Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty explores This paper presents a new approach to Gaussian process regression that improves uncertainty quantification in the presence of input measurement errors.. Commercial viability score: 2/10 in Statistical Modeling.
Updated 2 days ago
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
- Prior WorkOptimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual functionViability: 2.0
- Prior WorkOn Regret Bounds of Thompson Sampling for Bayesian OptimizationViability: 2.0
- Prior WorkHigh-Dimensional Gaussian Mean Estimation under Realizable ContaminationViability: 2.0
- Higher ViabilityGGMPs: Generalized Gaussian Mixture ProcessesViability: 4.0
- Higher ViabilityInverse-Free Sparse Variational Gaussian ProcessesViability: 6.0