bayesian inference

Structural Inference of Dynamical Systems with Conjoined State Space Models NeurIPS 2024 Subsampling Error in Stochastic Gradient Langevin Diffusions AISTATS 2024 Tensor-train methods for sequential state and parameter learning in state-space models JMLR 2024 The Bayesian sampling in a canonical recurrent circuit with a diversity of inhibitory interneurons NeurIPS 2024 The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection NeurIPS 2024 Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators AISTATS 2024 Thompson Sampling For Combinatorial Bandits: Polynomial Regret and Mismatched Sampling Paradox NeurIPS 2024 Toward In-Context Teaching: Adapting Examples to Students’ Misconceptions ACL 2024 Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract) IJCAI 2024 Towards Offline Opponent Modeling with In-context Learning ICLR 2024 Transfer Learning with Uncertainty Quantification: Random Effect Calibration of Source to Target (RECaST) JMLR 2024 Uncertainty-based Offline Variational Bayesian Reinforcement Learning for Robustness under Diverse Data Corruptions NeurIPS 2024 Uncertainty informed optimal resource allocation with Gaussian process based Bayesian inference L4DC 2024 Uncertainty Matters: Stable Conclusions under Unstable Assessment of Fairness Results AISTATS 2024 Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model AAAI 2024 Unrolled denoising networks provably learn to perform optimal Bayesian inference NeurIPS 2024 Unsupervised Anomaly Detection via Masked Diffusion Posterior Sampling IJCAI 2024 Using Autodiff to Estimate Posterior Moments, Marginals and Samples UAI 2024 Variational Multi-scale Representation for Estimating Uncertainty in 3D Gaussian Splatting NeurIPS 2024 VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition ICLR 2024 Virtual-Event-Based Posterior Sampling and Inference for Neyman-Scott Processes JMLR 2024 A Bayesian Approach To Analysing Training Data Attribution In Deep Learning NeurIPS 2023 A Bayesian Approach to Robust Inverse Reinforcement Learning CoRL 2023 A Bayesian Take on Gaussian Process Networks NeurIPS 2023 A Complete Recipe for Diffusion Generative Models ICCV 2023