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NashEval is proposed, a general framework for robust contextual equilibrium learning that frame evaluation as a contextual game between two players, each selecting a distribution over agents as the strategy to receive greater collective preference than the other.
A novel method, dubbed Disentangle and Distillation-based Dynamic Ensemble for multi-modal Recommendation (D3ER), which introduces gradient boosting into MR for the first time to formalize the optimization objective for alternately learning HOI and HEI.
Few-shot audio classifiers may rely on foreground-background co-occurrences and fail when those correlations shift. On SpurAudio, the resulting representation shift is concentrated and class dependent: for ResNet12, the top 10 percent of channels explain 82.80 percent of the null-corrected shift contribution. We propos...
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