10 Search Results for "McCauley, Samuel"

Document
DOI: 10.4230/LIPIcs.WADS.2025.47
B-Treaps Revised: Write Efficient Randomized Block Search Trees with High Load

Authors: Roodabeh Safavi and Martin P. Seybold

Published in: LIPIcs, Volume 349, 19th International Symposium on Algorithms and Data Structures (WADS 2025)

Abstract
Uniquely represented (UR) data structures represent each logical state with a unique storage state. We study the problem of maintaining a dynamic set of n keys from a totally ordered universe in this context. UR structures are also called "strongly history independent" structures in the literature. We introduce a two-layer data structure called (α,ε)-Randomized Block Search Tree (RBST) that is uniquely represented and suitable for external memory (EM). Though RBSTs naturally generalize the well-known binary Treaps, several new ideas are needed to analyze the expected search, update, and storage efficiency in terms of block-reads, block-writes, and blocks stored. We prove that searches have O(ε^{-1} + log_α n) block-reads, that dynamic updates perform O(ε^{-1} + log_α(n)/α) block-writes and O(ε^{-2}+(1+(ε^{-1}+log n)/α)log_α n) block-reads, and that (α, ε)-RBSTs have an asymptotic load-factor of at least (1-ε) for every ε ∈ (0,1/2]. Thus (α, ε)-RBSTs improve on the known, uniquely represented B-Treap [Golovin; ICALP'09]. Compared with non-UR structures, the RBST is also, to the best of our knowledge, the first external memory structure that is storage-efficient and has a non-amortized, write-efficient update bound.
Cite as

Roodabeh Safavi and Martin P. Seybold. B-Treaps Revised: Write Efficient Randomized Block Search Trees with High Load. In 19th International Symposium on Algorithms and Data Structures (WADS 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 349, pp. 47:1-47:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{safavi_et_al:LIPIcs.WADS.2025.47,
  author =	{Safavi, Roodabeh and Seybold, Martin P.},
  title =	{{B-Treaps Revised: Write Efficient Randomized Block Search Trees with High Load}},
  booktitle =	{19th International Symposium on Algorithms and Data Structures (WADS 2025)},
  pages =	{47:1--47:23},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-398-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{349},
  editor =	{Morin, Pat and Oh, Eunjin},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WADS.2025.47},
  URN =		{urn:nbn:de:0030-drops-242786},
  doi =		{10.4230/LIPIcs.WADS.2025.47},
  annote =	{Keywords: Unique Representation, Randomization, Top-Down Analysis, Block Search Tree, Write-Efficiency, Storage-Efficiency}
}
Document
DOI: 10.4230/LIPIcs.SEA.2025.23
Elias-Fano Compression for Space-Efficient Rank and Select Structures

Authors: Lannie Dalton Hough and Abhinav Bhatele

Published in: LIPIcs, Volume 338, 23rd International Symposium on Experimental Algorithms (SEA 2025)

Abstract
Bit vectors are an important component in many data structures. Such data structures are used in a variety of applications and domains including databases, search engines, and computational biology. Many use cases depend on being able to perform rank and/or select queries on the bit vector. No existing rank and select structure enabling these queries is most efficient both for space and for time; there is a tradeoff between the two. In practice, the smallest rank and select data structures, cs-poppy and pasta-flat, impose a space overhead of 3.51%, or 3.125% if only rank needs to be supported. In this paper, we present a new data structure, orzo, which reduces the overhead of the rank component by a further 26.5%. We preserve desirable cache-centric design decisions made in prior work, which allows us to minimize the performance penalty of creating a smaller data structure.
Cite as

Lannie Dalton Hough and Abhinav Bhatele. Elias-Fano Compression for Space-Efficient Rank and Select Structures. In 23rd International Symposium on Experimental Algorithms (SEA 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 338, pp. 23:1-23:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{hough_et_al:LIPIcs.SEA.2025.23,
  author =	{Hough, Lannie Dalton and Bhatele, Abhinav},
  title =	{{Elias-Fano Compression for Space-Efficient Rank and Select Structures}},
  booktitle =	{23rd International Symposium on Experimental Algorithms (SEA 2025)},
  pages =	{23:1--23:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-375-1},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{338},
  editor =	{Mutzel, Petra and Prezza, Nicola},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2025.23},
  URN =		{urn:nbn:de:0030-drops-232617},
  doi =		{10.4230/LIPIcs.SEA.2025.23},
  annote =	{Keywords: rank and select, cache-aware, succinct data structures, bit vector}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.117
Incremental Approximate Single-Source Shortest Paths with Predictions

