MiniSymposia

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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