Pages that link to "Item:Q5743237"
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The following pages link to Theoretical Guarantees for Approximate Sampling from Smooth and Log-Concave Densities (Q5743237):
Displaying 50 items.
- Theoretical properties of quasi-stationary Monte Carlo methods (Q670746) (← links)
- Log-Sobolev inequalities and sampling from log-concave distributions (Q1296584) (← links)
- Sampling from log-concave distributions (Q1336592) (← links)
- Explicit contraction rates for a class of degenerate and infinite-dimensional diffusions (Q1685680) (← links)
- Ensemble preconditioning for Markov chain Monte Carlo simulation (Q1702007) (← links)
- Normalizing constants of log-concave densities (Q1746544) (← links)
- On the exponentially weighted aggregate with the Laplace prior (Q1800807) (← links)
- Fourier transform MCMC, heavy-tailed distributions, and geometric ergodicity (Q1998313) (← links)
- Is there an analog of Nesterov acceleration for gradient-based MCMC? (Q2040101) (← links)
- Approximation of heavy-tailed distributions via stable-driven SDEs (Q2040106) (← links)
- A duality formula and a particle Gibbs sampler for continuous time Feynman-Kac measures on path spaces (Q2042659) (← links)
- Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimation (Q2058738) (← links)
- Sampling from non-smooth distributions through Langevin diffusion (Q2065460) (← links)
- Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates (Q2075323) (← links)
- Unadjusted Langevin algorithm for sampling a mixture of weakly smooth potentials (Q2083423) (← links)
- Nonparametric Bayesian inference for reversible multidimensional diffusions (Q2105199) (← links)
- Variance reduction for additive functionals of Markov chains via martingale representations (Q2114045) (← links)
- Central limit theorem and self-normalized Cramér-type moderate deviation for Euler-Maruyama scheme (Q2137002) (← links)
- Oracle lower bounds for stochastic gradient sampling algorithms (Q2137007) (← links)
- Improved bounds for discretization of Langevin diffusions: near-optimal rates without convexity (Q2137032) (← links)
- Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference (Q2137043) (← links)
- Stochastic gradient Hamiltonian Monte Carlo for non-convex learning (Q2137760) (← links)
- Constrained ensemble Langevin Monte Carlo (Q2148951) (← links)
- Complexity of zigzag sampling algorithm for strongly log-concave distributions (Q2152554) (← links)
- Ergodicity of the infinite swapping algorithm at low temperature (Q2157335) (← links)
- On sampling from a log-concave density using kinetic Langevin diffusions (Q2174987) (← links)
- Exponential weights in multivariate regression and a low-rankness favoring prior (Q2179638) (← links)
- Variance reduction for Markov chains with application to MCMC (Q2195839) (← links)
- Bridging the gap between constant step size stochastic gradient descent and Markov chains (Q2196224) (← links)
- Optimal scaling of random-walk Metropolis algorithms on general target distributions (Q2196541) (← links)
- On stochastic gradient Langevin dynamics with dependent data streams in the logconcave case (Q2214233) (← links)
- On the limitations of single-step drift and minorization in Markov chain convergence analysis (Q2240862) (← links)
- Mixing of Hamiltonian Monte Carlo on strongly log-concave distributions: continuous dynamics (Q2240875) (← links)
- The tamed unadjusted Langevin algorithm (Q2274251) (← links)
- User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient (Q2280028) (← links)
- Non-asymptotic guarantees for sampling by stochastic gradient descent (Q2290072) (← links)
- Multivariate approximations in Wasserstein distance by Stein's method and Bismut's formula (Q2312685) (← links)
- High-dimensional Bayesian inference via the unadjusted Langevin algorithm (Q2325343) (← links)
- Higher order Langevin Monte Carlo algorithm (Q2326072) (← links)
- PAC-Bayesian risk bounds for group-analysis sparse regression by exponential weighting (Q2418515) (← links)
- Quantitative contraction rates for Markov chains on general state spaces (Q2631852) (← links)
- Nonasymptotic bounds for sampling algorithms without log-concavity (Q2657917) (← links)
- Functional inequalities for perturbed measures with applications to log-concave measures and to some Bayesian problems (Q2676921) (← links)
- Convergence rates of Gibbs measures with degenerate minimum (Q2676926) (← links)
- Optimising portfolio diversification and dimensionality (Q2679246) (← links)
- Nonasymptotic estimates for stochastic gradient Langevin dynamics under local conditions in nonconvex optimization (Q2682367) (← links)
- Unifying presampling via concentration bounds (Q2695631) (← links)
- Scaling Limit of the Stein Variational Gradient Descent: The Mean Field Regime (Q4627155) (← links)
- Quantitative Harris-type theorems for diffusions and McKean–Vlasov processes (Q4633774) (← links)
- Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau (Q4686924) (← links)
