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Influence Functions for Machine Learning: Nonparametric Estimators for
  Entropies, Divergences and Mutual Informations
v1v2v3 (latest)

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations

17 November 2014
Kirthevasan Kandasamy
A. Krishnamurthy
Barnabás Póczós
Larry A. Wasserman
J. M. Robins
    TDI
ArXiv (abs)PDFHTML

Papers citing "Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations"

5 / 5 papers shown
Title
The Fundamental Limits of Structure-Agnostic Functional Estimation
The Fundamental Limits of Structure-Agnostic Functional Estimation
Sivaraman Balakrishnan
Edward H. Kennedy
Larry A. Wasserman
70
11
0
06 May 2023
Visually Communicating and Teaching Intuition for Influence Functions
Visually Communicating and Teaching Intuition for Influence Functions
Aaron Fisher
Edward H. Kennedy
211
55
0
08 Oct 2018
Independence clustering (without a matrix)
Independence clustering (without a matrix)
D. Ryabko
83
6
0
20 Mar 2017
Locally Robust Semiparametric Estimation
Locally Robust Semiparametric Estimation
Victor Chernozhukov
J. Escanciano
Hidehiko Ichimura
Whitney Newey
J. M. Robins
138
210
0
29 Jul 2016
Specific Differential Entropy Rate Estimation for Continuous-Valued Time
  Series
Specific Differential Entropy Rate Estimation for Continuous-Valued Time Series
David M. Darmon
49
20
0
08 Jun 2016
1