Entropy, optimization and counting
From MaRDI portal
Publication:5259538
DOI10.1145/2591796.2591803zbMath1315.94027arXiv1304.8108OpenAlexW2015604011MaRDI QIDQ5259538
Mohit Singh, Nisheeth K. Vishnoi
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/1304.8108
Analysis of algorithms and problem complexity (68Q25) Convex programming (90C25) Measures of information, entropy (94A17) Approximation algorithms (68W25)
Related Items (13)
Maximum entropy and integer partitions ⋮ Generalized maximum entropy estimation ⋮ On the Computability of Continuous Maximum Entropy Distributions with Applications ⋮ Random Walks in Polytopes and Negative Dependence ⋮ Computational implications of reducing data to sufficient statistics ⋮ Efficiently list‐edge coloring multigraphs asymptotically optimally ⋮ Unnamed Item ⋮ Combinatorial Bernoulli factories ⋮ Unnamed Item ⋮ Log-concave polynomials. I: Entropy and a deterministic approximation algorithm for counting bases of matroids ⋮ On a probabilistic approach to synthesize control policies from example datasets ⋮ Contention resolution, matrix scaling and fair allocation ⋮ Isolating a Vertex via Lattices: Polytopes with Totally Unimodular Faces
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- 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
This page was built for publication: Entropy, optimization and counting