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Paper

Linear Classifiers that Encourage Constructive Adaptation

by Independent / Community 00163110327a03bdf24f1b7797c39ad7e297d2b2

Free2AITools Nexus Index

66.9

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 80

P: Popularity 55

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss in model performance in deployment. In this work, we aim to address this problem by learning classifiers that encourage decision subjects to change their features in a way that leads to improvement in both predicted and true outcome. We frame the dynamics of prediction and adaptation as a two-stage game, and characte...

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Semantic Scholar 19 Citations

Paper Information Summary
Entity Passport
Registry ID	00163110327a03bdf24f1b7797c39ad7e297d2b2
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{00163110327a03bdf24f1b7797c39ad7e297d2b2,
  author = {Unknown},
  title = {Linear Classifiers that Encourage Constructive Adaptation Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00163110327a03bdf24f1b7797c39ad7e297d2b2}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Linear Classifiers that Encourage Constructive Adaptation [Paper]. Free2AITools. https://api.semanticscholar.org/00163110327a03bdf24f1b7797c39ad7e297d2b2

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 80

Popularity (P) 55

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Linear Classifiers that Encourage Constructive Adaptation: Authority (A:80), Popularity (P:55), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

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📝 Executive Summary

"Machine learning systems are often used in settings where individuals adapt their features to obtain a desired outcome. In such settings, strategic behavior leads to a sharp loss in model performance in deployment. In this work, we aim to address this problem by learning classifiers that encourage decision subjects to change their features in a way that leads to improvement in both predicted and true outcome. We frame the dynamics of prediction and adaptation as a two-stage game, and characte..."

❝ Cite Node

@article{Unknown2026Linear,
  title={Linear Classifiers that Encourage Constructive Adaptation},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈19CitationsSemantic Scholar

🏛️80AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

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🏷️ Research Topics

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📦Data Source: semantic_scholar

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
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downloads: 0
stars: null
forks: null
citations: 19

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