{"total":11,"items":[{"citing_arxiv_id":"2605.22243","ref_index":56,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies","primary_cat":"cs.LG","submitted_at":"2026-05-21T09:50:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20669","ref_index":26,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"GSA-YOLO: A High-Efficiency Framework via Structured Sparsity and Adaptive Knowledge Distillation for Real-Time X-ray Security Inspection","primary_cat":"cs.CV","submitted_at":"2026-05-20T03:36:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"GSA-YOLO modifies YOLOv8n with structured sparsity via Group Lasso and Sparse Structure Selection plus Adaptive Knowledge Distillation, reporting 189.62 FPS and mAP50:95 gains of 2.4% and 1.8% on HiXray and PIDray datasets.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17763","ref_index":45,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Comparing Two Categorical Gini Correlations with Applications to Classification Problems","primary_cat":"stat.ME","submitted_at":"2026-05-18T02:29:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16593","ref_index":279,"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":"2605.12830","ref_index":61,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Linking COPD Prevalence with Income Distribution: A Spatial Heterogeneous Compositional Regression via Geographically Weighted Penalized Approach","primary_cat":"stat.ME","submitted_at":"2026-05-12T23:54:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new geographically weighted penalized compositional regression model with pairwise fusion penalty is proposed to handle spatial heterogeneity and compositional covariates, demonstrated on U.S. income and COPD data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12118","ref_index":1,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions","primary_cat":"stat.ML","submitted_at":"2026-05-12T13:34:30+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"[exp (Q(λ, µ, η)(ti −t i−1))]xobs ti−1 ,xobs ti ,(3) where P(x obs t1 ) is the probability of observing xobs t1 (given some distribution over initial states), Q(λ, µ, η)∈R 2K ×2K is the generator matrix corresponding to the parameters, and [·]xobs ti−1 ,xobs ti indicates the row corresponding to state xobs ti−1 and the column corresponding to state xobs ti [1]. Q encodes the instantaneous rates of changing from state xobs ti−1 to state xobs ti ; see Appendix D for details on how it is constructed. Importantly, the exponential function in Equation (3) is thematrix exponential. For CT-HMMs, this operation accounts for all possible intrinsic trajectories between observed states. Most matrix exponential algorithms are at leastO((2 K)3), making exact likelihood calculations for large graphs"},{"citing_arxiv_id":"2605.00056","ref_index":61,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Smart Ensemble Learning Framework for Predicting Groundwater Heavy Metal Pollution","primary_cat":"cs.LG","submitted_at":"2026-04-29T21:40:18+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Ensemble learning with Gaussian copula transformation predicts groundwater heavy metal pollution index with high accuracy (R²=0.96) while identifying key contaminants via clustering.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18919","ref_index":39,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Proposing Topic Models and Evaluation Frameworks for Analyzing Associations with External Outcomes: An Application to Leadership Analysis Using Large-Scale Corporate Review Data","primary_cat":"cs.CL","submitted_at":"2026-04-20T23:52:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An LLM-based topic modeling method with a custom evaluation framework improves topic interpretability, specificity, and polarity consistency over prior approaches when linking corporate review text to external outcomes such as employee morale.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.05225","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"fastml: Guarded Resampling Workflows for Safer Automated Machine Learning in R","primary_cat":"stat.CO","submitted_at":"2026-04-06T22:41:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"fastml is an R package that enforces leakage-free preprocessing through guarded resampling and provides a unified interface for safer automated ML including survival analysis.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.03541","ref_index":54,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Choosing the Right Regularizer for Applied ML: Simulation Benchmarks of Popular Scikit-learn Regularization Frameworks","primary_cat":"cs.LG","submitted_at":"2026-04-04T01:33:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Simulations show Ridge, Lasso, and ElasticNet perform similarly for prediction at high sample-to-feature ratios, but Lasso feature selection recall drops to 0.18 under high multicollinearity and low SNR while ElasticNet holds at 0.93.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"well-constrained, with the exception of a small proportion of LassoCV estimates that saturate at the maximum boundary. For context, LassoCV defaults to a sequence of 100αvalues, geometrically spaced. The upper bound is defined as: αmax = 1 n X T y ∞ (10) and the lower bound is determined by theepsparameter (defaulting to 10 −3): αmin =α max ·eps (11) 13 Applying this logic to our simulated data yields a range ofα∈[1.54,33.0]. Although our study explores a significantly broader range (10 −3 to 10 5)-including values over 3,000 times larger than the scikit-learn defaults-we still observe a subset of values at the upper limit. These instances occur almost exclusively in under-determined regions: specifically, 1.56 observations per feature where SNR ≥0.2, or 15.6 observations per feature where SNR<0."},{"citing_arxiv_id":"2604.04964","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Bayesian Global-Local Shrinkage with Univariate Guidance for Ultra-High-Dimensional Regression","primary_cat":"stat.ME","submitted_at":"2026-04-03T22:41:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"BUGS embeds univariate marginal guidance into a regularized horseshoe prior to induce adaptive shrinkage, supplies theoretical contraction guarantees, and offers an active-set MCMC approximation that scales to p=1,000,000 while improving false-discovery control.","context_count":1,"top_context_role":"baseline","top_context_polarity":"baseline","context_text":"cg18537454- - chr10:22623213N Shore 134.72 [108.27, 157.19] cg18307303IL12B 1stExon/ 5'UTR chr5:158757456N Shore -160.58[-189.31, -124.81] cg12277678LINC00703 TSS200 chr10:4426258OpenSea 146.62 [120.41, 176.92] cg01911567FRMD4A Body chr10:13727536OpenSea -57.58 [-75.36, -39.97] cg03338348TSNAXIP1, RANBP10 TSS1500/ 1stExon/ 5'UTR chr16:67840472Island 24.36 [17.17, 30.95] cg04167854SERGEF Body chr11:18031127N Shelf -30.28 [-53.17, -4.93] cg23691090C22orf26, LOC150381 1stExon/ 5'UTR/ Body chr22:46449981Island -62.22 [-109.90, -17.09] cg09292826 TCTEX1D4, BTBD19 TSS1500/ TSS200 chr1:45274032S Shore -38.77 [-75.95, -6.10] cg12985929SEPT9 5'UTR/ Body chr17:75370611S Shore -25.97 [-49.70, -3.91] cg20961940SLC44A4 Body chr6:31832850S Shore -36."}],"limit":50,"offset":0}