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Documents authored by Tang, Jie

Found 2 Possible Name Variants:

Tang, Jie

Document
DOI: 10.4230/DagRep.11.8.11
Structure and Learning (Dagstuhl Seminar 21362)

Authors: Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen

Published in: Dagstuhl Reports, Volume 11, Issue 8 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 Year Roadmap for AI" at AAAI 2019. In this Dagstuhl seminar, we discussed related problems from an interdiscplinary perspective, in particular, Cognitive Science, Cognitive Psychology, Physics, Computational Humor, Linguistic, Machine Learning, and AI. This report overviews presentations and working groups during the seminar, and lists two open problems.
Cite as

Tiansi Dong, Achim Rettinger, Jie Tang, Barbara Tversky, and Frank van Harmelen. Structure and Learning (Dagstuhl Seminar 21362). In Dagstuhl Reports, Volume 11, Issue 8, pp. 11-34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{dong_et_al:DagRep.11.8.11,
  author =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  title =	{{Structure and Learning (Dagstuhl Seminar 21362)}},
  pages =	{11--34},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{8},
  editor =	{Dong, Tiansi and Rettinger, Achim and Tang, Jie and Tversky, Barbara and van Harmelen, Frank},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.8.11},
  URN =		{urn:nbn:de:0030-drops-157670},
  doi =		{10.4230/DagRep.11.8.11},
  annote =	{Keywords: Knowledge graph, Machine learning, Neural-symbol unification}
}

Tang, Shaojie

Document
DOI: 10.4230/LIPIcs.ISAAC.2021.69
Adaptive Regularized Submodular Maximization

Authors: Shaojie Tang and Jing Yuan

Published in: LIPIcs, Volume 212, 32nd International Symposium on Algorithms and Computation (ISAAC 2021)

Abstract
In this paper, we study the problem of maximizing the difference between an adaptive submodular (revenue) function and a non-negative modular (cost) function. The input of our problem is a set of n items, where each item has a particular state drawn from some known prior distribution The revenue function g is defined over items and states, and the cost function c is defined over items, i.e., each item has a fixed cost. The state of each item is unknown initially and one must select an item in order to observe its realized state. A policy π specifies which item to pick next based on the observations made so far. Denote by g_{avg}(π) the expected revenue of π and let c_{avg}(π) denote the expected cost of π. Our objective is to identify the best policy π^o ∈ arg max_π g_{avg}(π)-c_{avg}(π) under a k-cardinality constraint. Since our objective function can take on both negative and positive values, the existing results of submodular maximization may not be applicable. To overcome this challenge, we develop a series of effective solutions with performance guarantees. Let π^o denote the optimal policy. For the case when g is adaptive monotone and adaptive submodular, we develop an effective policy π^l such that g_{avg}(π^l) - c_{avg}(π^l) ≥ (1-1/e-ε)g_{avg}(π^o) - c_{avg}(π^o), using only O(nε^{-2}log ε^{-1}) value oracle queries. For the case when g is adaptive submodular, we present a randomized policy π^r such that g_{avg}(π^r) - c_{avg}(π^r) ≥ 1/eg_{avg}(π^o) - c_{avg}(π^o).
Cite as

Shaojie Tang and Jing Yuan. Adaptive Regularized Submodular Maximization. In 32nd International Symposium on Algorithms and Computation (ISAAC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 212, pp. 69:1-69:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{tang_et_al:LIPIcs.ISAAC.2021.69,
  author =	{Tang, Shaojie and Yuan, Jing},
  title =	{{Adaptive Regularized Submodular Maximization}},
  booktitle =	{32nd International Symposium on Algorithms and Computation (ISAAC 2021)},
  pages =	{69:1--69:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-214-3},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{212},
  editor =	{Ahn, Hee-Kap and Sadakane, Kunihiko},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ISAAC.2021.69},
  URN =		{urn:nbn:de:0030-drops-155029},
  doi =		{10.4230/LIPIcs.ISAAC.2021.69},
  annote =	{Keywords: Adaptive submodularity, approximation algorithms, active learning}
}
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