Papers with tabular classification
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? (2020.acl-main)
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| Challenge: | a new study examines the impact of algorithmic explanations on simulatability of machine learning models . a model is simulatable when a person can predict its behavior on new inputs . |
| Approach: | They conduct human subject tests to isolate effect of algorithmic explanations on simulatability . they find ratings of explanations are not predictive of how helpful they are . |
| Outcome: | The results provide the first reliable estimates of how explanations influence simulatability . they show that ratings are not predictive of how helpful explanations are . |
Generalization or Memorization? Multi-Agent vs. Baseline LLMs and AutoML Models for Tabular Classification (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly used for structured tabular data. |
| Approach: | They evaluate a representative modular Multi-Agent LLM framework against state-of-the-art AutoML systems and established baselines. |
| Outcome: | The proposed model outperforms AutoML on pre-cutoff and post-cut off datasets. |