arXiv (All)
open access
Adversarial Estimation of Binary Configuration Probabilities under Sparse Dependence
We consider the problem of estimating binary configuration probabilities, which is common in operations management applications, including product bundling, advertising, etc. Existing approaches typically model each assortment as a category and apply multinomial models to estimate the choice probabi
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Diversified Multinomial Logit Contextual Bandits#363253
- An Agent-Based Simulation Model Integrating Generative AI and Fast and Frugal Heuristics for Enhanced Marketing Automation#781277
- Engineering Robustness into Personal Agents with the AI Workflow Store#175107
- DSABench: Benchmarking Source Discovery, Coordination, and Execution for Multi-Database Agents#1325596
- SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation#259343
Record · ID 1199645
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive