Recasts covariance shrinkage as risk minimization over stochastic interpolants between distributions, recovering known estimators via scheduling, couplings, and early stopping, and proposing a neural estimator with quadratic risk bounds.
A well-conditioned estimator for large- dimensional covariance matrices
8 Pith papers cite this work. Polarity classification is still indexing.
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Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.
A methodological framework for BCIs that separates speed and accuracy with Gain and Conservation measures combined into an alpha-controlled balance for tunable operating points.
Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.
JBShield is vulnerable to adaptive JB-GCG attacks (up to 53% ASR) because jailbreak representations occupy a distinct region in refusal-direction space; the new RTV defense using Mahalanobis detection on multi-layer fingerprints reaches 0.99 AUROC and limits adaptive ASR to 7%.
Task-aligned supervised geometric stability predicts linear steerability with high accuracy while unsupervised stability detects representational drift earlier and with lower false alarms than CKA or Procrustes.
Asymmetry PRISM-CPU achieves 4.5x-24.1x speedups over reference solvers on N=100-2000 problems and GPU completes all 500 accounts in 109.5s where OSQP completes 4.
citing papers explorer
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Covariance Shrinkage via Stochastic Interpolation
Recasts covariance shrinkage as risk minimization over stochastic interpolants between distributions, recovering known estimators via scheduling, couplings, and early stopping, and proposing a neural estimator with quadratic risk bounds.
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Error Bounds for Importance Sampling with Estimated Proposal Distributions
Derives non-asymptotic error bounds for standard, defensive, and self-normalized importance sampling with random KDE proposals from geometrically ergodic Markov chains, separating n^{-1/2} Monte Carlo error from MIAE/MISE proposal error.
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Causal Anomaly Detection for Lithium-Ion Battery Degradation
CausalHealth detects lithium-ion battery degradation with 100% sensitivity and up to 402-cycle lead time using causal anomaly scores from voltage, current, temperature, and resistance time series across seven cells.
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A Methodological Framework for Explicit Control of the Speed-Accuracy Trade-off in Brain-Computer Interfaces
A methodological framework for BCIs that separates speed and accuracy with Gain and Conservation measures combined into an alpha-controlled balance for tunable operating points.
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Estimating Precision Matrices for High-Dimensional Interval-Valued Data
Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.
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The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability
Task-aligned supervised geometric stability predicts linear steerability with high accuracy while unsupervised stability detects representational drift earlier and with lower false alarms than CKA or Procrustes.
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Asymmetry PRISM: A CPU/GPU Portfolio Optimization Engine for Deadline-Bounded Institutional Rebalancing
Asymmetry PRISM-CPU achieves 4.5x-24.1x speedups over reference solvers on N=100-2000 problems and GPU completes all 500 accounts in 109.5s where OSQP completes 4.