Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

176
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
176
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.6K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.6K
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

207
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
207
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

590
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
590
Relative Risk01:12

Relative Risk

348
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
348

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

IEEE transactions on pattern analysis and machine intelligence·2023
Same author

Utilizing risk-controlling prediction calibration to reduce false alarm rates in epileptic seizure prediction.

Frontiers in neuroscience·2023
Same author

Unified Single-Image and Video Super-Resolution via Denoising Algorithms.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2019
Same author

Example-Based Image Synthesis via Randomized Patch-Matching.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2017
Same author

Con-Patch: When a Patch Meets Its Context.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2016
Same author

Single image interpolation via adaptive nonlocal sparsity-based modeling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2014
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Semi-Supervised Risk Control via Prediction-Powered Inference.

Bat-Sheva Einbinder, Liran Ringel, Yaniv Romano

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 31, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a semi-supervised calibration method to improve risk-controlling prediction sets (RCPS) by using unlabeled data. This approach overcomes sample-size limitations for more accurate hyper-parameter tuning in machine learning.

    More Related Videos

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
    07:31

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

    Published on: May 15, 2020

    7.2K

    Related Experiment Videos

    Last Updated: Sep 13, 2025

    An R-Based Landscape Validation of a Competing Risk Model
    05:37

    An R-Based Landscape Validation of a Competing Risk Model

    Published on: September 16, 2022

    2.2K
    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
    07:31

    Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

    Published on: May 15, 2020

    7.2K

    Area of Science:

    • Machine Learning
    • Statistical Inference
    • Data Science

    Background:

    • The risk-controlling prediction sets (RCPS) framework offers rigorous error rate control for machine learning models.
    • Current RCPS calibration relies on limited labeled hold-out data, leading to noisy hyper-parameters and conservative predictions.

    Purpose of the Study:

    • To introduce a novel semi-supervised calibration procedure for RCPS.
    • To leverage unlabeled data to overcome sample-size limitations in hyper-parameter tuning.
    • To enhance the statistical validity and reduce conservatism of prediction rules.

    Main Methods:

    • Developed a semi-supervised calibration procedure building on prediction-powered inference.
    • Tailored the procedure for risk-controlling tasks.
    • Applied the method to few-shot image classification and early time series classification datasets.

    Main Results:

    • Demonstrated the benefits of the semi-supervised approach in real-world experiments.
    • Showcased rigorous hyper-parameter tuning without compromising statistical validity.
    • Validated the procedure's effectiveness in challenging classification tasks.

    Conclusions:

    • The proposed semi-supervised calibration effectively addresses sample-size limitations in RCPS.
    • This method provides a statistically sound way to tune hyper-parameters using unlabeled data.
    • The approach shows promise for improving predictive rule accuracy in various machine learning applications.