RoSHAP is a robust feature-ranking metric that summarizes the distributional properties of SHAP values via bootstrap resampling and asymptotic normality to reward active, strong, and stable features.
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Xgboost: A scalable tree boosting system
Baseline reference. 67% of citing Pith papers use this work as a benchmark or comparison.
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- method a: Single-span bridges do not have a bent and are not included in the ML-based predictions. b: There is a small proportion of bridges that have more than 7 columns per bent. These bridges are considered as outliers and have been excluded before the model training. Figure 7: Proposed classifier chain for imputing missing attributes. An XGBoost classifier [40] is used as the predictive model to impute the four target attributes. The hyperparameters of each classifier are tuned using Bayesian optim
- baseline Despite their efficiency, such adaptation can be unstable and prone to overfitting when supervision is scarce [48, 10, 23], highlighting the need for more data-efficient and robust fine-tuning strategies. Interestingly, the tabular learning community has long relied on a different paradigm to address similar challenges: gradient boosting. Systems such as XGBoost [ 5], LightGBM [20], and CatBoost [35] consistently achieve strong performance across a wide range of tabular tasks and are known to be
- baseline wi for each paper by log-normalizing and aggregating its GitHub stars, citation counts, influential citations, and Altmetric score. The distribution of the four log-normalized ground-truth impact metrics utilized in the dataset is shown in Figure 4. Baselines.We benchmark FAME against three distinct categories of evaluators. First, we evaluate ML models, including XGBoost [9], SVR [11, 27], Transformer [31] and TGCN [39], trained directly 5 Table 1: Prospective forecasting performance across an
- method Extreme wildfires are challenging to predict [41], as they emerge from the complex interplay of fire weather [9, 10, 37], topography [33], vegetation fuels [32, 37], and human factors such as ignition and fire suppression [16, 19, 26, 44], all of which are difficult to fully represent in process-based wildfire models. Whereas machine learning (ML) approaches such as XGBoost [8] have shown promise in wildfire prediction [5, 18, 21, 25, 42], outperforming process-based wildfire models [41], they t
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CogAlpha combines LLM reasoning with code-level evolutionary search to discover financial alphas that show higher predictive accuracy and generalization than prior methods on five stock datasets.
O'Prior, a compositional synthetic prior with hierarchical SCMs, realism engines, stress modules, and curriculum protocols, improves tabular foundation model accuracy and robustness on real benchmarks when architecture and compute are held fixed.
Probe trajectories across token positions in LRMs, combined with signal-processing features, improve prediction of future model outputs over static probes on safety and math tasks.
SpecX is a new large-scale multi-modal spectroscopy benchmark with tiered datasets that supports unified evaluation across specialized models and MLLMs, showing specialized models excel at signal-level tasks while MLLMs are stronger in high-level reasoning but weaker in precise spectral grounding.
FAME models scientific topic trajectories in continuous time to forecast paper impact more accurately than LLMs by aligning manuscripts with field momentum in a dynamic latent space.
BoostLLM trains sequential PEFT adapters in a boosting framework with tree path inputs to improve LLM performance on few-shot tabular classification, matching or exceeding XGBoost.
A boosting-enhanced Bayesian conjugate model for oncology demand forecasting outperforms ARIMA, LSTM, and XGBoost in trend direction accuracy by up to 38.25% on real Brazilian hospital data.
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.
Traj-CoA is a multi-agent LLM framework that sequentially processes noisy five-year EHR data via worker agents into EHRMem for manager-agent lung cancer risk prediction and outperforms four categories of baselines in zero-shot evaluation.
EAPO adapts wildfire models to new environments via k-nearest neighbor data retrieval and hybrid fine-tuning that emphasizes rare extreme events, achieving ROC-AUC 0.7310 on real data.
A methodology decomposes total uncertainty in regional risk assessment into contributions from probabilistic exposure characterization and other sources using analytical and simulation approaches.