Authors: Samuel McCauley, Benjamin Moseley, Aidin Niaparast, Helia Niaparast, and Shikha Singh

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
The algorithms-with-predictions framework has been used extensively to develop online algorithms with improved beyond-worst-case competitive ratios. Recently, there is growing interest in leveraging predictions for designing data structures with improved beyond-worst-case running times. In this paper, we study the fundamental data structure problem of maintaining approximate shortest paths in incremental graphs in the algorithms-with-predictions model. Given a sequence σ of edges that are inserted one at a time, the goal is to maintain approximate shortest paths from the source to each vertex in the graph at each time step. Before any edges arrive, the data structure is given a prediction of the online edge sequence σ̂ which is used to "warm start" its state. As our main result, we design a learned algorithm that maintains (1+ε)-approximate single-source shortest paths, which runs in Õ(m η log W/ε) time, where W is the weight of the heaviest edge and η is the prediction error. We show these techniques immediately extend to the all-pairs shortest-path setting as well. Our algorithms are consistent (performing nearly as fast as the offline algorithm) when predictions are nearly perfect, have a smooth degradation in performance with respect to the prediction error and, in the worst case, match the best offline algorithm up to logarithmic factors. That is, the algorithms are "ideal" in the algorithms-with-predictions model. As a building block, we study the offline incremental approximate single-source shortest-path (SSSP) problem. In the offline incremental SSSP problem, the edge sequence σ is known a priori and the goal is to construct a data structure that can efficiently return the length of the shortest paths in the intermediate graph G_t consisting of the first t edges, for all t. Note that the offline incremental problem is defined in the worst-case setting (without predictions) and is of independent interest.
Cite as

Samuel McCauley, Benjamin Moseley, Aidin Niaparast, Helia Niaparast, and Shikha Singh. Incremental Approximate Single-Source Shortest Paths with Predictions. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 117:1-117:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{mccauley_et_al:LIPIcs.ICALP.2025.117,
  author =	{McCauley, Samuel and Moseley, Benjamin and Niaparast, Aidin and Niaparast, Helia and Singh, Shikha},
  title =	{{Incremental Approximate Single-Source Shortest Paths with Predictions}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{117:1--117:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.117},
  URN =		{urn:nbn:de:0030-drops-234946},
  doi =		{10.4230/LIPIcs.ICALP.2025.117},
  annote =	{Keywords: Algorithms with Predictions, Shortest Paths, Approximation Algorithms, Dynamic Graph Algorithms}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.93
On Incremental Approximate Shortest Paths in Directed Graphs

Authors: Adam Górkiewicz and Adam Karczmarz

Published in: LIPIcs, Volume 334, 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)

Abstract
In this paper, we show new data structures maintaining approximate shortest paths in sparse directed graphs with polynomially bounded non-negative edge weights under edge insertions. We give more efficient incremental (1+ε)-approximate APSP data structures that work against an adaptive adversary: a deterministic one with Õ(m^{3/2}n^{3/4}) total update time and a randomized one with Õ(m^{4/3}n^{5/6}) total update time. For sparse graphs, these both improve polynomially upon the best-known bound against an adaptive adversary [Karczmarz and Łącki, ESA 2019]. To achieve that, building on the ideas of [Chechik and Zhang, SODA 2021] and [Kyng, Meierhans and Probst Gutenberg, SODA 2022], we show a near-optimal (1+ε)-approximate incremental SSSP data structure for a special case when all edge updates are adjacent to the source, that might be of independent interest. We also describe a very simple and near-optimal offline incremental (1+ε)-approximate SSSP data structure. While online near-linear partially dynamic SSSP data structures have been elusive so far (except for dense instances), our result excludes using certain types of impossibility arguments to rule them out. Additionally, our offline solution leads to near-optimal and deterministic all-pairs bounded-leg shortest paths data structure for sparse graphs.
Cite as

