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cs.LG2025
Faithful and Interpretable Explanations for Complex Ensemble Time Series Forecasts using Surrogate Models and Forecastability Analysis
Yikai Zhao, Jiekai Ma
Modern time series forecasting increasingly relies on complex ensemble models generated by AutoML systems like AutoGluon, delivering superior accuracy but with significant costs to…
cs.CL2025
Play Favorites: A Statistical Method to Measure Self-Bias in LLM-as-a-Judge
Evangelia Spiliopoulou, Riccardo Fogliato, Hanna Burnsky +4
Large language models (LLMs) can serve as judges that offer rapid and reliable assessments of other LLM outputs. However, models may systematically assign overly favorable ratings…
cs.IR2025
From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals
Ze Liu, Xianquan Wang, Shuochen Liu +5
Implicit feedback is central to modern recommender systems but is inherently noisy, often impairing model training and degrading user experience. At scale, such noise can mislead l…