{"total":18,"items":[{"citing_arxiv_id":"2606.25942","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Elliptical Regularized Hotelling Testing for High Dimensional Data","primary_cat":"stat.ME","submitted_at":"2026-06-24T15:19:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proposes ERHT-CC test based on spatial median and spatial-sign covariance with Cauchy aggregation over ridge parameters, deriving asymptotic normality and local power under elliptical symmetry.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.25107","ref_index":35,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Nonparametric Estimation of Optimal Stochastic Just-In-Time Adaptive Interventions for Distal Outcomes","primary_cat":"stat.ME","submitted_at":"2026-06-23T19:27:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A nonparametric estimator of the regimen-response curve for stochastic JITAIs on distal outcomes is developed, with weak convergence to a Gaussian process and asymptotic theory for the optimizing policy.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21466","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Likelihood Inference for Latent Network Models under Snowball Sampling","primary_cat":"stat.ME","submitted_at":"2026-06-19T14:19:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Exact likelihood for continuous latent space models under snowball sampling reduces to closed form via conditional edge independence, enabling stochastic EM for unbiased inference on networks like patent co-inventors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19743","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data","primary_cat":"stat.ME","submitted_at":"2026-06-18T03:20:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Develops an NNGP spatio-temporal model with SMC squared inference for haplotype frequency estimation from pooled genetic data, demonstrated on 3- and 6-marker antimalarial resistance datasets in Africa.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19303","ref_index":105,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"P-K-GCN: Physics-augmented Koopman-enhanced Graph Convolutional Network for Deep Spatiotemporal Super-resolution","primary_cat":"cs.LG","submitted_at":"2026-06-17T17:26:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"P-K-GCN integrates continuous spline GCN, Koopman linearization, and physics augmentation for spatiotemporal super-resolution on irregular geometries, claiming theoretical error reduction via Rademacher complexity bounds and superior accuracy on cardiac electrodynamics.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17413","ref_index":105,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows","primary_cat":"cs.LG","submitted_at":"2026-06-16T01:49:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A deep learning method amortizes probabilistic XCO2 retrieval from OCO-2 spectra via Laplace approximations and normalizing flows, trained on simulations with model errors to achieve faster inference and better-calibrated uncertainties than operational solvers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12677","ref_index":83,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Restricted Multivariate Spatial Modeling","primary_cat":"stat.ME","submitted_at":"2026-06-10T21:01:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Develops a restricted MCAR model via reparameterization to measure and control informativeness in multivariate spatial modeling of health events across subgroups.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04673","ref_index":6,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Improving Longitudinal Targeted Maximum Likelihood Estimation in Target Trial Emulation using Joint Calibrated Weights","primary_cat":"stat.ME","submitted_at":"2026-06-03T09:55:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Joint calibrated LTMLE integrates LTMLE with joint calibrated weights to improve finite-sample efficiency and robustness to misspecification for per-protocol effect estimation in target trial emulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18724","ref_index":83,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Sensitivity analysis for causal mediation: bridge score, sharp sensitivity bounds, and calibration","primary_cat":"stat.ME","submitted_at":"2026-05-18T17:50:57+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16593","ref_index":55,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Policy Learning with Observational Data: The Case of Hepatitis C Treatment for HIV/HCV Co-Infected Patients","primary_cat":"stat.AP","submitted_at":"2026-05-15T19:56:25+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A weighted K-means plus decision-tree pipeline learns multi-action policies from observational data and is applied to HCV treatment choices for HIV co-infected patients, finding a high-clearance subgroup and potential cost savings of CAN$3.6-4.9 million.