Adam Górkiewicz and Adam Karczmarz. On Incremental Approximate Shortest Paths in Directed Graphs. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 93:1-93:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{gorkiewicz_et_al:LIPIcs.ICALP.2025.93,
  author =	{G\'{o}rkiewicz, Adam and Karczmarz, Adam},
  title =	{{On Incremental Approximate Shortest Paths in Directed Graphs}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{93:1--93:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-372-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{334},
  editor =	{Censor-Hillel, Keren and Grandoni, Fabrizio and Ouaknine, Jo\"{e}l and Puppis, Gabriele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICALP.2025.93},
  URN =		{urn:nbn:de:0030-drops-234700},
  doi =		{10.4230/LIPIcs.ICALP.2025.93},
  annote =	{Keywords: dynamic shortest paths, incremental shortest paths, offline dynamic algorithms}
}
Document
DOI: 10.4230/LIPIcs.ESA.2024.88
Improved Space-Efficient Approximate Nearest Neighbor Search Using Function Inversion

Authors: Samuel McCauley

Published in: LIPIcs, Volume 308, 32nd Annual European Symposium on Algorithms (ESA 2024)

Abstract
Approximate nearest neighbor search (ANN) data structures have widespread applications in machine learning, computational biology, and text processing. The goal of ANN is to preprocess a set S so that, given a query q, we can find a point y whose distance from q approximates the smallest distance from q to any point in S. For most distance functions, the best-known ANN bounds for high-dimensional point sets are obtained using techniques based on locality-sensitive hashing (LSH). Unfortunately, space efficiency is a major challenge for LSH-based data structures. Classic LSH techniques require a very large amount of space, oftentimes polynomial in |S|. A long line of work has developed intricate techniques to reduce this space usage, but these techniques suffer from downsides: they must be hand tailored to each specific LSH, are often complicated, and their space reduction comes at the cost of significantly increased query times. In this paper we explore a new way to improve the space efficiency of LSH using function inversion techniques, originally developed in (Fiat and Naor 2000). We begin by describing how function inversion can be used to improve LSH data structures. This gives a fairly simple, black box method to reduce LSH space usage. Then, we give a data structure that leverages function inversion to improve the query time of the best known near-linear space data structure for approximate nearest neighbor search under Euclidean distance: the ALRW data structure of (Andoni, Laarhoven, Razenshteyn, and Waingarten 2017). ALRW was previously shown to be optimal among "list-of-points" data structures for both Euclidean and Manhattan ANN; thus, in addition to giving improved bounds, our results imply that list-of-points data structures are not optimal for Euclidean or Manhattan ANN .
Cite as

Samuel McCauley. Improved Space-Efficient Approximate Nearest Neighbor Search Using Function Inversion. In 32nd Annual European Symposium on Algorithms (ESA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 308, pp. 88:1-88:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{mccauley:LIPIcs.ESA.2024.88,
  author =	{McCauley, Samuel},
  title =	{{Improved Space-Efficient Approximate Nearest Neighbor Search Using Function Inversion}},
  booktitle =	{32nd Annual European Symposium on Algorithms (ESA 2024)},
  pages =	{88:1--88:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-338-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{308},
  editor =	{Chan, Timothy and Fischer, Johannes and Iacono, John and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2024.88},
  URN =		{urn:nbn:de:0030-drops-211590},
  doi =		{10.4230/LIPIcs.ESA.2024.88},
  annote =	{Keywords: similarity search, locality-sensitive hashing, randomized algorithms, data structures, space efficiency, function inversion}
}
Document
DOI: 10.4230/LIPIcs.SEA.2024.21
SPIDER: Improved Succinct Rank and Select Performance

Authors: Matthew D. Laws, Jocelyn Bliven, Kit Conklin, Elyes Laalai, Samuel McCauley, and Zach S. Sturdevant

Published in: LIPIcs, Volume 301, 22nd International Symposium on Experimental Algorithms (SEA 2024)

