Bayesian & Probabilistic

Posterior Consistency for Bayesian Relevance Vector Machines JMLR 2023 Posterior Contraction for Deep Gaussian Process Priors JMLR 2023 Posterior Sampling for Competitive RL: Function Approximation and Partial Observation NeurIPS 2023 Posterior Sampling for Deep Reinforcement Learning ICML 2023 Post-hoc Uncertainty Learning Using a Dirichlet Meta-Model AAAI 2023 Practical Equivariances via Relational Conditional Neural Processes NeurIPS 2023 Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information ICLR 2023 Prediction-Oriented Bayesian Active Learning AISTATS 2023 Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecasting NeurIPS 2023 Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching JMLR 2023 Probabilistic Attention-to-Influence Neural Models for Event Sequences ICML 2023 Probabilistic circuits that know what they don’t know UAI 2023 Probabilistic Debiasing of Scene Graphs CVPR 2023 Probabilistic Exponential Integrators NeurIPS 2023 Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference UAI 2023 Probabilistic Imputation for Time-series Classification with Missing Data ICML 2023 Probabilistic Invariance for Gaussian Process State Space Models L4DC 2023 Probabilistic Knowledge Distillation of Face Ensembles CVPR 2023 Probabilistic Multi-Dimensional Classification UAI 2023 Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives IJCAI 2023 Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models ICCV 2023 Probabilistic Programs as an Action Description Language AAAI 2023 Probabilistic Reasoning and Learning for Trustworthy AI AAAI 2023 Probabilistic Rule Induction from Event Sequences with Logical Summary Markov Models IJCAI 2023 Probabilistic Shape Models of Anatomy Directly from Images AAAI 2023