4 Search Results for "Li, Sihan"

Document
DOI: 10.4230/LIPIcs.ITCS.2026.91
Limitations of Membership Queries in Testable Learning

Authors: Jane Lange and Mingda Qiao

Published in: LIPIcs, Volume 362, 17th Innovations in Theoretical Computer Science Conference (ITCS 2026)

Abstract
Membership queries (MQ) often yield speedups for learning tasks, particularly in the distribution-specific setting. We show that in the testable learning model of Rubinfeld and Vasilyan [Rubinfeld and Vasilyan, 2023], membership queries cannot decrease the time complexity of testable learning algorithms beyond the complexity of sample-only distribution-specific learning. In the testable learning model, the learner must output a hypothesis whenever the data distribution satisfies a desired property, and if it outputs a hypothesis, the hypothesis must be near-optimal. We give a general reduction from sample-based refutation of boolean concept classes, as presented in [Vadhan, 2017; Kothari and Livni, 2018], to testable learning with queries (TL-Q). This yields lower bounds for TL-Q via the reduction from learning to refutation given in [Kothari and Livni, 2018]. The result is that, relative to a concept class and a distribution family, no m-sample TL-Q algorithm can be super-polynomially more time-efficient than the best m-sample PAC learner. Finally, we define a class of "statistical" MQ algorithms that encompasses many known distribution-specific MQ learners, such as those based on influence estimation or subcube-conditional statistical queries. We show that TL-Q algorithms in this class imply efficient statistical-query refutation and learning algorithms. Thus, combined with known SQ dimension lower bounds, our results imply that these efficient membership query learners cannot be made testable.
Cite as

Jane Lange and Mingda Qiao. Limitations of Membership Queries in Testable Learning. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 91:1-91:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

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@InProceedings{lange_et_al:LIPIcs.ITCS.2026.91,
  author =	{Lange, Jane and Qiao, Mingda},
  title =	{{Limitations of Membership Queries in Testable Learning}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{91:1--91:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-410-9},
  ISSN =	{1868-8969},
  year =	{2026},
  volume =	{362},
  editor =	{Saraf, Shubhangi},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2026.91},
  URN =		{urn:nbn:de:0030-drops-253785},
  doi =		{10.4230/LIPIcs.ITCS.2026.91},
  annote =	{Keywords: Testable learning, PAC learning}
}
Document
DOI: 10.4230/LIPIcs.ITCS.2025.26
Settling the Complexity of Testing Grainedness of Distributions, and Application to Uniformity Testing in the Huge Object Model

Authors: Clément L. Canonne, Sayantan Sen, and Joy Qiping Yang

Published in: LIPIcs, Volume 325, 16th Innovations in Theoretical Computer Science Conference (ITCS 2025)

Abstract
In this work, we study the problem of testing m-grainedness of probability distributions over an n-element universe 𝒰, or, equivalently, of whether a probability distribution is induced by a multiset S ⊆ 𝒰 of size |S| = m. Recently, Goldreich and Ron (Computational Complexity, 2023) proved that Ω(n^c) samples are necessary for testing this property, for any c < 1 and m = Θ(n). They also conjectured that Ω(m/(log m)) samples are necessary for testing this property when m = Θ(n). In this work, we positively settle this conjecture. Using a known connection to the Distribution over Huge objects (DoHo) model introduced by Goldreich and Ron (TheoretiCS, 2023), we leverage our results to provide improved bounds for uniformity testing in the DoHo model.
Cite as