Abstract
Rank and select data structures seek to preprocess a bit vector to quickly answer two kinds of queries: Rank(i) gives the number of 1 bits in slots 0 through i, and Select(j) gives the first slot s with Rank(s) = j. A succinct data structure can answer these queries while using space much smaller than the size of the original bit vector. State of the art succinct rank and select data structures use as little as 4% extra space (over the underlying bit vector) while answering rank and select queries very quickly. Rank queries can be answered using only a handful of array accesses. Select queries can be answered by starting with similar array accesses, followed by a linear scan through the bit vector. Nonetheless, a tradeoff remains: data structures that use under 4% space are significantly slower at answering rank and select queries than less-space-efficient data structures (using, say, over 20% extra space). In this paper we make significantly progress towards closing this gap. We give a new data structure, SPIDER, which uses 3.82% extra space. SPIDER gives the best known rank query time for data sets of 8 billion or more bits, even compared to much less space-efficient data structures. For select queries, SPIDER outperforms all data structures that use less than 4% space, and significantly closes the gap in select performance between data structures with less than 4% space, and those that use more (over 20% for both rank and select) space. SPIDER makes two main technical contributions. For rank queries, it improves performance by interleaving the metadata with the bit vector to improve cache efficiency. For select queries, it uses predictions to almost eliminate the cost of the linear scan. These predictions are inspired by recent results on data structures with machine-learned predictions, adapted to the succinct data structure setting. Our results hold on both real and synthetic data, showing that these predictions are effective in practice.
Cite as

Matthew D. Laws, Jocelyn Bliven, Kit Conklin, Elyes Laalai, Samuel McCauley, and Zach S. Sturdevant. SPIDER: Improved Succinct Rank and Select Performance. In 22nd International Symposium on Experimental Algorithms (SEA 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 301, pp. 21:1-21:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{laws_et_al:LIPIcs.SEA.2024.21,
  author =	{Laws, Matthew D. and Bliven, Jocelyn and Conklin, Kit and Laalai, Elyes and McCauley, Samuel and Sturdevant, Zach S.},
  title =	{{SPIDER: Improved Succinct Rank and Select Performance}},
  booktitle =	{22nd International Symposium on Experimental Algorithms (SEA 2024)},
  pages =	{21:1--21:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-325-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{301},
  editor =	{Liberti, Leo},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2024.21},
  URN =		{urn:nbn:de:0030-drops-203865},
  doi =		{10.4230/LIPIcs.SEA.2024.21},
  annote =	{Keywords: Rank and Select, Succinct Data Structures, Data Structres, Cache Performance, Predictions}
}
Document
DOI: 10.4230/LIPIcs.ESA.2021.60
Telescoping Filter: A Practical Adaptive Filter

Authors: David J. Lee, Samuel McCauley, Shikha Singh, and Max Stein

Published in: LIPIcs, Volume 204, 29th Annual European Symposium on Algorithms (ESA 2021)

Abstract
Filters are small, fast, and approximate set membership data structures. They are often used to filter out expensive accesses to a remote set S for negative queries (that is, filtering out queries x ∉ S). Filters have one-sided errors: on a negative query, a filter may say "present" with a tunable false-positive probability of ε. Correctness is traded for space: filters only use log (1/ε) + O(1) bits per element. The false-positive guarantees of most filters, however, hold only for a single query. In particular, if x is a false positive, a subsequent query to x is a false positive with probability 1, not ε. With this in mind, recent work has introduced the notion of an adaptive filter. A filter is adaptive if each query is a false positive with probability ε, regardless of answers to previous queries. This requires "fixing" false positives as they occur. Adaptive filters not only provide strong false positive guarantees in adversarial environments but also improve query performance on practical workloads by eliminating repeated false positives. Existing work on adaptive filters falls into two categories. On the one hand, there are practical filters, based on the cuckoo filter, that attempt to fix false positives heuristically without meeting the adaptivity guarantee. On the other hand, the broom filter is a very complex adaptive filter that meets the optimal theoretical bounds. In this paper, we bridge this gap by designing the telescoping adaptive filter (TAF), a practical, provably adaptive filter. We provide theoretical false-positive and space guarantees for our filter, along with empirical results where we compare its performance against state-of-the-art filters. We also implement the broom filter and compare it to the TAF. Our experiments show that theoretical adaptivity can lead to improved false-positive performance on practical inputs, and can be achieved while maintaining throughput that is similar to non-adaptive filters.
Cite as

