Efficient Credal Prediction through Decalibration
Efficient Credal Prediction through Decalibration explores Efficiently estimate uncertainty in machine learning models by predicting probability intervals, enabling safer deployment in critical applications.. Commercial viability score: 7/10 in Uncertainty Estimation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisFrom Entropy to Calibrated Uncertainty: Training Language Models to Reason About UncertaintyViability: 3.0
- Builds On ThisDirect Learning of Calibration-Aware Uncertainty for Neural PDE SurrogatesViability: 2.0
- Builds On ThisMitigating the Multiplicity Burden: The Role of Calibration in Reducing Predictive Multiplicity of ClassifiersViability: 4.0
- Builds On ThisKnow What You Know: Metacognitive Entropy Calibration for Verifiable RL ReasoningViability: 6.0
- Prior WorkCREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression