markov chain monte carlo

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract) IJCAI 2024 Towards safe multi-task Bayesian optimization L4DC 2024 Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy ICLR 2024 Walking the Values in Bayesian Inverse Reinforcement Learning UAI 2024 Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion NeurIPS 2024 A Bayesian Take on Gaussian Process Networks NeurIPS 2023 Adaptive Tuning for Metropolis Adjusted Langevin Trajectories AISTATS 2023 A Dynamical System View of Langevin-Based Non-Convex Sampling NeurIPS 2023 A Faster Practical Approximation Scheme for the Permanent AAAI 2023 Algorithmic Aspects of the Log-Laplace Transform and a Non-Euclidean Proximal Sampler COLT 2023 Any-scale Balanced Samplers for Discrete Space ICLR 2023 A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-Run Langevin Flow for Approximate Inference AAAI 2023 A Targeted Accuracy Diagnostic for Variational Approximations AISTATS 2023 Automatic Data Augmentation via Invariance-Constrained Learning ICML 2023 Bayesian Spiked Laplacian Graphs JMLR 2023 Bayesian Variable Selection in a Million Dimensions AISTATS 2023 City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC AAAI 2023 Compositional Score Modeling for Simulation-Based Inference ICML 2023 Concentration analysis of multivariate elliptic diffusions JMLR 2023 Convergence of First-Order Methods for Constrained Nonconvex Optimization with Dependent Data ICML 2023 Denoising MCMC for Accelerating Diffusion-Based Generative Models ICML 2023 Differentially Private Distributed Bayesian Linear Regression with MCMC ICML 2023 Discrete Langevin Samplers via Wasserstein Gradient Flow AISTATS 2023 Discrete Predictor-Corrector Diffusion Models for Image Synthesis ICLR 2023 Discriminative Calibration: Check Bayesian Computation from Simulations and Flexible Classifier NeurIPS 2023