bayesian inference
A statistical method that utilizes Bayes' theorem to update the probability estimate for a hypothesis as more evidence becomes available. In AI, it allows models to incorporate prior knowledge and quantify uncertainty in predictions.
- ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
- Adaptive Defense against Harmful Fine-Tuning for Large Language Models via Bayesian Data Scheduler
- An Adaptive Quantum Circuit of Dempster's Rule of Combination for Uncertain Pattern Classification
- Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models
- Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
- FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators
- Modeling Neural Activity with Conditionally Linear Dynamical Systems
- Multilevel neural simulation-based inference
- NoisyGRPO: Incentivizing Multimodal CoT Reasoning via Noise Injection and Bayesian Estimation
- Parallelizing MCMC Across the Sequence Length
- Personalized Bayesian Federated Learning with Wasserstein Barycenter Aggregation
- Predictive Coding Enhances Meta-RL To Achieve Interpretable Bayes-Optimal Belief Representation Under Partial Observability
- Reverse Diffusion Sequential Monte Carlo Samplers
- Squared families are useful conjugate priors
- Statistical Parity with Exponential Weights
- Uncertainty-Guided Exploration for Efficient AlphaZero Training
- Variational Inference with Mixtures of Isotropic Gaussians
- Variational Task Vector Composition
- Variational Uncertainty Decomposition for In-Context Learning