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Papers
Scalable Bayesian Learning with posteriors
ICLR 2025
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
UAI 2025
Sketching in High-Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors
JMLR 2025
SoftCVI: Contrastive variational inference with self-generated soft labels
ICLR 2025
Tamed Langevin sampling under weaker conditions
AISTATS 2025
Transferable Direct Prompt Injection via Activation-Guided MCMC Sampling
EMNLP 2025
Tuning-Free Coreset Markov Chain Monte Carlo via Hot DoG
UAI 2025
Unified Discrete Diffusion for Categorical Data
JMLR 2025
Unleashing High-Quality Image Generation in Diffusion Sampling Using Second-Order Levenberg-Marquardt-Langevin
ICCV 2025
3D Gaussian Splatting as Markov Chain Monte Carlo
NeurIPS 2024
Accelerated Sampling with Stacked Restricted Boltzmann Machines
ICLR 2024
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
CVPR 2024
A Generalized Bayesian Approach to Distribution-on-Distribution Regression
UAI 2024
A Greedy Approximation for k-Determinantal Point Processes
AISTATS 2024
Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC
JMLR 2024
Approximating Intersections and Differences Between Linear Statistical Shape Models Using Markov Chain Monte Carlo
WACV 2024
autoMALA: Locally adaptive Metropolis-adjusted Langevin algorithm
AISTATS 2024
Bayesian Bi-clustering of Neural Spiking Activity with Latent Structures
ICLR 2024
Bayesian Pseudo-Coresets via Contrastive Divergence
UAI 2024
Causal Structure Learning With Momentum: Sampling Distributions Over Markov Equivalence Classes
PGM 2024
Chain of Log-Concave Markov Chains
ICLR 2024
Constrained Sampling with Primal-Dual Langevin Monte Carlo
NeurIPS 2024
Contraction of Markovian Operators in Orlicz Spaces and Error Bounds for Markov Chain Monte Carlo (Extended Abstract)
COLT 2024
Convergence of Kinetic Langevin Monte Carlo on Lie groups
COLT 2024
Coreset Markov chain Monte Carlo
AISTATS 2024