Bayesian Inference

BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference ICLR 2024 Bayesian Adaptation of Network Depth and Width for Continual Learning ICML 2024 Bayesian Adaptive Calibration and Optimal Design NeurIPS 2024 Bayesian Calibration of Win Rate Estimation with LLM Evaluators EMNLP 2024 Bayesian Design Principles for Offline-to-Online Reinforcement Learning ICML 2024 Bayesian Differentiable Physics for Cloth Digitalization CVPR 2024 Bayesian Diffusion Models for 3D Shape Reconstruction CVPR 2024 Bayesian Domain Adaptation with Gaussian Mixture Domain-Indexing NeurIPS 2024 Bayesian Exploration Networks ICML 2024 Bayesian Knowledge Distillation: A Bayesian Perspective of Distillation with Uncertainty Quantification ICML 2024 Bayesian Low-rank Adaptation for Large Language Models ICLR 2024 Bayesian Neural Controlled Differential Equations for Treatment Effect Estimation ICLR 2024 Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization NeurIPS 2024 Bayesian Online Learning for Consensus Prediction AISTATS 2024 Bayesian Online Natural Gradient (BONG) NeurIPS 2024 Bayesian Optimisation with Unknown Hyperparameters: Regret Bounds Logarithmically Closer to Optimal NeurIPS 2024 Bayesian Optimization of Function Networks with Partial Evaluations ICML 2024 Bayesian Optimization of Functions over Node Subsets in Graphs NeurIPS 2024 Bayesian Program Learning by Decompiling Amortized Knowledge ICML 2024 Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models ACL 2024 Bayesian Regression Markets JMLR 2024 Bayesian Regret Minimization in Offline Bandits ICML 2024 Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning ICML 2024 Bayes-optimal learning of an extensive-width neural network from quadratically many samples NeurIPS 2024 Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields CVPR 2024