BC-ACI augments ACI with EWM bias correction and adaptive dead-zone to tighten prediction intervals under distribution shifts while preserving coverage and performance on stationary series.
Individual comparisons by ranking methods.Biometrics Bulletin, 1(6):80–83
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Kernel covariance embeddings of non-atomic Borel probability measures on locally compact Polish spaces induce singular centered Gaussians in the RKHS, making equality testing equivalent to singularity testing via the Feldman-Hajek dichotomy.
PHIDA uses persistent homology to constrain node-to-cluster mapping in ART-based online clustering and reports top average ranks on 24 benchmarks in both stationary and nonstationary settings.
QAOA achieves approximation ratios of 0.90-0.95 on N=5-20 Euclidean graphs, outperforming classical baselines by 2.7-4.4% with 2-3x faster runtimes and picojoule-scale energy use, projecting 8.2% real-world routing efficiency gains and 2.62 EJ annual US fuel savings.
A review summarizing parametric, nonparametric, Bayesian, and machine learning methods for efficacy analysis in clinical trials and identifying gaps such as high-dimensional data and missingness.
citing papers explorer
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Bias-Corrected Adaptive Conformal Inference for Multi-Horizon Time Series Forecasting
BC-ACI augments ACI with EWM bias correction and adaptive dead-zone to tighten prediction intervals under distribution shifts while preserving coverage and performance on stationary series.
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Kernel Embeddings and the Separation of Measure Phenomenon
Kernel covariance embeddings of non-atomic Borel probability measures on locally compact Polish spaces induce singular centered Gaussians in the RKHS, making equality testing equivalent to singularity testing via the Feldman-Hajek dichotomy.
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PHIDA: Persistence-Guided Node-to-Cluster Mapping for Online Clustering
PHIDA uses persistent homology to constrain node-to-cluster mapping in ART-based online clustering and reports top average ranks on 24 benchmarks in both stationary and nonstationary settings.
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Potential Energy Savings from Quantum Computing-Based Route Optimization
QAOA achieves approximation ratios of 0.90-0.95 on N=5-20 Euclidean graphs, outperforming classical baselines by 2.7-4.4% with 2-3x faster runtimes and picojoule-scale energy use, projecting 8.2% real-world routing efficiency gains and 2.62 EJ annual US fuel savings.
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Efficacy Analysis in Clinical Trials: A Comprehensive Review of Statistical and Machine Learning Approaches
A review summarizing parametric, nonparametric, Bayesian, and machine learning methods for efficacy analysis in clinical trials and identifying gaps such as high-dimensional data and missingness.