximations of Ito processes  
Simon Ellinger Sharp lower error bounds for strong approximation of SDEs with piecewise Lipschitz continuous drift      
Mate Gerenczer Milstein scheme for SDEs with irregular drift  
Larisa Yaroslavtseva Sharp lower error bounds for strong approximation of SDEs with a drift coefficient of Sobolev regularity s in (1/2,1)  
session 3 Daniel Rudolf Convergence and well-definedness of ellitpical slice sampling  
Tomasz Bochacik On error bounds, optimality and exceptional sets for selected randomized schemes for ODEs  
Sonja Cox Infinite-dimensional Wishart processes  
Pierre Bras  Convergence of Langevin-Simulated Annealing Algorithms with Multiplicative Noise  
session 4 Michaela Szölgyenyi A higher order approximation method for jump-diffusion SDEs with discontinuous drift coefficient  
Tim Johnston Nonasymptotic Bounds for EM via Interacting Particle Systems  
Gunther Leobacher McKean–Vlasov equations with discontinuous drift  
Oleg Butkovsky Strong rate of convergence of the Euler scheme for SDEs with
irregular drift driven by Levy noise
session 5 Lukasz Stepien Adaptive step-size control for global approximation of SDEs driven by countably dimensional Wiener process   
Monika Eisenmann Domain decomposition methods for SPDEs  
Sotirios Sabanis Adaptive stochastic optimizers, Euler-Krylov’s polygonal approximations and the training of neural nets  
Stefan Heinrich Randomized Complexity of Vector-Valued Approximation  
SDEs: Theory, Numerics and Applications
organised by
Gabriel Lord
session 1 Mireille Bossy Stochastic approach for the simulation of non-spherical particles in turbulence  
Evelyn Buckwar Construction and analysis of splitting methods for Chemical Langevin Equations  
Khadija Meddouni Numerical methods for stochastic neural field equations  
Conall Kelly Adaptive Meshes for Stochastic Jump Differential Equations  
MCM for reactor physics
organised by
Andrea Zoia and Eric Dumonteil
session 1 Mathias Rousset Fluctuations of Rare Event Simulation with Monte Carlo Splitting in the Small Noise Asymptotics  
Davide Mancusi Variance Reduction and Noise Source Sampling Techniques for Monte Carlo Simulations of Neutron Noise  
Vince Maes Estimating the statistical error of analog particle-tracing Monte Carlo methods  
Benjamin Dechenaux Percolation properties of the neutron population in nuclear reactors  
session 2 Emma Horton A binary branching model with Moran-type interactions  
Elod Pazman Comparison of Dynamic and Static Flux Estimators and Their Impact on Group Constant Production in the Time-Dependent Monte Carlo Reactor Code GUARDYAN  
Alex Cox Twisted particle filters for neutron transport  
Andrea Zoia Analysis of heterogeneous Markov media for particle transport problems  
PDMPs for high dimensional sampling: theory and application
organised by
Ritabrata Dutta and Benedict Leimkuhler 
session 1 Guillaume Chennetier Adaptive importance sampling based on fault tree analysis for piecewise deterministic Markov process  
Joris Bierkens A detailed investigation of subsampling regimes for PDMPs  
Matthias Sachs Posterior Computation with the Gibbs Zig-Zag Sampler  
Ritabrata Dutta Exact sampling of scoring rule posterior using Gibbs Boomerang  
session 2 Alexandre Bouchard-Cote PDMPs as Monte Carlo algorithms models  
Nawaf Bou-Rabee Randomized Time Integrator for Unadjusted Hamiltonian MCMC  
Peter A. Whalley Contraction and Convergence Rates for Discretized Kinetic Langevin Dynamics    
Torben Sell Gradient-Based Markov Chain Monte Carlo for Bayesian Inference With Non-Differentiable Priors  
Monte Carlo Methods for Bayesian inference and optimization
organised by
Lotfi Chaari
session 1 Savvas Melidonis Efficient Bayesian computation for low-photon imaging problems  
Emilie Chouzenoux PMCnet for Efficient Bayes Inference in Neural Networks  
Mohamed Fakhfakh Hamiltonian Monte Carlo Bayesian Optimization for Sparse Neural Networks  
Nadege Polette Bayesian Inference for Inverse Problems with Hyperparameters Estimation of the Field Covariance Function  
High dimensional integration and approximation
organised by
Frances Kuo
session 1 James Nichols Community detection with entropic regularization  
Alexander Gilbert Density estimation in uncertainty quantification using quasi-Monte Carlo methods with preintegration  
Michael Feischl A quasi-Monte Carlo data compression algorithm for machine learning  