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09506","ref_index":41,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Accelerating Bayesian Phylogenetic Inference via Delayed Acceptance Sequential Monte Carlo with Random Forest Surrogates","primary_cat":"stat.ME","submitted_at":"2026-05-10T12:27:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A random-forest surrogate for likelihood change under tree moves enables delayed-acceptance SMC that cuts expensive likelihood evaluations while preserving posterior estimates on simulated and real phylogenetic data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21721","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Riesz Representer Perspective on Targeted Learning","primary_cat":"stat.ME","submitted_at":"2026-04-23T14:24:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A recursive Riesz representer-based targeted minimum loss estimation procedure unifies asymptotically efficient estimation of causal estimands such as time-varying treatment effects and mediation effects.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"recognized that when the target estimand admits the structure above, the EIFϕthen takes the form ϕ(P)(O) =h(X;η) +α(X)(Y−η(X))−Ψ(η),(1) whereXdenotes all variables inOupon which the nuisance functionηdepends andh(·)is a non-stochastic trans- formation of the nuisance functionη. Here,αis called aRiesz representerand is analogous to inverse probability weights (Horvitz & Thompson, 1952) and balancing weights (Zubizarreta, 2015) commonly used for estimation in causal inference and missing data problems. Such a representation is motivated by the convenience of estimating αusing specialized machine learning algorithms (Chernozhukov et al., 2022a) or de-biasing well-known regression algorithms (Bruns-Smith et al., 2025). However, as Williams et al. (2025) argue based on the work of Newey (1994),"},{"citing_arxiv_id":"2604.18644","ref_index":17,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"FASE : A Fairness-Aware Spatiotemporal Event Graph Framework for Predictive Policing","primary_cat":"cs.LG","submitted_at":"2026-04-19T21:22:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"FASE pairs a spatiotemporal graph neural network and multivariate Hawkes process for crime prediction with a fairness-constrained linear program for patrol allocation, showing that allocation fairness holds in simulation but a 3.5 percentage point detection gap between minority and non-minority ZIPs","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.02522","ref_index":78,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Opal: Private Memory for Personal AI","primary_cat":"cs.CR","submitted_at":"2026-04-02T21:23:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Opal enables private long-term memory for personal AI by decoupling reasoning to a trusted enclave with a lightweight knowledge graph and piggybacking reindexing on ORAM accesses.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"The Internet Society. [76] Tao Ge, Xin Chan, Xiaoyang Wang, Dian Yu, Haitao Mi, and Dong Yu. 2024. Scaling synthetic data creation with 1,000,000,000 personas. arXiv preprint arXiv:2406.20094(2024). [77] Xinyang Ge, Hsuan-Chi Kuo, and Weidong Cui. 2022. Hecate: Lifting and Shifting On-Premises Workloads to an Untrusted Cloud. InCCS. ACM, 1231-1242. [78] Jim Gemmell, Gordon Bell, and Roger Lueder. 2006. MyLifeBits: a personal database for everything.Commun. ACM49, 1 (2006), 88-95. doi:10.1145/1107458.1107460 [79] Arthur Gervais, Reza Shokri, Adish Singla, Srdjan Capkun, and Vin- cent Lenders. 2014. Quantifying Web-Search Privacy. InCCS. ACM, 966-977. [80] Emily Gliklich, Rong Guo, and Regan W. Bergmark."},{"citing_arxiv_id":"2512.03760","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A decay-adjusted spatio-temporal model to account for the impact of mass drug administration on neglected tropical disease prevalence","primary_cat":"stat.AP","submitted_at":"2025-12-03T13:06:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new decay-adjusted spatio-temporal model improves estimation of neglected tropical disease prevalence by explicitly accounting for the waning impact of mass drug administration in sparse survey data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.15453","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Testing Conditional Independence via the Spectral Generalized Covariance Measure: Beyond Euclidean Data","primary_cat":"stat.ME","submitted_at":"2025-11-19T14:10:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2505.05707","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Crowding Out The Noise: Algorithmic Collective Action Under Differential Privacy","primary_cat":"cs.LG","submitted_at":"2025-05-09T00:55:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Differential privacy reduces algorithmic collective action effectiveness, with formal lower bounds on success probability depending on collective size and privacy parameters, plus experimental verification on neural nets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.09543","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks","primary_cat":"cs.LG","submitted_at":"2019-07-22T19:32:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A physics-constrained cGAN is trained as an image-to-image translator on remote-sensing layers to recover spatial sensitivities of urban land-use change to macroeconomic indicators via backpropagation gradients.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}