BoostLoRA grows effective adapter rank linearly via iterative boosting on hard examples with orthogonal low-rank updates, outperforming both single-shot ultra-low-rank adapters and full fine-tuning on math and code tasks with zero added inference overhead.
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XGBoost: A Scalable Tree Boosting System
Mixed citation behavior. Most common role is method (50%).
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cs.LG 5 astro-ph.EP 1 cond-mat.mtrl-sci 1 cs.AI 1 cs.DC 1 cs.HC 1 cs.PF 1 nucl-ex 1 q-bio.OT 1years
2026 13representative citing papers
Probabilistic PCA latent-space model with Bayesian inference reconstructs TNO near-IR spectra from photometry, achieving 95% credible-interval coverage and supporting taxonomy plus survey optimization.
XGBoost models trained on Screenome screenshot features and CES-D scores predict within-person depressive symptom change with AUCs of 0.906 and 0.755 under temporal holdout, generalizing to unseen people at AUC 0.821.
The ttbar production cross section in PbPb collisions at 5.36 TeV is measured as 3.42 +0.54-0.51 (stat) +0.50-0.43 (syst) μb and is consistent with NNLO pQCD predictions using nuclear PDFs.
Benchmark across 78 endpoint-split entries finds classical ML winning 47.4% of best performances over pretrained models, GNNs, and LLMs, with performance depending on model-task-split fit rather than scale.
COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
A knowledge-data dual paradigm using geomorphic priors and a tabular foundation model achieves baseline-level landslide susceptibility prediction accuracy with only 30% of typical data in tested regions.
Spark Policy Toolkit supplies semantic contracts plus mapInPandas/mapInArrow inference and executor-side split search so policy learning remains correct and fast on Spark clusters up to tens of millions of rows.
Domain-adapted ECG foundation models with self-supervised pretraining and selective fine-tuning reach macro-AUROC 0.8509 for multi-label structural heart disease detection on the EchoNext benchmark.
PCA and k-means on NHANES data identified four reproductive phenotypes in U.S. women aged 20-44, with one fragile subgroup showing 77.5% early multimorbidity prevalence; XGBoost improved discrimination over logistic regression but had worse calibration.
citing papers explorer
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BoostLoRA: Growing Effective Rank by Boosting Adapters
BoostLoRA grows effective adapter rank linearly via iterative boosting on hard examples with orthogonal low-rank updates, outperforming both single-shot ultra-low-rank adapters and full fine-tuning on math and code tasks with zero added inference overhead.
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Probabilistic Spectral Reconstruction of Trans-Neptunian Objects from Sparse Photometry: A Framework for Taxonomy, Survey Optimization, and Outlier Detection
Probabilistic PCA latent-space model with Bayesian inference reconstructs TNO near-IR spectra from photometry, achieving 95% credible-interval coverage and supporting taxonomy plus survey optimization.
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Within-person prediction of depressive symptom change using year-long Screenome data and CES-D assessments
XGBoost models trained on Screenome screenshot features and CES-D scores predict within-person depressive symptom change with AUCs of 0.906 and 0.755 under temporal holdout, generalizing to unseen people at AUC 0.821.
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Measurement of the top quark pair production cross section in PbPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.36 TeV
The ttbar production cross section in PbPb collisions at 5.36 TeV is measured as 3.42 +0.54-0.51 (stat) +0.50-0.43 (syst) μb and is consistent with NNLO pQCD predictions using nuclear PDFs.
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Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
Benchmark across 78 endpoint-split entries finds classical ML winning 47.4% of best performances over pretrained models, GNNs, and LLMs, with performance depending on model-task-split fit rather than scale.
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COMPASS: A Unified Decision-Intelligence System for Navigating Performance Trade-off in HPC
COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
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FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Foundation models outperform dataset-specific machine learning in energy time series forecasting across 54 datasets in 9 categories.
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Mind the Gap? A Distributional Comparison of Real and Synthetic Priors for Tabular Foundation Models
The synthetic prior for tabular foundation models covers only a narrow part of real table distributions, but this mismatch does not degrade model generalization.
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Data-Efficient Indentation Size Effect Correction in Steels Using Machine Learning and Physics-Guided Augmentation
Physics-guided data augmentation combined with neural networks enables accurate indentation size effect correction in steels from small sets of shallow nanoindentation measurements, outperforming Nix-Gao in the shallow regime.
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Knowledge-Data Dually Driven Paradigm for Accurate Landslide Susceptibility Prediction under Data-Scarce Conditions Using Geomorphic Priors and Tabular Foundation Model
A knowledge-data dual paradigm using geomorphic priors and a tabular foundation model achieves baseline-level landslide susceptibility prediction accuracy with only 30% of typical data in tested regions.
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Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark
Spark Policy Toolkit supplies semantic contracts plus mapInPandas/mapInArrow inference and executor-side split search so policy learning remains correct and fast on Spark clusters up to tens of millions of rows.
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Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
Domain-adapted ECG foundation models with self-supervised pretraining and selective fine-tuning reach macro-AUROC 0.8509 for multi-label structural heart disease detection on the EchoNext benchmark.
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AI-Derived Reproductive Phenotypes and Explainable ML for Concurrent Early Multimorbidity in U.S. Women: NHANES 2017-March 2020
PCA and k-means on NHANES data identified four reproductive phenotypes in U.S. women aged 20-44, with one fragile subgroup showing 77.5% early multimorbidity prevalence; XGBoost improved discrimination over logistic regression but had worse calibration.