Bayesian Inference

Neurosymbolic Reasoning and Learning with Restricted Boltzmann Machines AAAI 2023 Noise-Aware Statistical Inference with Differentially Private Synthetic Data AISTATS 2023 Non-adversarial training of Neural SDEs with signature kernel scores NeurIPS 2023 Nonparametric Gaussian Process Covariances via Multidimensional Convolutions AISTATS 2023 Nonparametric Identifiability of Causal Representations from Unknown Interventions NeurIPS 2023 Non-reversible Parallel Tempering for Deep Posterior Approximation AAAI 2023 Nonstationary Bandit Learning via Predictive Sampling AISTATS 2023 No-regret Sample-efficient Bayesian Optimization for Finding Nash Equilibria with Unknown Utilities AISTATS 2023 NP-SemiSeg: When Neural Processes meet Semi-Supervised Semantic Segmentation ICML 2023 Offline Meta Reinforcement Learning with In-Distribution Online Adaptation ICML 2023 Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling ICML 2023 On Excess Mass Behavior in Gaussian Mixture Models with Orlicz-Wasserstein Distances ICML 2023 On inference and learning with probabilistic generating circuits UAI 2023 Online Restless Bandits with Unobserved States ICML 2023 On Masked Pre-training and the Marginal Likelihood NeurIPS 2023 On permutation symmetries in Bayesian neural network posteriors: a variational perspective NeurIPS 2023 On Sampling with Approximate Transport Maps ICML 2023 On Second-Order Scoring Rules for Epistemic Uncertainty Quantification ICML 2023 On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models AAAI 2023 On the Complexity of Bayesian Generalization ICML 2023 On the Convergence of Black-Box Variational Inference NeurIPS 2023 On the Estimation of Gaussian Mixture Copula Models ICML 2023 On the geometry of Stein variational gradient descent JMLR 2023 On the Robustness of Mechanism Design under Total Variation Distance NeurIPS 2023 On the role of model uncertainties in Bayesian optimisation UAI 2023