Vesa Kaarnioja On the Periodic Model of Uncertainty Quantification With Application to Bayesian Inverse Problems  
session 2 Abirami Srikumar Multi-level quasi-Monte Carlo methods for kernel interpolation in uncertainty quantification  
Dirk Nuyens Higher order lattice rules on Rd  
Yoshihito Kazashi Density estimation in RKHS with application to Korobov spaces in high dimensions  
Ian Sloan Periodic kernel-based high-dimensional approximation  
Recent Progress in Langevin MC
organised by
Jun Yang
session 1 Alain Durmus On the convergence of the Unadjusted and Metropolis Adjusted Langevin Algorithms  
Tyler Farghly Adaptive Langevin Monte Carlo methods for heavy-tailed sampling via weighted functional inequalities  
Sifan Liu Langevin Quasi-Monte Carlo  
Konstantinos Zygalakis Accelerating MCMC for imaging science by using an implicit Langevin algorithm  
MLMC techniques for discontinuous functionals
organised by
Chiheb Ben Hammouda and Raul Tempone
session 1 Chiheb Ben Hammouda MLMC Combined with Numerical Smoothing for Efficient Probabilities Computation, Density Estimation, and Option Pricing  
Abdul-Lateef Haji-Ali Multilevel Path Branching for Digital Options  
Ahmed Kebaier The interpolated drift implicit Euler scheme Multilevel Monte Carlo method for pricing Barrier options and applications to the CIR and CEV models  
Andreas Stein  An antithetic multilevel Monte Carlo Milstein scheme for SPDEs  
session 2 Sebastian Krumscheid Multilevel Monte Carlo methods for parametric expectations: distribution and robustness measures  
Fabio Nobile MLMC for the computation of CVaR and its sensitivities in PDE-constrained risk-averse optimization  
Cedric Beschle CLMC techniques for elliptic PDEs with random discontinuities  
Daniel Roth  Multilevel Monte Carlo Learning  
Nonreversible processes: theory and applications
organised by
Daniel Adams, Goncalo Dos Reis and Hong Duong
session 1 Giovanni Conforti A probabilistic view of Sinkhorn’s algorithm  
Arnaud Guillin Pair of run and tumble processes with interactions: invariant measure, jamming and mixing time  
Carsten Hartmann Constraints, strong confinement limits and conditional expectations in nonreversible Langevin dynamics  
Daniel T. Adams The Structure of GENERIC, Hypocoercivity, and Variational Schemes  
Sampling Strategies for Bayesian Inference
organised by
Elena Akhmatskaya and Jesus Maria Sanz-Serna
session 1 Konstantinos Zygalakis Bayesian Inference with Data-Driven Image Priors Encoded by Neural Networks  
Luke Shaw Rotation-based Integrators for HMC  
Nicola Branchini Generalized Self-Normalized Importance Sampling  
 L. Nagar & M. Parga Pazos Adaptive Integration Approach for Sampling with Hamiltonian Monte Carlo Based Methods  
Convergence results for kinetic samplers
organised by
Pierre Monmarché
session 1 Lionel Riou-Durand Metropolis Adjusted Langevin Trajectories: a robust alternative to Hamiltonian Monte Carlo  
Katharina Schuh Convergence of unadjusted Hamiltonian Monte Carlo and Langevin dynamics via couplings  
Lucas Journel Sampling of singular Gibbs measure  
Lihan Wang Convergence Rates of Kinetic Sampling Dynamics via Space-time Poincaré-type Inequality  
Exploring the intersections of importance sampling, MCMC, and optimization
organised by
Dootika Vats
session 1 Juan Kuntz Particle-Based Algorithms for Maximum Likelihood Estimation of Latent Variable Models  
Jimmy Olsson PaRISian particle Gibbs samplers for state and pa- rameter learning in nonlinear state-space models  
Víctor Elvira MCMC-driven adaptive importance samplers  
Dootika Vats Comparing apples to oranges: a universal effective sample size  
(Quasi-)MC Software
organised by
Fred Hickernell and Pieterjan Robbe
session 1 Aleksei Sorokin Collaborative Integrations with the QMCPy Framework  
Anne Reinarz UM-Bridge  
Mikkel Lykkegaard TinyDA: Multilevel Delayed Acceptance MCMC for Human Beings  
Pieterjan Robbe Multilevel Delayed Acceptance MCMC for the prediction of xenon diffusion in UO2 nuclear fuel  
Sampling Schemes: Quality Measures, Point Generation, and Applications
organised by
Michael Gnewuch and Florian Pausinger
session 1 François Clement Subset Sampling for Low Discrepancy Point Sets  
Jasmin Fiedler Maximal inequalities in Discrepancy theory  
Nathan Kirk Stratified Sampling of the Unit Cube  
Zexin Pan Super-polynomial Accuracy of Median-of-means  
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