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world:projet-mda-2025
This is an old revision of the document!
| Link to the paper | Title | Authors | Associated team (2 students per project) | Comments |
|---|---|---|---|---|
| paper 1 | A probabilistic approach to convex (φ)-entropy decay for Markov chains | Giovanni Conforti | ||
| paper 2 | Shifted Composition III: Local Error Framework for KL Divergence | Jason M. Altschuler & Sinho Chewi | ||
| paper 3 | A probability approximation framework: Markov process approach | Peng Chen, Qi‑Man Shao & Lihu Xu | ||
| paper 4 | Multivariate approximations in Wasserstein distance by Stein’s method and Bismut’s formula | Xiao Fang, Qi‑Man Shao & Lihu Xu | ||
| paper 5 | Berry–Es̈een Bounds for Multivariate Nonlinear Statistics with Applications to M‑estimators and Stochastic Gradient Descent Algorithms | Qi‑Man Shao & Zhuo‐Song Zhang | ||
| paper 6 | Asymptotically unbiased approximation of the QSD of diffusion processes with a decreasing time step Euler scheme | Fabien Panloup & Julien Reygner | ||
| paper 7 | General Markovian randomized equilibrium existence and construction in zero‑sum Dynkin games for diffusions | Sören Christensen & Kristoffer Lindensjö | ||
| paper 8 | On spectral gap decomposition for Markov chains | Qian Qin | ||
| paper 9 | A phase transition in sampling from Restricted Boltzmann Machines | Youngwoo Kwon, Qian Qin, Guanyang Wang & Yuchen Wei | ||
| paper 10 | Spectral gap bounds for reversible hybrid Gibbs chains | Qian Qin, Nianqiao Ju & Guanyang Wang | ||
| paper 11 | Analysis of two‑component Gibbs samplers using the theory of two projections | Qian Qin | ||
| paper 12 | Wasserstein‑based methods for convergence complexity analysis of MCMC with applications | Qian Qin & James P. Hobert | ||
| paper 13 | On importance sampling and independent Metropolis‑Hastings with an unbounded weight function | George Deligiannidis, Pierre E. Jacob, El Mahdi Khribch & Guanyang Wang | ||
| paper 14 | The No‑Underrun Sampler: A Locally‑Adaptive, Gradient‑Free MCMC Method | Nawaf Bou‑Rabee, Bob Carpenter, Sifan Liu & Stefan Oberdörster | ||
| paper 15 | Deep Learning for Computing Convergence Rates of Markov Chains | Y Qu et al. | ||
| paper 16 | Mixing Time Bounds for the Gibbs Sampler under Isoperimetry | Alexander Goyal, George Deligiannidis & Nikolas Kantas | ||
| paper 17 | Metropolis–Hastings transition kernel couplings | John O’Leary & Guanyang Wang |
world/projet-mda-2025.1762336935.txt.gz · Last modified: 2025/11/05 11:02 by rdouc
