score function
In statistics and machine learning, a score function evaluates the performance of a model or hypothesis by providing a quantitative measure, often used in optimization problems to assess how well a particular set of parameters fits the data.
- Assessing the quality of denoising diffusion models in Wasserstein distance: noisy score and optimal bounds
- Conformal Prediction for Ensembles: Improving Efficiency via Score-Based Aggregation
- Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptive
- DualCnst: Enhancing Zero-Shot Out-of-Distribution Detection via Text-Image Consistency in Vision-Language Models
- Improving the Euclidean Diffusion Generation of Manifold Data by Mitigating Score Function Singularity
- RankSEG-RMA: An Efficient Segmentation Algorithm via Reciprocal Moment Approximation
- SD-KDE: Score-Debiased Kernel Density Estimation
- STITCH-OPE: Trajectory Stitching with Guided Diffusion for Off-Policy Evaluation
- Scalable and adaptive prediction bands with kernel sum-of-squares
- When and how can inexact generative models still sample from the data manifold?