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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 |
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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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