David J. Lee, Samuel McCauley, Shikha Singh, and Max Stein. Telescoping Filter: A Practical Adaptive Filter. In 29th Annual European Symposium on Algorithms (ESA 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 204, pp. 60:1-60:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{lee_et_al:LIPIcs.ESA.2021.60,
  author =	{Lee, David J. and McCauley, Samuel and Singh, Shikha and Stein, Max},
  title =	{{Telescoping Filter: A Practical Adaptive Filter}},
  booktitle =	{29th Annual European Symposium on Algorithms (ESA 2021)},
  pages =	{60:1--60:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-204-4},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{204},
  editor =	{Mutzel, Petra and Pagh, Rasmus and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2021.60},
  URN =		{urn:nbn:de:0030-drops-146410},
  doi =		{10.4230/LIPIcs.ESA.2021.60},
  annote =	{Keywords: Filters, approximate-membership query data structures (AMQs), Bloom filters, quotient filters, cuckoo filters, adaptivity, succinct data structures}
}
Document
DOI: 10.4230/LIPIcs.ICDT.2021.21
Approximate Similarity Search Under Edit Distance Using Locality-Sensitive Hashing

Authors: Samuel McCauley

Published in: LIPIcs, Volume 186, 24th International Conference on Database Theory (ICDT 2021)

Abstract
Edit distance similarity search, also called approximate pattern matching, is a fundamental problem with widespread database applications. The goal of the problem is to preprocess n strings of length d, to quickly answer queries q of the form: if there is a database string within edit distance r of q, return a database string within edit distance cr of q. Previous approaches to this problem either rely on very large (superconstant) approximation ratios c, or very small search radii r. Outside of a narrow parameter range, these solutions are not competitive with trivially searching through all n strings. In this work we give a simple and easy-to-implement hash function that can quickly answer queries for a wide range of parameters. Specifically, our strategy can answer queries in time Õ(d3^rn^{1/c}). The best known practical results require c ≫ r to achieve any correctness guarantee; meanwhile, the best known theoretical results are very involved and difficult to implement, and require query time that can be loosely bounded below by 24^r. Our results significantly broaden the range of parameters for which there exist nontrivial theoretical bounds, while retaining the practicality of a locality-sensitive hash function.
Cite as

Samuel McCauley. Approximate Similarity Search Under Edit Distance Using Locality-Sensitive Hashing. In 24th International Conference on Database Theory (ICDT 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 186, pp. 21:1-21:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{mccauley:LIPIcs.ICDT.2021.21,
  author =	{McCauley, Samuel},
  title =	{{Approximate Similarity Search Under Edit Distance Using Locality-Sensitive Hashing}},
  booktitle =	{24th International Conference on Database Theory (ICDT 2021)},
  pages =	{21:1--21:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-179-5},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{186},
  editor =	{Yi, Ke and Wei, Zhewei},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICDT.2021.21},
  URN =		{urn:nbn:de:0030-drops-137299},
  doi =		{10.4230/LIPIcs.ICDT.2021.21},
  annote =	{Keywords: edit distance, approximate pattern matching, approximate nearest neighbor, similarity search, locality-sensitive hashing}
}
Document
DOI: 10.4230/LIPIcs.ESA.2019.29
Non-Cooperative Rational Interactive Proofs

Authors: Jing Chen, Samuel McCauley, and Shikha Singh

Published in: LIPIcs, Volume 144, 27th Annual European Symposium on Algorithms (ESA 2019)

Abstract
Interactive-proof games model the scenario where an honest party interacts with powerful but strategic provers, to elicit from them the correct answer to a computational question. Interactive proofs are increasingly used as a framework to design protocols for computation outsourcing. Existing interactive-proof games largely fall into two categories: either as games of cooperation such as multi-prover interactive proofs and cooperative rational proofs, where the provers work together as a team; or as games of conflict such as refereed games, where the provers directly compete with each other in a zero-sum game. Neither of these extremes truly capture the strategic nature of service providers in outsourcing applications. How to design and analyze non-cooperative interactive proofs is an important open problem. In this paper, we introduce a mechanism-design approach to define a multi-prover interactive-proof model in which the provers are rational and non-cooperative - they act to maximize their expected utility given others' strategies. We define a strong notion of backwards induction as our solution concept to analyze the resulting extensive-form game with imperfect information. We fully characterize the complexity of our proof system under different utility gap guarantees. (At a high level, a utility gap of u means that the protocol is robust against provers that may not care about a utility loss of 1/u.) We show, for example, that the power of non-cooperative rational interactive proofs with a polynomial utility gap is exactly equal to the complexity class P^{NEXP}.
Cite as

