Bayesian Inference

Random Exploration in Bayesian Optimization: Order-Optimal Regret and Computational Efficiency ICML 2024 Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling CoRL 2024 RECOMBINER: Robust and Enhanced Compression with Bayesian Implicit Neural Representations ICLR 2024 Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss NeurIPS 2024 R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning ICLR 2024 Reference-free Hallucination Detection for Large Vision-Language Models EMNLP 2024 Refining Minimax Regret for Unsupervised Environment Design ICML 2024 Regression Aware Inference with LLMs EMNLP 2024 ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages ICML 2024 REMEDI: Corrective Transformations for Improved Neural Entropy Estimation ICML 2024 Reparameterization invariance in approximate Bayesian inference NeurIPS 2024 Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks ICML 2024 Reparameterized Variational Rejection Sampling AISTATS 2024 Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling ICML 2024 Reverse Diffusion Monte Carlo ICLR 2024 Revisiting Differentially Private ReLU Regression NeurIPS 2024 Riemannian Laplace Approximation with the Fisher Metric AISTATS 2024 RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content ICML 2024 Risk Measures and Upper Probabilities: Coherence and Stratification JMLR 2024 Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum AISTATS 2024 Risk-sensitive control as inference with Rényi divergence NeurIPS 2024 Robust Approximate Sampling via Stochastic Gradient Barker Dynamics AISTATS 2024 Robust Gaussian Processes via Relevance Pursuit NeurIPS 2024 Robustifying and Boosting Training-Free Neural Architecture Search ICLR 2024 Robust Inverse Graphics via Probabilistic Inference ICML 2024