Rounding sum-of-squares relaxations
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Publication:5259536
DOI10.1145/2591796.2591886zbMath1315.90028arXiv1312.6652OpenAlexW2115318789MaRDI QIDQ5259536
Boaz Barak, Jonathan A. Kelner, David Steurer
Publication date: 26 June 2015
Published in: Proceedings of the forty-sixth annual ACM symposium on Theory of computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1312.6652
Analysis of algorithms and problem complexity (68Q25) Semidefinite programming (90C22) Approximation algorithms (68W25) Quantum algorithms and complexity in the theory of computing (68Q12)
Related Items (14)
Quantum de Finetti theorems under local measurements with applications ⋮ Sum-of-squares rank upper bounds for matching problems ⋮ Disordered systems insights on computational hardness ⋮ Noisy tensor completion via the sum-of-squares hierarchy ⋮ Unnamed Item ⋮ Notes on computational-to-statistical gaps: predictions using statistical physics ⋮ Limitations of semidefinite programs for separable states and entangled games ⋮ A Nearly Tight Sum-of-Squares Lower Bound for the Planted Clique Problem ⋮ Algorithmic and optimization aspects of Brascamp-Lieb inequalities, via operator scaling ⋮ Iterated linear optimization ⋮ Unnamed Item ⋮ Finding a low-rank basis in a matrix subspace ⋮ Unnamed Item ⋮ Sum-of-Squares Rank Upper Bounds for Matching Problems
Uses Software
Cites Work
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- Efficient algorithms for privately releasing marginals via convex relaxations
- The Geometry of Differential Privacy: The Small Database and Approximate Cases
- Faster Algorithms for Privately Releasing Marginals
- Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
- Privately Releasing Conjunctions and the Statistical Query Barrier
- On the geometry of differential privacy
- Interactive privacy via the median mechanism
- The price of privately releasing contingency tables and the spectra of random matrices with correlated rows
- Lower Bounds in Differential Privacy
- Iterative Constructions and Private Data Release
- Characterizing the sample complexity of private learners
- Faster private release of marginals on small databases
- Bounds on the Sample Complexity for Private Learning and Private Data Release
- Differential Privacy and the Fat-Shattering Dimension of Linear Queries
- Our Data, Ourselves: Privacy Via Distributed Noise Generation
- New Efficient Attacks on Statistical Disclosure Control Mechanisms
- Collusion-secure fingerprinting for digital data
- On the complexity of differentially private data release
- Advances in Cryptology – CRYPTO 2004
- Answering n {2+o(1)} counting queries with differential privacy is hard
- Theory of Cryptography
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