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Mingda Qiao

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Articles by Mingda Qiao

Computer Science > Data Structures and Algorithms

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By Shivam Nadimpalli, Mingda Qiao, Ronitt Rubinfeld
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (343) Issue with article?

[2512.02279] Limitations of Membership Queries in Testable Learning

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By Mingda Qiao
Open in Who Shared Issue with article?

[2511.15507] Sample-Adaptivity Tradeoff in On-Demand Sampling

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By Omar Montasser, Mingda Qiao
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (538) Issue with article?

No Price Tags? No Problem: Query Strategies for Unpriced Information

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By Mingda Qiao, Ronitt Rubinfeld
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (695) Issue with article?

[2508.09592] Online Prediction with Limited Selectivity

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By Mingda Qiao
| arXiv
arXiv:2508.09592 (cs) View PDF HTML (experimental) Subjects: Machine Learning (cs.LG); Data Structures and Algorithms (cs.DS) Cite as: arXiv:2508.09592 [cs.LG] (or arXiv:2508.09592v1 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2508.09592 Submission history From: Licheng Liu [ view email] [v1] Wed, 13 Aug 2025 08:17:12 UTC (24 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps...
Open in Who Shared View Duplicates (150) Issue with article?

[2407.13979] Truthfulness of Calibration Measures

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By Mingda Qiao, Kunhe Yang, Eric Zhao
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (599) Issue with article?

[2402.15169] Platforms for Efficient and Incentive-Aware Collaboration

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By Mingda Qiao, Kunhe Yang
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (693) Issue with article?

[2402.10445] Collaborative Learning with Different Labeling Functions

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By Mingda Qiao
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (695) Issue with article?
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