{"total":16,"items":[{"citing_arxiv_id":"2606.29326","ref_index":2,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Gradient boosting with vector-valued leafs","primary_cat":"stat.ML","submitted_at":"2026-06-28T10:42:29+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Extends gradient boosting framework to vector inputs and sketches an efficient algorithm for histogram-based decision trees.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17890","ref_index":41,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Dynamic Rollout Editing for Reducing Overthinking in RL-Trained Reasoning Models","primary_cat":"cs.CL","submitted_at":"2026-06-16T13:10:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Dynamic Rollout Editing reduces overthinking in RL-trained LLMs by editing post-answer continuations in successful rollouts and preferring the edited versions within GRPO groups.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10725","ref_index":24,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports","primary_cat":"cs.LG","submitted_at":"2026-06-09T11:33:46+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Pre-AF 13 is a 13-feature interpretable risk score from NLP-processed EHR discharge reports that predicts 24-month AF incidence in CVD patients with ROC AUC 0.725, outperforming CHARGE-AF, C2HEST, MHS, and HAVOC.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28655","ref_index":70,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"AutoScientists: Self-Organizing Agent Teams for Long-Running Scientific Experimentation","primary_cat":"cs.AI","submitted_at":"2026-05-27T15:56:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19520","ref_index":116,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10","primary_cat":"astro-ph.GA","submitted_at":"2026-05-19T08:21:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A multimodal neural network trained on MPA-JHU references produces SFR, stellar mass, and metallicity estimates for 547 million low-redshift galaxies in DESI LS DR10.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13337","ref_index":23,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Context-Aware Web Attack Detection in Open-Source SIEM Systems via MITRE ATT&CK-Enriched Behavioral Profiling","primary_cat":"cs.CR","submitted_at":"2026-05-13T10:54:36+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Smart-SIEM adds context-aware ML profiling to Wazuh SIEM, lifting binary attack detection F1 to 0.967 and six-class categorization to 0.914 while recovering from concept drift via retraining.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.13219","ref_index":47,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Comparative analysis of missing data imputation methods for CSST survey: Impact on photometric redshift estimation performance","primary_cat":"astro-ph.GA","submitted_at":"2026-05-13T09:12:58+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"KNN imputation gives highest photo-z accuracy under ideal random missingness with complete training data, while SAITS is more robust for incomplete training sets and realistic mixed missingness patterns in CSST data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09450","ref_index":21,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue","primary_cat":"astro-ph.GA","submitted_at":"2026-05-10T10:02:08+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06343","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models","primary_cat":"cs.AI","submitted_at":"2026-05-07T14:29:31+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03281","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Donor-Aware scRNA-seq Benchmarks for IBD Classification","primary_cat":"q-bio.QM","submitted_at":"2026-05-05T02:13:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Donor-aware benchmarks show AUROCs up to 0.978 for IBD classification from scRNA-seq using CLR cell-type compositions and GatedStructuralCFN embeddings, with compartment stratification improving both performance and feature stability.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06684","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"From Canopy to Collision: A Hybrid Predictive Framework for Identifying Risk Factors in Tree-Involved Traffic Crashes","primary_cat":"cs.LG","submitted_at":"2026-04-25T20:25:27+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Hybrid predictive modeling of crash data identifies non-use of restraints as the primary risk factor for severe injury in collisions involving trees.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"across diverse benchmark datasets [47]. The algorithm was specifically designed to address fundamental limitations in conventional gradient boosting frameworks, particularly in handling categorical variables and preventing target leakage during model training. The algorithmic foundation of CatBoost i ntroduces two key approaches , ordered boosting and ordered target statistics [48]. Both approaches were developed to address prediction shift, a phenomenon arising from target leakage that affects traditional gradient boosting implementations. The CatBoost model constructs an ensemble of symmetric decision trees iteratively, where the final prediction can be expressed using Equation 1: F(x) = ∑ ft(x, θt) T t=1 [1] Where 𝐹(𝑥) represents the aggregate prediction for input feature vector 𝑥, 𝑇 denotes the total"},{"citing_arxiv_id":"2601.01119","ref_index":68,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Community-Based Early-Stage Chronic Kidney Disease Screening using Explainable Machine Learning for Low-Resource Settings","primary_cat":"cs.LG","submitted_at":"2026-01-03T08:43:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Machine learning models trained on Bangladeshi community data achieve 89-90% balanced accuracy for early CKD detection using few accessible features, outperforming traditional screening tools and generalizing across external datasets from India, UAE, and Bangladesh.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.05564","ref_index":121,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"TabICL: A Tabular Foundation Model for In-Context Learning on Large Data","primary_cat":"cs.LG","submitted_at":"2025-02-08T13:25:04+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"TabICL scales in-context learning to large tabular data via column-then-row attention for row embeddings followed by a transformer, matching TabPFNv2 speed and performance while outperforming it and CatBoost on datasets over 10K samples.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2107.07511","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification","primary_cat":"cs.LG","submitted_at":"2021-07-15T17:59:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"UNKNOWN","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Pith review generated a malformed one-line summary.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"t′=1 st′1 {t′≥t−K}≥ 1−α } . With these adjusted quantiles in hand, we form prediction sets at each time step in the usual way, C(Xt) = [ ˆf(Xt)− ˆqtˆu(Xt), ˆf(Xt) + ˆqtˆu(Xt) ] . 24 We run this procedure on the Yandex Weather Prediction dataset. This dataset is part of the Shifts Project [29], which also provides an ensemble of 10 pretrained CatBoost [30] models for making the temper- ature predictions. We take the average prediction of these models as our base model ˆf. Each of the models has its own internal variance; we take the average of these variances as our uncertainty scalar ˆu. The dataset includes an in-distribution split of fresh data from the same time frame that the base model was trained"},{"citing_arxiv_id":"2003.06505","ref_index":1,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data","primary_cat":"stat.ML","submitted_at":"2020-03-13T23:10:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"AutoGluon-Tabular achieves superior accuracy on tabular classification and regression by multi-layer model ensembling and stacking, outperforming other AutoML frameworks on 50 benchmarks and Kaggle competitions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"1907.01960","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Fashion Retail: Forecasting Demand for New Items","primary_cat":"cs.OH","submitted_at":"2019-06-27T09:31:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"Generalized ML models trained on past sales data forecast demand for new fashion items from their attributes, with experiments across neural architectures and loss functions showing robust performance.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}