3 Search Results for "Zhang, Hongyang"


Document
Recovery from Non-Decomposable Distance Oracles

Authors: Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, and Shufan Zhang

Published in: LIPIcs, Volume 251, 14th Innovations in Theoretical Computer Science Conference (ITCS 2023)


Abstract
A line of work has looked at the problem of recovering an input from distance queries. In this setting, there is an unknown sequence s ∈ {0,1}^{≤ n}, and one chooses a set of queries y ∈ {0,1}^𝒪(n) and receives d(s,y) for a distance function d. The goal is to make as few queries as possible to recover s. Although this problem is well-studied for decomposable distances, i.e., distances of the form d(s,y) = ∑_{i=1}^n f(s_i, y_i) for some function f, which includes the important cases of Hamming distance, 𝓁_p-norms, and M-estimators, to the best of our knowledge this problem has not been studied for non-decomposable distances, for which there are important special cases such as edit distance, dynamic time warping (DTW), Fréchet distance, earth mover’s distance, and so on. We initiate the study and develop a general framework for such distances. Interestingly, for some distances such as DTW or Fréchet, exact recovery of the sequence s is provably impossible, and so we show by allowing the characters in y to be drawn from a slightly larger alphabet this then becomes possible. In a number of cases we obtain optimal or near-optimal query complexity. We also study the role of adaptivity for a number of different distance functions. One motivation for understanding non-adaptivity is that the query sequence can be fixed and the distances of the input to the queries provide a non-linear embedding of the input, which can be used in downstream applications involving, e.g., neural networks for natural language processing.

Cite as

Zhuangfei Hu, Xinda Li, David P. Woodruff, Hongyang Zhang, and Shufan Zhang. Recovery from Non-Decomposable Distance Oracles. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 73:1-73:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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@InProceedings{hu_et_al:LIPIcs.ITCS.2023.73,
  author =	{Hu, Zhuangfei and Li, Xinda and Woodruff, David P. and Zhang, Hongyang and Zhang, Shufan},
  title =	{{Recovery from Non-Decomposable Distance Oracles}},
  booktitle =	{14th Innovations in Theoretical Computer Science Conference (ITCS 2023)},
  pages =	{73:1--73:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-263-1},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{251},
  editor =	{Tauman Kalai, Yael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2023.73},
  URN =		{urn:nbn:de:0030-drops-175767},
  doi =		{10.4230/LIPIcs.ITCS.2023.73},
  annote =	{Keywords: Sequence Recovery, Edit Distance, DTW Distance, Fr\'{e}chet Distance}
}
Document
Improved Algorithms for Adaptive Compressed Sensing

Authors: Vasileios Nakos, Xiaofei Shi, David P. Woodruff, and Hongyang Zhang

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


Abstract
In the problem of adaptive compressed sensing, one wants to estimate an approximately k-sparse vector x in R^n from m linear measurements A_1 x, A_2 x,..., A_m x, where A_i can be chosen based on the outcomes A_1 x,..., A_{i-1} x of previous measurements. The goal is to output a vector x^ for which |x-x^|_p <=C * min_{k-sparse x'} |x-x'|_q, with probability at least 2/3, where C > 0 is an approximation factor. Indyk, Price and Woodruff (FOCS'11) gave an algorithm for p=q=2 for C = 1+epsilon with O((k/epsilon) loglog (n/k)) measurements and O(log^*(k) loglog (n)) rounds of adaptivity. We first improve their bounds, obtaining a scheme with O(k * loglog (n/k) + (k/epsilon) * loglog(1/epsilon)) measurements and O(log^*(k) loglog (n)) rounds, as well as a scheme with O((k/epsilon) * loglog (n log (n/k))) measurements and an optimal O(loglog (n)) rounds. We then provide novel adaptive compressed sensing schemes with improved bounds for (p,p) for every 0 < p < 2. We show that the improvement from O(k log(n/k)) measurements to O(k log log (n/k)) measurements in the adaptive setting can persist with a better epsilon-dependence for other values of p and q. For example, when (p,q) = (1,1), we obtain O(k/sqrt{epsilon} * log log n log^3 (1/epsilon)) measurements. We obtain nearly matching lower bounds, showing our algorithms are close to optimal. Along the way, we also obtain the first nearly-optimal bounds for (p,p) schemes for every 0 < p < 2 even in the non-adaptive setting.

Cite as

Vasileios Nakos, Xiaofei Shi, David P. Woodruff, and Hongyang Zhang. Improved Algorithms for Adaptive Compressed Sensing. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 90:1-90:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


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@InProceedings{nakos_et_al:LIPIcs.ICALP.2018.90,
  author =	{Nakos, Vasileios and Shi, Xiaofei and Woodruff, David P. and Zhang, Hongyang},
  title =	{{Improved Algorithms for Adaptive Compressed Sensing}},
  booktitle =	{45th International Colloquium on Automata, Languages, and Programming (ICALP 2018)},
  pages =	{90:1--90:14},
  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.2018.90},
  URN =		{urn:nbn:de:0030-drops-90945},
  doi =		{10.4230/LIPIcs.ICALP.2018.90},
  annote =	{Keywords: Compressed Sensing, Adaptivity, High-Dimensional Vectors}
}
Document
Matrix Completion and Related Problems via Strong Duality

Authors: Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, and Hongyang Zhang

Published in: LIPIcs, Volume 94, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018)


Abstract
This work studies the strong duality of non-convex matrix factorization problems: we show that under certain dual conditions, these problems and its dual have the same optimum. This has been well understood for convex optimization, but little was known for non-convex problems. We propose a novel analytical framework and show that under certain dual conditions, the optimal solution of the matrix factorization program is the same as its bi-dual and thus the global optimality of the non-convex program can be achieved by solving its bi-dual which is convex. These dual conditions are satisfied by a wide class of matrix factorization problems, although matrix factorization problems are hard to solve in full generality. This analytical framework may be of independent interest to non-convex optimization more broadly. We apply our framework to two prototypical matrix factorization problems: matrix completion and robust Principal Component Analysis (PCA). These are examples of efficiently recovering a hidden matrix given limited reliable observations of it. Our framework shows that exact recoverability and strong duality hold with nearly-optimal sample complexity guarantees for matrix completion and robust PCA.

Cite as

Maria-Florina Balcan, Yingyu Liang, David P. Woodruff, and Hongyang Zhang. Matrix Completion and Related Problems via Strong Duality. In 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 94, pp. 5:1-5:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


Copy BibTex To Clipboard

@InProceedings{balcan_et_al:LIPIcs.ITCS.2018.5,
  author =	{Balcan, Maria-Florina and Liang, Yingyu and Woodruff, David P. and Zhang, Hongyang},
  title =	{{Matrix Completion and Related Problems via Strong Duality}},
  booktitle =	{9th Innovations in Theoretical Computer Science Conference (ITCS 2018)},
  pages =	{5:1--5:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-060-6},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{94},
  editor =	{Karlin, Anna R.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2018.5},
  URN =		{urn:nbn:de:0030-drops-83583},
  doi =		{10.4230/LIPIcs.ITCS.2018.5},
  annote =	{Keywords: Non-Convex Optimization, Strong Duality, Matrix Completion, Robust PCA, Sample Complexity}
}
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