CredibleDFGO adds explicit supervision of covariance credibility to differentiable factor graph optimization for GNSS by using proper scoring rules on the predictive distribution, yielding more trustworthy uncertainties on urban test scenes.
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Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
Hard-label delivery via multipass or SLS matches or beats soft-label training on annotator disagreement data when annotations are sparse and leads to flatter minima.
citing papers explorer
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CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision
CredibleDFGO adds explicit supervision of covariance credibility to differentiable factor graph optimization for GNSS by using proper scoring rules on the predictive distribution, yielding more trustworthy uncertainties on urban test scenes.
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Training-Free Probabilistic Time-Series Forecasting with Conformal Seasonal Pools
Conformal Seasonal Pools is a training-free method that outperforms DeepNPTS on CRPS, quantile loss, and especially 95% coverage (0.89 vs 0.66) across six time-series datasets while being over 500x faster on CPU.
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Same Target, Different Basins: Hard vs. Soft Labels for Annotator Distributions
Hard-label delivery via multipass or SLS matches or beats soft-label training on annotator disagreement data when annotations are sparse and leads to flatter minima.