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Documents authored by Huang, Yi


Found 4 Possible Name Variants:

Huang, Yi

Document
Network Construction with Ordered Constraints

Authors: Yi Huang, Mano Vikash Janardhanan, and Lev Reyzin

Published in: LIPIcs, Volume 93, 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017)


Abstract
In this paper, we study the problem of constructing a network by observing ordered connectivity constraints, which we define herein. These ordered constraints are made to capture realistic properties of real-world problems that are not reflected in previous, more general models. We give hardness of approximation results and nearly-matching upper bounds for the offline problem, and we study the online problem in both general graphs and restricted sub-classes. In the online problem, for general graphs, we give exponentially better upper bounds than exist for algorithms for general connectivity problems. For the restricted classes of stars and paths we are able to find algorithms with optimal competitive ratios, the latter of which involve analysis using a potential function defined over PQ-trees.

Cite as

Yi Huang, Mano Vikash Janardhanan, and Lev Reyzin. Network Construction with Ordered Constraints. In 37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 93, pp. 34:1-34:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{huang_et_al:LIPIcs.FSTTCS.2017.34,
  author =	{Huang, Yi and Janardhanan, Mano Vikash and Reyzin, Lev},
  title =	{{Network Construction with Ordered Constraints}},
  booktitle =	{37th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2017)},
  pages =	{34:1--34:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-055-2},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{93},
  editor =	{Lokam, Satya and Ramanujam, R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2017.34},
  URN =		{urn:nbn:de:0030-drops-84090},
  doi =		{10.4230/LIPIcs.FSTTCS.2017.34},
  annote =	{Keywords: subgraph connectivity, network construction, ordered connectivity constraints, PQ-trees}
}

Huang, Yipeng

Document
QDB: From Quantum Algorithms Towards Correct Quantum Programs

Authors: Yipeng Huang and Margaret Martonosi

Published in: OASIcs, Volume 67, 9th Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU 2018)


Abstract
With the advent of small-scale prototype quantum computers, researchers can now code and run quantum algorithms that were previously proposed but not fully implemented. In support of this growing interest in quantum computing experimentation, programmers need new tools and techniques to write and debug QC code. In this work, we implement a range of QC algorithms and programs in order to discover what types of bugs occur and what defenses against those bugs are possible in QC programs. We conduct our study by running small-sized QC programs in QC simulators in order to replicate published results in QC implementations. Where possible, we cross-validate results from programs written in different QC languages for the same problems and inputs. Drawing on this experience, we provide a taxonomy for QC bugs, and we propose QC language features that would aid in writing correct code.

Cite as

Yipeng Huang and Margaret Martonosi. QDB: From Quantum Algorithms Towards Correct Quantum Programs. In 9th Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU 2018). Open Access Series in Informatics (OASIcs), Volume 67, pp. 4:1-4:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{huang_et_al:OASIcs.PLATEAU.2018.4,
  author =	{Huang, Yipeng and Martonosi, Margaret},
  title =	{{QDB: From Quantum Algorithms Towards Correct Quantum Programs}},
  booktitle =	{9th Workshop on Evaluation and Usability of Programming Languages and Tools (PLATEAU 2018)},
  pages =	{4:1--4:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-091-0},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{67},
  editor =	{Barik, Titus and Sunshine, Joshua and Chasins, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.PLATEAU.2018.4},
  URN =		{urn:nbn:de:0030-drops-101962},
  doi =		{10.4230/OASIcs.PLATEAU.2018.4},
  annote =	{Keywords: correctness, debugging}
}

Huang, Zhiyi

Document
Track A: Algorithms, Complexity and Games
Scalable and Jointly Differentially Private Packing

Authors: Zhiyi Huang and Xue Zhu

Published in: LIPIcs, Volume 132, 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019)


Abstract
We introduce an (epsilon, delta)-jointly differentially private algorithm for packing problems. Our algorithm not only achieves the optimal trade-off between the privacy parameter epsilon and the minimum supply requirement (up to logarithmic factors), but is also scalable in the sense that the running time is linear in the number of agents n. Previous algorithms either run in cubic time in n, or require a minimum supply per resource that is sqrt{n} times larger than the best possible.

Cite as

Zhiyi Huang and Xue Zhu. Scalable and Jointly Differentially Private Packing. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 73:1-73:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{huang_et_al:LIPIcs.ICALP.2019.73,
  author =	{Huang, Zhiyi and Zhu, Xue},
  title =	{{Scalable and Jointly Differentially Private Packing}},
  booktitle =	{46th International Colloquium on Automata, Languages, and Programming (ICALP 2019)},
  pages =	{73:1--73:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-109-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{132},
  editor =	{Baier, Christel and Chatzigiannakis, Ioannis and Flocchini, Paola and Leonardi, Stefano},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2019.73},
  URN =		{urn:nbn:de:0030-drops-106498},
  doi =		{10.4230/LIPIcs.ICALP.2019.73},
  annote =	{Keywords: Joint differential privacy, packing, scalable algorithms}
}
Document
Online Makespan Minimization: The Power of Restart

Authors: Zhiyi Huang, Ning Kang, Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang

Published in: LIPIcs, Volume 116, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018)


