CREAMs architecturally encode concept-concept and concept-task relationships in CBMs plus an optional regularized side-channel, achieving competitive performance and improved intervenability under incomplete concepts.
A value for n-person games
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
BAMI mitigates precision and ambiguity biases in GUI grounding via coarse-to-fine focus and candidate selection, raising accuracy on ScreenSpot-Pro without training.
SFT on LLMs removes noise-like token interactions in a brief early phase before introducing overfitted ones, explaining inconsistent effectiveness across model scales.
VIP-COP is a black-box method that optimizes context for tabular foundation models by ranking and selecting high-value samples and features via online KernelSHAP regression, outperforming baselines on large high-dimensional data.
citing papers explorer
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Towards Reasonable Concept Bottleneck Models
CREAMs architecturally encode concept-concept and concept-task relationships in CBMs plus an optional regularized side-channel, achieving competitive performance and improved intervenability under incomplete concepts.
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BAMI: Training-Free Bias Mitigation in GUI Grounding
BAMI mitigates precision and ambiguity biases in GUI grounding via coarse-to-fine focus and candidate selection, raising accuracy on ScreenSpot-Pro without training.
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Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective
SFT on LLMs removes noise-like token interactions in a brief early phase before introducing overfitted ones, explaining inconsistent effectiveness across model scales.
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VIP-COP: Context Optimization for Tabular Foundation Models
VIP-COP is a black-box method that optimizes context for tabular foundation models by ranking and selecting high-value samples and features via online KernelSHAP regression, outperforming baselines on large high-dimensional data.