Paper links early SIS prevalence to epidemic-threshold uncertainty
Early prevalence is shown to inform persistence through rate difference and infected diffusivity, with O(epsilon) posterior convergence.
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17 stories, newest first · one card per story
Stories tagged #uncertainty-quantification
TabPFN outperforms published models on three energetic-material properties
Untuned TabPFN on Chemprop embeddings beats Chemprop itself and is more than ten times faster than XGBoost.
Denoising Neural Processes model noisy inputs during meta-learning
On real air-quality sensors, DnNP cuts PM2.5 RMSE by up to 30% and NLL by up to 73% versus standard neural processes.
arXiv paper studies conformal prediction for time-dependent PDEs
Conformal prediction guarantees break down in time-dependent PDE settings, requiring discretization restrictions for coverage.
Paper introduces uncertainty-guided surrogate for microfluidic droplet generator inverse design
Neural ensemble surrogates with targeted enrichment and PSO achieve nearly doubled accuracy for optimizing microdroplet devices.
Physics-informed surrogate models one-dimensional p-n junction diodes
Median L1 error is 0.11% on potential and 1.5% and 0.61% on carrier densities.
Probabilistic meshless PDE methods extended to compact manifolds
The paper proves consistency and error bounds, then matches existing accuracy on cortical thickness data.
Clinical selective prediction misreads imbalance as uncertainty
Across 56 synthetic settings and three disease cohorts, standard entropy failed to protect minority patients.
BAARC reconstructs full Raman spectra from partial adaptive measurements
A simulated Bayesian framework picks small wavenumber patches until reconstruction and classification uncertainty are low.
Causal robustness margin proposed to measure mechanistic LLM uncertainty
Incorrect or uncertain answers had smaller margins across three open-source models and five QA datasets.
Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective
Paper shows conformal prediction set-size reduction tracks information gain and relates to Shannon mutual information.
One-Shot Private Confidence Regions via Resampling
A one-shot resampling method builds differentially private confidence regions without privatizing every resample.
U-Space: Uncovering When and Why Uncertainty Arises in Language Models
U-Space maps language-model residual states into a subspace that tracks token-level doubt and certainty without extra training.
Amortized Structured Stochastic Variational Inference for Gaussian Process Latent Variable Models
Paper applies amortized structured stochastic variational inference to Gaussian Process Latent Variable Models, letting latent posteriors depend on inducing points and improving manifold reconstruction metrics.
Uncertainty and Explainability in Deep Rough Volatility: A Neural Information-Theoretic Posterior Approach
Neural posterior inference calibrates rough Heston models and uses Hellinger-SHAP to explain implied-volatility information gain.
Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift
Paper derives nonasymptotic error bounds for split conformalized quantile regression under known covariate shift.
PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers
Researchers release PosteriorBench, a benchmark testing whether generative inverse solvers recover full reference posteriors across four physics-based inverse problems.