uncertainty estimation
Uncertainty estimation involves assessing the confidence of predictions made by AI models, often necessary for applications like healthcare or finance, where knowing the uncertainty can guide decision-making.
- Conformal Information Pursuit for Interactively Guiding Large Language Models
- Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
- Distance-informed Neural Processes
- Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection
- Epistemic Uncertainty for Generated Image Detection
- PlanU: Large Language Model Reasoning through Planning under Uncertainty
- Quantifying Uncertainty in the Presence of Distribution Shifts
- SEGA: Shaping Semantic Geometry for Robust Hashing under Noisy Supervision
- Towards Reliable LLM-based Robots Planning via Combined Uncertainty Estimation
- Training-Free Bayesianization for Low-Rank Adapters of Large Language Models
- UFO-RL: Uncertainty-Focused Optimization for Efficient Reinforcement Learning Data Selection
- Uncertainty Estimation on Graphs with Structure Informed Stochastic Partial Differential Equations
- Variational Uncertainty Decomposition for In-Context Learning
- Vicinal Label Supervision for Reliable Aleatoric and Epistemic Uncertainty Estimation
- Words That Unite The World: A Unified Framework for Deciphering Central Bank Communications