A study of local approximations in information theory

A study of local approximations in information theory

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.

Bibliographic Details
Main Author:	Makur, Anuran
Other Authors:	Lizhong Zheng.
Format:	Thesis
Language:	eng
Published:	Massachusetts Institute of Technology 2015
Subjects:	Electrical Engineering and Computer Science.
Online Access:	http://hdl.handle.net/1721.1/99789

Similar Items

Information contraction and decomposition
by: Makur, Anuran.
Published: (2019)

Local to global geometric methods in information theory
by: Abbe, Emmanuel Auguste
Published: (2009)

Stochastic realization theory for exact and approximate multiscale models
by: Tucker, Dewey S. (Dewey Stanton)
Published: (2006)

Comparison of channels: criteria for domination by a symmetric channel
by: Makur, Anuran, et al.
Published: (2020)

Polynomial Singular Value Decompositions of a Family of Source-Channel Models
by: Makur, Anuran, et al.
Published: (2021)

Representation and transfer learning using information-theoretic approximations
by: Qiu, David.
Published: (2020)

Less noisy domination by symmetric channels
by: Makur, Anuran, et al.
Published: (2019)

Bounds between contraction coefficients
by: Makur, Anuran, et al.
Published: (2017)

Local approximations of deep learning models for black-box adversarial attacks
by: Sun, Michael(Michael Z.)
Published: (2019)

Anytime information theory
by: Sahai, Anant
Published: (2005)

An efficient algorithm for information decomposition and extraction
by: Makur, Anuran, et al.
Published: (2018)

Euclidean network information theory
by: Huang, Shao-Lun, Ph. D. Massachusetts Institute of Technology
Published: (2014)

Informative sensing : theory and applications
by: Chang, Hyun Sung
Published: (2012)

Distributed coordination in network information theory
by: Schein, Brett E. (Brett Eric)
Published: (2005)

Broadcasting on Random Directed Acyclic Graphs
by: Makur, Anuran, et al.
Published: (2021)

Broadcasting on Random Networks
by: Makur, Anuran, et al.
Published: (2021)

Broadcasting on Random Networks
by: Makur, Anuran, et al.
Published: (2021)

Reconstruction on 2D Regular Grids
by: Makur, Anuran, et al.
Published: (2022)

Probabilistic Clustering using Maximal Matrix Norm Couplings
by: Qiu, David, et al.
Published: (2021)

On approximating projection games
by: Manurangsi, Pasin
Published: (2016)

A family of algorithms for approximate Bayesian inference
by: Minka, Thomas Peter
Published: (2014)

Analysis of approximation and uncertainty in optimization
by: Mastin, Dana Andrew
Published: (2015)

Approximate string joins with abbreviations
by: Tao, Wenbo, Ph. D. Massachusetts Institute of Technology
Published: (2018)

Mechanism design with approximate types
by: Zhu, Zeyuan Allen
Published: (2012)

Approximating the maximum acyclic subgraph
by: Newman, Alantha
Published: (2014)

Locally Decodable Codes and Private Information Retrieval Schemes
by: Yekhanin, Sergey
Published: (2008)

Energy-efficient approximate computation in Topaz
by: Achour, Sara
Published: (2015)

Logical reasoning for approximate and unreliable computation
by: Carbin, Michael (Michael James)
Published: (2015)

Fast approximate hierarchical solution of MDPs
by: Barry, Jennifer L. (Jennifer Lynn)
Published: (2010)

Approximation algorithms for disjoint paths problems
by: Kleinberg, Jon M
Published: (2005)

Signal approximation using the bilinear transform
by: Venkataraman, Archana, Ph. D. Massachusetts Institute of Technology
Published: (2009)

Accuracy-aware optimization of approximate programs
by: Misailović, Saša
Published: (2016)

Approximation algorithms for stochastic scheduling problems
by: Dean, Brian C. (Brian Christopher), 1975-
Published: (2006)

Realization and approximation of stationary stochastic processes
by: Avniel, Yehuda
Published: (2005)

Coherent approximation of distributed expert assessments
by: Jones, Peter B., Ph.D. Massachusetts Institute of Technology
Published: (2011)

Perturbation stability for approximate MAP inference
by: Lang, Hunter(Hunter J.)
Published: (2019)

Approximate inference in Gaussian graphical models
by: Malioutov, Dmitry M., 1981-
Published: (2009)

Training hierarchical networks for function approximation
by: Miranda, Brando, M. Eng. Massachusetts Institute of Technology
Published: (2018)

Multiple-user quantum information theory for optical communication channels
by: Guha, Saikat, 1980-
Published: (2009)

Approximator : a software tool for automatic generation of approximate arithmetic circuits
by: Balasubramanian, Padmanabhan, et al.
Published: (2022)