3 Search Results for "Jawale, Ruta"

Document
DOI: 10.4230/LIPIcs.ITCS.2026.69
Computing Equilibrium Points of Electrostatic Potentials

Authors: Abheek Ghosh, Paul W. Goldberg, and Alexandros Hollender

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

Abstract
We study the computation of equilibrium points of electrostatic potentials: locations in space where the electrostatic force arising from a collection of charged particles vanishes. This is a novel scenario of optimization in which solutions are guaranteed to exist due to a nonconstructive argument, but gradient descent is unreliable due to the presence of singularities. We present an algorithm based on piecewise approximation of the potential function by Taylor series. The main insight is to divide the domain into a grid with variable coarseness, where grid cells are exponentially smaller in regions where the function changes rapidly compared to regions where it changes slowly. Our algorithm finds approximate equilibrium points in time poly-logarithmic in the approximation parameter, but these points are not guaranteed to be close to exact solutions. Nevertheless, we show that such points can be computed efficiently under a mild assumption that we call "strong non-degeneracy". We complement these algorithmic results by studying a generalization of this problem and showing that it is CLS-hard and in PPAD, leaving its precise classification as an intriguing open problem.
Cite as

Abheek Ghosh, Paul W. Goldberg, and Alexandros Hollender. Computing Equilibrium Points of Electrostatic Potentials. In 17th Innovations in Theoretical Computer Science Conference (ITCS 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 362, pp. 69:1-69:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026)

Copy BibTex To Clipboard

@InProceedings{ghosh_et_al:LIPIcs.ITCS.2026.69,
  author =	{Ghosh, Abheek and Goldberg, Paul W. and Hollender, Alexandros},
  title =	{{Computing Equilibrium Points of Electrostatic Potentials}},
  booktitle =	{17th Innovations in Theoretical Computer Science Conference (ITCS 2026)},
  pages =	{69:1--69:22},
  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.2026.69},
  URN =		{urn:nbn:de:0030-drops-253566},
  doi =		{10.4230/LIPIcs.ITCS.2026.69},
  annote =	{Keywords: Total search problems, TFNP, PPAD, CLS, polynomial equations}
}
Document
Track A: Algorithms, Complexity and Games
DOI: 10.4230/LIPIcs.ICALP.2025.56
Boosting SNARKs and Rate-1 Barrier in Arguments of Knowledge

Authors: Jiaqi Cheng and Rishab Goyal

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

Abstract
We design a generic compiler to boost any non-trivial succinct non-interactive argument of knowledge (SNARK) to full succinctness. Our results come in two flavors: 1) For any constant ε > 0, any SNARK with proof size |π| < |ω|/(λ^ε) + poly(λ, |x|) can be upgraded to a fully succinct SNARK, where all system parameters (such as proof/CRS sizes and setup/verifier run-times) grow as fixed polynomials in λ, independent of witness size. 2) Under an additional assumption that the underlying SNARK has as an efficient knowledge extractor, we further improve our result to upgrade any non-trivial SNARK. For example, we show how to design fully succinct SNARKs from SNARKs with proofs of length |ω| - Ω(λ), or |ω|/(1+ε) + poly(λ, |x|), any constant ε > 0. Our result reduces the long-standing challenge of designing fully succinct SNARKs to designing arguments of knowledge that beat the trivial construction. It also establishes optimality of rate-1 arguments of knowledge (such as NIZKs [Gentry-Groth-Ishai-Peikert-Sahai-Smith; JoC'15] and BARGs [Devadas-Goyal-Kalai-Vaikuntanathan, Paneth-Pass; FOCS'22]), and suggests any further improvement is tantamount to designing fully succinct SNARKs, thus requires bypassing established black-box barriers [Gentry-Wichs; STOC'11].
Cite as

