Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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19 Pith papers cite this work, alongside 1,006 external citations. Polarity classification is still indexing.
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The Bayes-optimal classifier for elliptical distributions is derived in closed form from the radial generator, yielding a tuning-free alternative to spline GAMs with proven consistency.
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
An identification theorem shows that a randomized experiment and simulator together recover causal model values from confounded logs, with logs used only afterward to reduce estimation error.
Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
Non-closing Lie brackets of intervention-response fields can serve as a high-recall screen for causal edges under latent confounding, but do not by themselves identify general DAGs.
MEC-Cox extends generalized entropy calibration to ATT-weighted IPW Cox regression by balancing ML prognostic summaries between treated patients and external controls.
Derives a closed-form task-specific strictly proper scoring rule for ATE estimation by matching local curvature of the IPW error metric.
Rashomon-seeded annealing repurposes Rashomon sets as warm starts for annealed importance sampling to enable full posterior inference in factorial designs without exhaustive enumeration.
42% of significant turn-level associations in LLM conversation analysis are spurious due to unaccounted autocorrelation, with a validated two-stage correction framework improving replication.
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
Diffusion model improves GFS/GEFS ensemble CAPE forecasts and incorporates aerosol optical depths for additional gains.
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
Excessively long blocks lower asymptotic relative efficiency in the block-maxima method, and new likelihood and diagnostic procedures are proposed to check whether a chosen length is adequate under rounding or censoring.
Any spectral diagnostic that depends only on singular values or the symmetric part of a degree-normalized attention matrix is invariant under transpose, so it cannot see the direction of information flow.
A hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.
Raw IFS forecasts outperform raw AIFS for wind speed at all horizons, but post-processing with EMOS or QR reduces the gap, leaving IFS ahead mainly at short leads.
citing papers explorer
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Private Rate-Double-Robust Inference
Local privacy mechanisms preserve rate-double-robustness, enabling unbiased and semiparametrically efficient inference on target parameters indexed linearly by infinite-dimensional and nonlinearly by low-dimensional components from noisy private data.
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Closed-form fractional radial links for elliptical Mahalanobis discriminant analysis
The Bayes-optimal classifier for elliptical distributions is derived in closed form from the radial generator, yielding a tuning-free alternative to spline GAMs with proven consistency.
-
Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
DR-ME is the first semiparametrically efficient finite-location kernel test for interpretable distributional treatment effects, using orthogonal doubly robust features derived from observational data.
-
Advance warning of $\gamma$-ray blazar flares from \textit{Fermi}-LAT light curves: a strictly causal machine-learning backtest
A causal machine-learning model using variability features from Fermi-LAT light curves predicts blazar flare activity within 90 days with 86% recall on held-out data for one FSRQ.
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The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice
An identification theorem shows that a randomized experiment and simulator together recover causal model values from confounded logs, with logs used only afterward to reduce estimation error.
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Heat-Kernel Entropy Profiles and Geometric Effective Sample Size for Weighted Measures on Manifolds
Heat-kernel smoothing over weighted points on a compact manifold yields a scale-dependent geometric effective sample size that discounts nearby and duplicate particles.
-
Latent Confounded Causal Discovery via Lie Bracket Geometry
Non-closing Lie brackets of intervention-response fields can serve as a high-recall screen for causal edges under latent confounding, but do not by themselves identify general DAGs.
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MEC-Cox: Machine-Learning-Assisted Generalized Entropy Calibration for ATT Marginal Hazard-Ratio Estimation
MEC-Cox extends generalized entropy calibration to ATT-weighted IPW Cox regression by balancing ML prognostic summaries between treated patients and external controls.
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Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference
Derives a closed-form task-specific strictly proper scoring rule for ATE estimation by matching local curvature of the IPW error metric.
-
Rashomon-Seeded Annealing for Robust Bayesian Inference in Factorial Designs
Rashomon-seeded annealing repurposes Rashomon sets as warm starts for annealed importance sampling to enable full posterior inference in factorial designs without exhaustive enumeration.
-
The Autocorrelation Blind Spot: Why 42% of Turn-Level Findings in LLM Conversation Analysis May Be Spurious
42% of significant turn-level associations in LLM conversation analysis are spurious due to unaccounted autocorrelation, with a validated two-stage correction framework improving replication.
-
Restricted Multivariate Spatial Modeling
Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.
-
Improving Ensemble CAPE Forecasts with a Diffusion Model Incorporating Aerosol Information
Diffusion model improves GFS/GEFS ensemble CAPE forecasts and incorporates aerosol optical depths for additional gains.
-
Estimating treatment duration effects via clone-censor-weight: a breast cancer case study
The clone-censor-weight approach is formalized and tested via simulations before application to a breast cancer cohort comparing 2 versus 5 years of adjuvant tamoxifen, yielding estimates with substantial uncertainty.
-
How long should a block be?
Excessively long blocks lower asymptotic relative efficiency in the block-maxima method, and new likelihood and diagnostic procedures are proposed to check whether a chosen length is adequate under rounding or censoring.
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Self-Attention as Transport: Limits of Symmetric Spectral Diagnostics
Any spectral diagnostic that depends only on singular values or the symmetric part of a degree-normalized attention matrix is invariant under transpose, so it cannot see the direction of information flow.
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Dynamic Multi-Pair Trading Strategy in Cryptocurrency Markets with Deep Reinforcement Learning
A hybrid DRL system for multi-pair crypto trading with deterministic risk shielding outperforms a heuristic baseline at 10% significance on Binance futures data.
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AI and physics-based weather forecasting: A comparative study
Raw IFS forecasts outperform raw AIFS for wind speed at all horizons, but post-processing with EMOS or QR reduces the gap, leaving IFS ahead mainly at short leads.
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