The paper proves the first optimal O(n^{-1/2}) Wasserstein-1 CLT rates for locally dependent sequences and geometrically ergodic Markov chains, plus new W_p rates for p greater than or equal to 2 under mild moments, with an application to U-statistics.
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Random forests.Machine learning, 45(1):5–32
13 Pith papers cite this work. Polarity classification is still indexing.
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A single germanium photodiode uses optoelectronic chromatic dispersion to generate multi-frequency RF features that machine-learning models convert into spectral reconstructions with 0.178 nm accuracy for single wavelengths in the C- and L-bands.
Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
A progressive symbolic regression framework discovers an interpretable analytical formula chain for skin friction prediction across subsonic to hypersonic attached flows using inviscid surface flow features.
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.
PACE interleaves active generation of diverse learners with subsequent pruning to produce smaller ensembles that retain performance and offer faithfulness guarantees.
XAI explanations should be narratives with continuous structure, cause-effect, fluency and diversity, and new metrics are needed to evaluate this better than standard NLP scores.
An LLM-driven evolutionary framework that writes and evolves Python-coded trading factors reports large gains in predictive accuracy on CSI300, but with incomplete validation.
Structured Gaussian process classifier embeds graph-encoded biological pathways into the kernel for uncertainty-aware omics classification and reports gains over unstructured baselines on three microbiome datasets.
Global color moments and RGB/HSV histograms alone support binary benign-malignant classification at up to 89% accuracy with classical ML classifiers, substantially above random baselines.
TabPFN maintains high ROC-AUC and structured attention under controlled additions of irrelevant features, nonlinear correlations, and mislabeled targets in binary classification.
AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
A TCN plus Attention-LSTM model trained on 2014-2024 Chinese A-share data outperforms static baselines and identifies prolonged undervaluation as the long-term driver and sudden cash-flow increases as the short-term trigger for repurchases.
citing papers explorer
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Wasserstein-p Central Limit Theorem Rates: From Local Dependence to Markov Chains
The paper proves the first optimal O(n^{-1/2}) Wasserstein-1 CLT rates for locally dependent sequences and geometrically ergodic Markov chains, plus new W_p rates for p greater than or equal to 2 under mild moments, with an application to U-statistics.
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Optoelectronic Chromatic Dispersion in a Single Photodiode for Machine-Learning-Based Computational Spectroscopy
A single germanium photodiode uses optoelectronic chromatic dispersion to generate multi-frequency RF features that machine-learning models convert into spectral reconstructions with 0.178 nm accuracy for single wavelengths in the C- and L-bands.
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Spectral Path Regression: Directional Chebyshev Harmonics for Interpretable Tabular Learning
Directional Chebyshev harmonics enable spectral path regression for tabular data with closed-form training, competitive accuracy, and explicit interpretability.
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Skin friction prediction for attached flows based on two-dimensional inviscid solutions
A progressive symbolic regression framework discovers an interpretable analytical formula chain for skin friction prediction across subsonic to hypersonic attached flows using inviscid surface flow features.
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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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PACE: Prune-And-Compress Ensemble Models
PACE interleaves active generation of diverse learners with subsequent pruning to produce smaller ensembles that retain performance and offer faithfulness guarantees.
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On the Importance and Evaluation of Narrativity in Natural Language AI Explanations
XAI explanations should be narratives with continuous structure, cause-effect, fluency and diversity, and new metrics are needed to evaluate this better than standard NLP scores.
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Cognitive Alpha Mining via LLM-Driven Code-Based Evolution
An LLM-driven evolutionary framework that writes and evolves Python-coded trading factors reports large gains in predictive accuracy on CSI300, but with incomplete validation.
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Structured Gaussian Processes for Uncertainty-Aware Classification of High-Dimensional, Small-Sampled Omics Data
Structured Gaussian process classifier embeds graph-encoded biological pathways into the kernel for uncertainty-aware omics classification and reports gains over unstructured baselines on three microbiome datasets.
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Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification
Global color moments and RGB/HSV histograms alone support binary benign-malignant classification at up to 89% accuracy with classical ML classifiers, substantially above random baselines.
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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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AlphaQuanter: An End-to-End Tool-Augmented Agentic Reinforcement Learning Framework for Stock Trading
AlphaQuanter introduces a single-agent tool-augmented RL framework for stock trading that learns dynamic policies over a transparent decision workflow and reports state-of-the-art financial metrics.
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Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks
A TCN plus Attention-LSTM model trained on 2014-2024 Chinese A-share data outperforms static baselines and identifies prolonged undervaluation as the long-term driver and sudden cash-flow increases as the short-term trigger for repurchases.