J
Jon Feldman
Researcher at Google
Publications - 71
Citations - 4752
Jon Feldman is an academic researcher from Google. The author has contributed to research in topics: Decoding methods & Linear code. The author has an hindex of 34, co-authored 71 publications receiving 4455 citations. Previous affiliations of Jon Feldman include Massachusetts Institute of Technology & Columbia University.
Papers
More filters
Journal ArticleDOI
Using linear programming to Decode Binary linear codes
TL;DR: The definition of a pseudocodeword unifies other such notions known for iterative algorithms, including "stopping sets," "irreducible closed walks," "trellis cycles," "deviation sets," and "graph covers," which is a lower bound on the classical distance.
Journal ArticleDOI
Growth codes: maximizing sensor network data persistence
TL;DR: This paper design and analyze techniques to increase "persistence" of sensed data, so that data is more likely to reach a data sink, even as network nodes fail, by replicating data compactly at neighboring nodes using novel "Growth Codes" that increase in efficiency as data accumulates at the sink.
Proceedings ArticleDOI
Online Stochastic Matching: Beating 1-1/e
TL;DR: In this article, the authors study the online stochastic bipartite matching problem, in a form motivated by display ad allocation on the Internet, and show that no online algorithm can achieve an approximation ratio better than 0.632.
Dissertation
Decoding error-correcting codes via linear programming
Jon Feldman,David R. Karger +1 more
TL;DR: This thesis investigates the application of linear programming (LP) relaxation to the problem of decoding an error-correcting code, and provides specific LP decoders for two major families of codes: turbo codes and low-density parity-check codes.
On the Capacity of Secure Network Coding
TL;DR: The problem of making a linear network code secure is equivalent to the problem of finding a linear code with certain generalized distance properties, and it is shown that if the authors give up a small amount of overall capacity, then a random code achieves these properties using a much smaller field size than the construction of Cai & Yeung.