Jing Chen, Samuel McCauley, and Shikha Singh. Non-Cooperative Rational Interactive Proofs. In 27th Annual European Symposium on Algorithms (ESA 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 144, pp. 29:1-29:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{chen_et_al:LIPIcs.ESA.2019.29,
  author =	{Chen, Jing and McCauley, Samuel and Singh, Shikha},
  title =	{{Non-Cooperative Rational Interactive Proofs}},
  booktitle =	{27th Annual European Symposium on Algorithms (ESA 2019)},
  pages =	{29:1--29:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-124-5},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{144},
  editor =	{Bender, Michael A. and Svensson, Ola and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ESA.2019.29},
  URN =		{urn:nbn:de:0030-drops-111508},
  doi =		{10.4230/LIPIcs.ESA.2019.29},
  annote =	{Keywords: non-cooperative game theory, extensive-form games with imperfect information, refined sequential equilibrium, rational proofs, interactive proofs}
}
Document
DOI: 10.4230/LIPIcs.FUN.2016.7
Resource Optimization for Program Committee Members: A Subreview Article

Authors: Michael A. Bender, Samuel McCauley, Bertrand Simon, Shikha Singh, and Frédéric Vivien

Published in: LIPIcs, Volume 49, 8th International Conference on Fun with Algorithms (FUN 2016)

Abstract
This paper formalizes a resource-allocation problem that is all too familiar to the seasoned program-committee member. For each submission j that the PC member has the honor of reviewing, there is a choice. The PC member can spend the time to review submission j in detail on his/her own at a cost of C_i. Alternatively, the PC member can spend the time to identify and contact peers, hoping to recruit them as subreviewers, at a cost of 1 per subreviewer. These potential subreviewers have a certain probability of rejecting each review request, and this probability increases as time goes on. Once the PC member runs out of time or unasked experts, he/she is forced to review the paper without outside assistance. This paper gives optimal solutions to several variations of the scheduling-reviewers problem. Most of the solutions from this paper are based on an iterated log function of C_i. In particular, with k rounds, the optimal solution sends the k-iterated log of C_i requests in the first round, the (k-1)-iterated log in the second round, and so forth. One of the contributions of this paper is solving this problem exactly, even when rejection probabilities may increase. Naturally, PC members must make an integral number of subreview requests. This paper gives, as an intermediate result, a linear-time algorithm to transform the artificial problem in which one can send fractional requests into the less-artificial problem in which one sends an integral number of requests. Finally, this paper considers the case where the PC member knows nothing about the probability that a potential subreviewer agrees to review the paper. This paper gives an approximation algorithm for this case, whose bounds improve as the number of rounds increases.
Cite as

Michael A. Bender, Samuel McCauley, Bertrand Simon, Shikha Singh, and Frédéric Vivien. Resource Optimization for Program Committee Members: A Subreview Article. In 8th International Conference on Fun with Algorithms (FUN 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 49, pp. 7:1-7:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

Copy BibTex To Clipboard

@InProceedings{bender_et_al:LIPIcs.FUN.2016.7,
  author =	{Bender, Michael A. and McCauley, Samuel and Simon, Bertrand and Singh, Shikha and Vivien, Fr\'{e}d\'{e}ric},
  title =	{{Resource Optimization for Program Committee Members: A Subreview Article}},
  booktitle =	{8th International Conference on Fun with Algorithms (FUN 2016)},
  pages =	{7:1--7:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-005-7},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{49},
  editor =	{Demaine, Erik D. and Grandoni, Fabrizio},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FUN.2016.7},
  URN =		{urn:nbn:de:0030-drops-58872},
  doi =		{10.4230/LIPIcs.FUN.2016.7},
  annote =	{Keywords: Scheduling, Delegation, Subreviews}
}
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