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Related Articles from SNS
Folded Transport MCMC: Certifiable Quotient Posterior Computation for Symmetric Bayesian Models
arXiv:2606.04307v1 Announce Type: new Abstract: Bayesian models with finite symmetry - mixture models with exchangeable components, structural identification with closely-spaced modes - define posteriors that are invariant under a group of label permutations, creating redundant multimodality that degrades MCMC convergence diagnostics. We introduce Folded Transport MCMC (FolT-MCMC), which performs inference directly on the quotient posterior by constructing an independence sampler on the...
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
arXiv:2605.30722v2 Announce Type: replace Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian reference to an approximation of the target posterior; the same flow then serves as both the independence Metropolis-Hastings proposal and the basis for a computable spectral-gap bound. We develop two complementary certificates.
Self-Certifying Transport MCMC via Dual Spectral-Gap Certificates
arXiv:2605.30722v1 Announce Type: new Abstract: We propose CerT-MCMC, a framework that equips learned-transport Markov chain Monte Carlo with automatic, rigorous convergence certificates. A normalising flow maps a Gaussian reference to an approximation of the target posterior; the same flow then serves as both the independence Metropolis-Hastings proposal and the basis for a computable spectral-gap bound. We develop two complementary certificates.
Non-Vacuous Certification of Transport MCMC via Oscillation-Controlled Normalizing Flows
arXiv:2606.01078v1 Announce Type: new Abstract: Transport MCMC trains a normalizing flow to precondition Metropolis--Hastings proposals, achieving high empirical efficiency on challenging posteriors; yet no prior work produces a numerically non-vacuous, rigorous spectral-gap bound for such samplers. We establish the first such bounds. For independence MH on the banana family we certify (\gamma^\ast = 0.828) at (D = 2) (covering in the original space) and (\gamma^\ast \ge 7.6\times 10^{-4})...
Local and Global Contraction Principles for MCMC Mixing
arXiv:2606.03033v1 Announce Type: new Abstract: We develop a contraction-based framework for proving mixing-time bounds for Markov chain Monte Carlo algorithms. The framework is built around global and local contraction coefficients of Markov kernels under the $\mathsf E_\gamma$-divergence with $\gamma\ge1$. For projected Langevin Monte Carlo on a compact convex domain, we show that Gaussian smoothing yields an explicit global contraction coefficient for the $\mathsf E_\gamma$-divergence....
Malaysia's social media account ban for under-16s kicks in on Jun 1, but questions remain
Malaysia's social media account ban for under-16s kicks in on Jun 1, but questions remain The Malaysian Communications and Multimedia Commission (MCMC) had previously announced the social media ban on May 22, which sought to reduce youths’ exposure to harmful content on digital platforms. KUALA LUMPUR: Malaysia on Monday (Jun 1) began enforcing rules barring millions of children younger than 16 years old from having social media accounts, joining a growing global effort to tighten safety...
Malaysia bars under-16s from social media as new rules come into force
Malaysia bars under-16s from social media as new rules come into force Malaysia’s roll-out of strict age-gating rules follows on the heels of Australia and Indonesia in the push to protect children online The measures come into force under the Online Safety Act 2025 and are being implemented by the Malaysian Communications and Multimedia Commission (MCMC) through two complementary frameworks: the Child Protection Code, which obliges platform providers to adopt a safety-by-design approach,...
True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration
Announce Type: cross Abstract: We study true self-avoiding walk (TSAW) as a mechanism for improving empirical integral estimation via Markov chain Monte Carlo (MCMC). We consider finite-state adaptive sampling dynamics associated with an irreducible Markov kernel $P$ on a finite set, with stationary distribution $\pi$, in which the transition probabilities are penalized according to empirical overuse. Our main result is that the empirical occupation counts $L_t(i)$ and transition counts...
Malaysia’s LGBTQ groups tread more carefully amid recent enforcement, ministers’ rhetoric
Malaysia’s LGBTQ groups tread more carefully amid recent enforcement, ministers’ rhetoric Recent moves by the Malaysian authorities point to a more restrictive climate for LGBTQ people in the country, analysts say, and this could be due to political competition as well as the visibility of some events. SINGAPORE: The blocking of two same-sex dating websites; a deputy minister urging Malaysians to use the term “deviant culture” to refer to the LGBTQ (lesbian, gay, bisexual, transgender or...
Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
arXiv:2603.14798v2 Announce Type: replace-cross Abstract: We propose a machine-learning algorithm for Bayesian inverse problems in the function-space regime. Based on one-step generative transport, the method learns an amortized neural operator whose pushforward of a Gaussian source approximates the posterior distribution conditioned on each new observation. We show that white-noise sources are incompatible with the function-space limit, and therefore adopt a prior-aligned GRF as the source.