For tabular in-context learning models, recourse is well-defined, its cost is bounded and converges to classical linear recourse as context grows; ASR-ICL finds sparse recourse with fewer queries.
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3 Pith papers cite this work. Polarity classification is still indexing.
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MachineLearningLM uses continued pretraining on SCM-synthesized ML tasks with random-forest distillation to give LLMs robust many-shot in-context learning on tabular classification, reaching random-forest accuracy levels while preserving general chat performance.
Zero-shot LLMs exhibit intervention bias in educational advising, over-recommending actions by 43 percentage points, while supervised DT and XGBoost models achieve near-zero calibration error and macro-F1 of 0.79.
citing papers explorer
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Algorithmic Recourse of In-Context Learning for Tabular Data
For tabular in-context learning models, recourse is well-defined, its cost is bounded and converges to classical linear recourse as context grows; ASR-ICL finds sparse recourse with fewer queries.
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MachineLearningLM: Scaling Many-shot In-context Learning via Continued Pretraining
MachineLearningLM uses continued pretraining on SCM-synthesized ML tasks with random-forest distillation to give LLMs robust many-shot in-context learning on tabular classification, reaching random-forest accuracy levels while preserving general chat performance.
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Deterministic Decisions for High-Stakes AI. A Zero-Egress Pipeline with the Deployability of RAG and the Accuracy of Machine Learning
Zero-shot LLMs exhibit intervention bias in educational advising, over-recommending actions by 43 percentage points, while supervised DT and XGBoost models achieve near-zero calibration error and macro-F1 of 0.79.