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The paper introduces Ψ-Resilience, a model-free feature importance method that derives explanations from 1D topological signals using 0-dimensional persistence of class-disagreement landscapes, achieving competitive Spearman correlations with SHAP and mutual information on synthetic and real datasets.
This paper introduces Deep-BQRL, a model-free distributional reinforcement learning framework that extends buffered-quantile learning to neural function approximation for risk-sensitive decision-making, showing improved performance over existing methods in specific experiments.
AdaPLD is a training-free method that improves model-free speculative decoding by using adaptive retrieval combining lexical and semantic similarity, and constructing branched reuse hypotheses to handle continuation uncertainty, achieving up to 3.10x decoding speedup.