Abstract
We consider the online makespan minimization problem on identical machines. Chen and Vestjens (ORL 1997) show that the largest processing time first (LPT) algorithm is 1.5-competitive. For the special case of two machines, Noga and Seiden (TCS 2001) introduce the SLEEPY algorithm that achieves a competitive ratio of (5 - sqrt{5})/2 ~~ 1.382, matching the lower bound by Chen and Vestjens (ORL 1997). Furthermore, Noga and Seiden note that in many applications one can kill a job and restart it later, and they leave an open problem whether algorithms with restart can obtain better competitive ratios. We resolve this long-standing open problem on the positive end. Our algorithm has a natural rule for killing a processing job: a newly-arrived job replaces the smallest processing job if 1) the new job is larger than other pending jobs, 2) the new job is much larger than the processing one, and 3) the processed portion is small relative to the size of the new job. With appropriate choice of parameters, we show that our algorithm improves the 1.5 competitive ratio for the general case, and the 1.382 competitive ratio for the two-machine case.

Cite as

Zhiyi Huang, Ning Kang, Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang. Online Makespan Minimization: The Power of Restart. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 116, pp. 14:1-14:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{huang_et_al:LIPIcs.APPROX-RANDOM.2018.14,
  author =	{Huang, Zhiyi and Kang, Ning and Tang, Zhihao Gavin and Wu, Xiaowei and Zhang, Yuhao},
  title =	{{Online Makespan Minimization: The Power of Restart}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2018)},
  pages =	{14:1--14:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-085-9},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{116},
  editor =	{Blais, Eric and Jansen, Klaus and D. P. Rolim, Jos\'{e} and Steurer, David},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.APPROX-RANDOM.2018.14},
  URN =		{urn:nbn:de:0030-drops-94182},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2018.14},
  annote =	{Keywords: Online Scheduling, Makespan Minimization, Identical Machines}
}
Document
Online Vertex-Weighted Bipartite Matching: Beating 1-1/e with Random Arrivals

Authors: Zhiyi Huang, Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang

Published in: LIPIcs, Volume 107, 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)


Abstract
We introduce a weighted version of the ranking algorithm by Karp et al. (STOC 1990), and prove a competitive ratio of 0.6534 for the vertex-weighted online bipartite matching problem when online vertices arrive in random order. Our result shows that random arrivals help beating the 1-1/e barrier even in the vertex-weighted case. We build on the randomized primal-dual framework by Devanur et al. (SODA 2013) and design a two dimensional gain sharing function, which depends not only on the rank of the offline vertex, but also on the arrival time of the online vertex. To our knowledge, this is the first competitive ratio strictly larger than 1-1/e for an online bipartite matching problem achieved under the randomized primal-dual framework. Our algorithm has a natural interpretation that offline vertices offer a larger portion of their weights to the online vertices as time goes by, and each online vertex matches the neighbor with the highest offer at its arrival.

Cite as

Zhiyi Huang, Zhihao Gavin Tang, Xiaowei Wu, and Yuhao Zhang. Online Vertex-Weighted Bipartite Matching: Beating 1-1/e with Random Arrivals. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 79:1-79:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{huang_et_al:LIPIcs.ICALP.2018.79,
  author =	{Huang, Zhiyi and Tang, Zhihao Gavin and Wu, Xiaowei and Zhang, Yuhao},
  title =	{{Online Vertex-Weighted Bipartite Matching: Beating 1-1/e with Random Arrivals}},
  booktitle =	{45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)},
  pages =	{79:1--79:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-076-7},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{107},
  editor =	{Chatzigiannakis, Ioannis and Kaklamanis, Christos and Marx, D\'{a}niel and Sannella, Donald},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2018.79},
  URN =		{urn:nbn:de:0030-drops-90830},
  doi =		{10.4230/LIPIcs.ICALP.2018.79},
  annote =	{Keywords: Vertex Weighted, Online Bipartite Matching, Randomized Primal-Dual}
}

Huang, Qunying

Document
LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection

Authors: Jinmeng Rao, Song Gao, Yuhao Kang, and Qunying Huang

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)


Abstract
The prevalence of location-based services contributes to the explosive growth of individual-level trajectory data and raises public concerns about privacy issues. In this research, we propose a novel LSTM-TrajGAN approach, which is an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. We design a loss metric function TrajLoss to measure the trajectory similarity losses for model training and optimization. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset. Compared with other common geomasking methods, our model can better prevent users from being re-identified, and it also preserves essential spatial, temporal, and thematic characteristics of the real trajectory data. The model better balances the effectiveness of trajectory privacy protection and the utility for spatial and temporal analyses, which offers new insights into the GeoAI-powered privacy protection.

Cite as

Jinmeng Rao, Song Gao, Yuhao Kang, and Qunying Huang. LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 12:1-12:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


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@InProceedings{rao_et_al:LIPIcs.GIScience.2021.I.12,
  author =	{Rao, Jinmeng and Gao, Song and Kang, Yuhao and Huang, Qunying},
  title =	{{LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{12:1--12:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.12},
  URN =		{urn:nbn:de:0030-drops-130471},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.12},
  annote =	{Keywords: GeoAI, Deep Learning, Trajectory Privacy, Generative Adversarial Networks}
}
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