Derives first lower bound on γ_t for mean-based algorithms in unknown-horizon bandit settings, proposes two new algorithms, and shows some are also no-regret.
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13 Pith papers cite this work. Polarity classification is still indexing.
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Individually calibrated predictors become collectively miscalibrated under Brier-optimal strategic responses with positive belief correlations, but VCG aggregation restores dominant-strategy incentive compatibility and near-optimal performance.
Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.
Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.
One-pass algorithms achieve Õ(M²/ε) space for regression splits and Õ(1/ε) space for Gini splits with matching Ω lower bounds.
Operator Boosting constructs compact neural-operator PDE surrogates by sequential residual learning with validation-selected shrinkage, yielding 72-95% parameter reduction and accuracy gains on 21 of 30 dataset-architecture pairs.
HANET is a hierarchical mixed-frequency attention model that improves forecasting of asset returns by attending over historical macroeconomic contexts rather than ignoring or naively concatenating macro features.
AgentGA optimizes agent seeds with genetic algorithms and parent-archive inheritance to improve autonomous code generation, beating a baseline on 15 of 16 Kaggle competitions.
LHCb reports first searches and the most stringent limits to date on rare decays such as b to s tau+ tau- and tau to three muons.
Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.
A systematic literature survey that classifies data-driven KPI prediction methods for 6G networks across KPI type, data source, protocol stack layer, horizon, model family, and objective.
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
NLP-derived attributes from construction incident reports remain strongly predictive of independently labeled safety outcomes even after removing potential label leakage, with injury severity now well predicted on a dataset of more than 90,000 reports.
citing papers explorer
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Mean-based algorithms: A lower bound and regret
Derives first lower bound on γ_t for mean-based algorithms in unknown-horizon bandit settings, proposes two new algorithms, and shows some are also no-regret.
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When Individually Calibrated Models Become Collectively Miscalibrated
Individually calibrated predictors become collectively miscalibrated under Brier-optimal strategic responses with positive belief correlations, but VCG aggregation restores dominant-strategy incentive compatibility and near-optimal performance.
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Polyhedral Instability Governs Regret in Online Learning
Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.
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A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis
Presents MBFC-2025 dataset and multi-view embeddings with fusion methods for media bias and factuality, reporting SOTA results on ACL-2020 and new benchmarks on MBFC-2025.
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Nearly Optimal Bounds for Computing Decision Tree Splits in Data Streams
One-pass algorithms achieve Õ(M²/ε) space for regression splits and Õ(1/ε) space for Gini splits with matching Ω lower bounds.
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Operator Boosting Produces Pareto-Efficient PDE Surrogates
Operator Boosting constructs compact neural-operator PDE surrogates by sequential residual learning with validation-selected shrinkage, yielding 72-95% parameter reduction and accuracy gains on 21 of 30 dataset-architecture pairs.
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Macro-aware time series forecasting via hierarchical mixed-frequency attention models
HANET is a hierarchical mixed-frequency attention model that improves forecasting of asset returns by attending over historical macroeconomic contexts rather than ignoring or naively concatenating macro features.
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AgentGA: Evolving Code Solutions in Agent-Seed Space
AgentGA optimizes agent seeds with genetic algorithms and parent-archive inheritance to improve autonomous code generation, beating a baseline on 15 of 16 Kaggle competitions.
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Rare and very rare decays at the LHCb experiment
LHCb reports first searches and the most stringent limits to date on rare decays such as b to s tau+ tau- and tau to three muons.
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Application of Deep Learning to Jet Charge Discrimination
Graph neural network achieves AUC of 0.883 for up versus anti-up quark jet charge discrimination in controlled QCD simulations.
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AI-Based KPI Prediction Methods in Future 6G Networks: A Survey
A systematic literature survey that classifies data-driven KPI prediction methods for 6G networks across KPI type, data source, protocol stack layer, horizon, model family, and objective.
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Impact of Validation Strategy on Machine Learning Performance in EEG-Based Alcoholism Classification
Nested cross-validation reveals optimistic bias in standard validation for EEG alcoholism classification, with AdaBoost reaching 78.3% accuracy and most model differences not statistically significant per McNemar's test.
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AI-based Prediction of Independent Construction Safety Outcomes from Universal Attributes
NLP-derived attributes from construction incident reports remain strongly predictive of independently labeled safety outcomes even after removing potential label leakage, with injury severity now well predicted on a dataset of more than 90,000 reports.