EmDT combines UMAP clustering with a Transformer-based diffusion process to create synthetic fraud samples that improve XGBoost classification on credit card fraud data while preserving correlations and privacy.
TabPFN maintains high ROC-AUC and structured attention under controlled additions of irrelevant features, nonlinear correlations, and mislabeled targets in binary classification.
Localized polygon-based models trained on clustered bus stops achieve prediction accuracy comparable to a single global model when using ridership, spatial, weather, and temporal features.
citing papers explorer
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RoSHAP: A Distributional Framework and Robust Metric for Stable Feature Attribution
RoSHAP is a robust feature-ranking metric that summarizes the distributional properties of SHAP values via bootstrap resampling and asymptotic normality to reward active, strong, and stable features.
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Cognitive Alpha Mining via LLM-Driven Code-Based Evolution
CogAlpha combines LLM reasoning with code-level evolutionary search to discover financial alphas that show higher predictive accuracy and generalization than prior methods on five stock datasets.
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Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality
O'Prior, a compositional synthetic prior with hierarchical SCMs, realism engines, stress modules, and curriculum protocols, improves tabular foundation model accuracy and robustness on real benchmarks when architecture and compute are held fixed.
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Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics
Probe trajectories across token positions in LRMs, combined with signal-processing features, improve prediction of future model outputs over static probes on safety and math tasks.
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SpecX: A Large-Scale Benchmark for Multi-Modal Spectroscopy and Cross-Paradigm Evaluation
SpecX is a new large-scale multi-modal spectroscopy benchmark with tiered datasets that supports unified evaluation across specialized models and MLLMs, showing specialized models excel at signal-level tasks while MLLMs are stronger in high-level reasoning but weaker in precise spectral grounding.
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FAME: Forecasting Academic Impact via Continuous-Time Manifold Evolution
FAME models scientific topic trajectories in continuous time to forecast paper impact more accurately than LLMs by aligning manuscripts with field momentum in a dynamic latent space.
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BoostLLM: Boosting-inspired LLM Fine-tuning for Few-shot Tabular Classification
BoostLLM trains sequential PEFT adapters in a boosting framework with tree path inputs to improve LLM performance on few-shot tabular classification, matching or exceeding XGBoost.
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Forecasting Oncology Demand Trends with Boosting-Based Bayesian Conjugate Models
A boosting-enhanced Bayesian conjugate model for oncology demand forecasting outperforms ARIMA, LSTM, and XGBoost in trend direction accuracy by up to 38.25% on real Brazilian hospital data.
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TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
TabPFN-2.5 scales tabular foundation models to 20x larger datasets, outperforms tuned tree models on TabArena, achieves near-perfect win rates against default XGBoost, and adds a distillation engine for fast production deployment.
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Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction
Traj-CoA is a multi-agent LLM framework that sequentially processes noisy five-year EHR data via worker agents into EHRMem for manager-agent lung cancer risk prediction and outperforms four categories of baselines in zero-shot evaluation.
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Environment-Adaptive Preference Optimization for Wildfire Prediction
EAPO adapts wildfire models to new environments via k-nearest neighbor data retrieval and hybrid fine-tuning that emphasizes rare extreme events, achieving ROC-AUC 0.7310 on real data.
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Quantifying Exposure Information Uncertainty in Regional Risk Assessment
A methodology decomposes total uncertainty in regional risk assessment into contributions from probabilistic exposure characterization and other sources using analytical and simulation approaches.
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EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection
EmDT combines UMAP clustering with a Transformer-based diffusion process to create synthetic fraud samples that improve XGBoost classification on credit card fraud data while preserving correlations and privacy.
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Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
TabPFN maintains high ROC-AUC and structured attention under controlled additions of irrelevant features, nonlinear correlations, and mislabeled targets in binary classification.
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Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
Localized polygon-based models trained on clustered bus stops achieve prediction accuracy comparable to a single global model when using ridership, spatial, weather, and temporal features.