Clément L. Canonne, Sayantan Sen, and Joy Qiping Yang. Settling the Complexity of Testing Grainedness of Distributions, and Application to Uniformity Testing in the Huge Object Model. In 16th Innovations in Theoretical Computer Science Conference (ITCS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 325, pp. 26:1-26:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{canonne_et_al:LIPIcs.ITCS.2025.26,
  author =	{Canonne, Cl\'{e}ment L. and Sen, Sayantan and Yang, Joy Qiping},
  title =	{{Settling the Complexity of Testing Grainedness of Distributions, and Application to Uniformity Testing in the Huge Object Model}},
  booktitle =	{16th Innovations in Theoretical Computer Science Conference (ITCS 2025)},
  pages =	{26:1--26:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-361-4},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{325},
  editor =	{Meka, Raghu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2025.26},
  URN =		{urn:nbn:de:0030-drops-226543},
  doi =		{10.4230/LIPIcs.ITCS.2025.26},
  annote =	{Keywords: Distribution testing, Uniformity testing, Huge Object Model, Lower bounds}
}
Document
Position
DOI: 10.4230/TGDK.1.1.2
Large Language Models and Knowledge Graphs: Opportunities and Challenges

Authors: Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Large Language Models (LLMs) have taken Knowledge Representation - and the world - by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.
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Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo, Sneha Singhania, Jiaoyan Chen, Stefan Dietze, Hajira Jabeen, Janna Omeliyanenko, Wen Zhang, Matteo Lissandrini, Russa Biswas, Gerard de Melo, Angela Bonifati, Edlira Vakaj, Mauro Dragoni, and Damien Graux. Large Language Models and Knowledge Graphs: Opportunities and Challenges. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 2:1-2:38, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{pan_et_al:TGDK.1.1.2,
  author =	{Pan, Jeff Z. and Razniewski, Simon and Kalo, Jan-Christoph and Singhania, Sneha and Chen, Jiaoyan and Dietze, Stefan and Jabeen, Hajira and Omeliyanenko, Janna and Zhang, Wen and Lissandrini, Matteo and Biswas, Russa and de Melo, Gerard and Bonifati, Angela and Vakaj, Edlira and Dragoni, Mauro and Graux, Damien},
  title =	{{Large Language Models and Knowledge Graphs: Opportunities and Challenges}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{2:1--2:38},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.2},
  URN =		{urn:nbn:de:0030-drops-194766},
  doi =		{10.4230/TGDK.1.1.2},
  annote =	{Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2018.8
Targeted Test Generation for Actor Systems

Authors: Sihan Li, Farah Hariri, and Gul Agha

Published in: LIPIcs, Volume 109, 32nd European Conference on Object-Oriented Programming (ECOOP 2018)

Abstract
This paper addresses the problem of targeted test generation for actor systems. Specifically, we propose a method to support generation of system-level tests to cover a given code location in an actor system. The test generation method consists of two phases. First, static analysis is used to construct an abstraction of an entire actor system in terms of a message flow graph (MFG). An MFG captures potential actor interactions that are defined in a program. Second, a backwards symbolic execution (BSE) from a target location to an "entry point" of the actor system is performed. BSE uses the MFG constructed in the first phase of our targeted test generation method to guide execution across actors. Because concurrency leads to a huge search space which can potentially be explored through BSE, we prune the search space by using two heuristics combined with a feedback-directed technique. We implement our method in Tap, a tool for Java Akka programs, and evaluate Tap on the Savina benchmarks as well as four open source projects. Our evaluation shows that the Tap achieves a relatively high target coverage (78% on 1,000 targets) and detects six previously unreported bugs in the subjects.
Cite as

Sihan Li, Farah Hariri, and Gul Agha. Targeted Test Generation for Actor Systems. In 32nd European Conference on Object-Oriented Programming (ECOOP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 109, pp. 8:1-8:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{li_et_al:LIPIcs.ECOOP.2018.8,
  author =	{Li, Sihan and Hariri, Farah and Agha, Gul},
  title =	{{Targeted Test Generation for Actor Systems}},
  booktitle =	{32nd European Conference on Object-Oriented Programming (ECOOP 2018)},
  pages =	{8:1--8:31},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-079-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{109},
  editor =	{Millstein, Todd},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2018.8},
  URN =		{urn:nbn:de:0030-drops-92135},
  doi =		{10.4230/LIPIcs.ECOOP.2018.8},
  annote =	{Keywords: actors, symbolic execution, test generation, static analysis}
}
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