Jiaqi Cheng and Rishab Goyal. Boosting SNARKs and Rate-1 Barrier in Arguments of Knowledge. In 52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 334, pp. 56:1-56:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{cheng_et_al:LIPIcs.ICALP.2025.56,
  author =	{Cheng, Jiaqi and Goyal, Rishab},
  title =	{{Boosting SNARKs and Rate-1 Barrier in Arguments of Knowledge}},
  booktitle =	{52nd International Colloquium on Automata, Languages, and Programming (ICALP 2025)},
  pages =	{56:1--56:20},
  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.2025.56},
  URN =		{urn:nbn:de:0030-drops-234339},
  doi =		{10.4230/LIPIcs.ICALP.2025.56},
  annote =	{Keywords: SNARGs, RAM Delegation}
}
Document
DOI: 10.4230/LIPIcs.ITC.2023.14
Locally Covert Learning

Authors: Justin Holmgren and Ruta Jawale

Published in: LIPIcs, Volume 267, 4th Conference on Information-Theoretic Cryptography (ITC 2023)

Abstract
The goal of a covert learning algorithm is to learn a function f by querying it, while ensuring that an adversary, who sees all queries and their responses, is unable to (efficiently) learn any more about f than they could learn from random input-output pairs. We focus on a relaxation that we call local covertness, in which queries are distributed across k servers and we only limit what is learnable by k - 1 colluding servers. For any constant k, we give a locally covert algorithm for efficiently learning any Fourier-sparse function (technically, our notion of learning is improper, agnostic, and with respect to the uniform distribution). Our result holds unconditionally and for computationally unbounded adversaries. Prior to our work, such an algorithm was known only for the special case of O(log n)-juntas, and only with k = 2 servers [Yuval Ishai et al., 2019]. Our main technical observation is that the original Goldreich-Levin algorithm only utilizes i.i.d. pairs of correlated queries, where each half of every pair is uniformly random. We give a simple generalization of this algorithm in which pairs are replaced by k-tuples in which any k - 1 components are jointly uniform. The cost of this generalization is that the number of queries needed grows exponentially with k.
Cite as

Justin Holmgren and Ruta Jawale. Locally Covert Learning. In 4th Conference on Information-Theoretic Cryptography (ITC 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 267, pp. 14:1-14:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

Copy BibTex To Clipboard

@InProceedings{holmgren_et_al:LIPIcs.ITC.2023.14,
  author =	{Holmgren, Justin and Jawale, Ruta},
  title =	{{Locally Covert Learning}},
  booktitle =	{4th Conference on Information-Theoretic Cryptography (ITC 2023)},
  pages =	{14:1--14:12},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-271-6},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{267},
  editor =	{Chung, Kai-Min},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITC.2023.14},
  URN =		{urn:nbn:de:0030-drops-183421},
  doi =		{10.4230/LIPIcs.ITC.2023.14},
  annote =	{Keywords: learning theory, adversarial machine learning, zero knowledge, Fourier analysis of boolean functions, Goldreich-Levin algorithm, Kushilevitz-Mansour algorithm}
}
  • Refine by Type
  • 3 Document/PDF
  • 2 Document/HTML

  • Refine by Publication Year
  • 1 2026
  • 1 2025
  • 1 2023

  • Refine by Author
  • 1 Cheng, Jiaqi
  • 1 Ghosh, Abheek
  • 1 Goldberg, Paul W.
  • 1 Goyal, Rishab
  • 1 Hollender, Alexandros
  • Show More...

  • Refine by Series/Journal
  • 3 LIPIcs

  • Refine by Classification
  • 1 Computing methodologies → Equation and inequality solving algorithms
  • 1 Security and privacy → Information-theoretic techniques
  • 1 Theory of computation → Computational complexity and cryptography
  • 1 Theory of computation → Continuous optimization
  • 1 Theory of computation → Machine learning theory
  • Show More...

  • Refine by Keyword
  • 1 CLS
  • 1 Fourier analysis of boolean functions
  • 1 Goldreich-Levin algorithm
  • 1 Kushilevitz-Mansour algorithm
  • 1 PPAD
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact