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Interpreting Black Box Predictions using Fisher Kernels

Interpreting Black Box Predictions using Fisher Kernels

23 October 2018
Rajiv Khanna
Been Kim
Joydeep Ghosh
Oluwasanmi Koyejo
    FAtt
ArXivPDFHTML

Papers citing "Interpreting Black Box Predictions using Fisher Kernels"

16 / 16 papers shown
Title
Data Cleansing for GANs
Data Cleansing for GANs
Naoyuki Terashita
Hiroki Ohashi
Satoshi Hara
AAML
114
0
0
01 Apr 2025
Most Influential Subset Selection: Challenges, Promises, and Beyond
Most Influential Subset Selection: Challenges, Promises, and Beyond
Yuzheng Hu
Pingbang Hu
Han Zhao
Jiaqi W. Ma
TDI
157
4
0
10 Jan 2025
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Models
Jinxu Lin
Linwei Tao
Minjing Dong
Chang Xu
TDI
56
2
0
24 Oct 2024
Towards Deep Learning Models Resistant to Adversarial Attacks
Towards Deep Learning Models Resistant to Adversarial Attacks
Aleksander Madry
Aleksandar Makelov
Ludwig Schmidt
Dimitris Tsipras
Adrian Vladu
SILM
OOD
213
11,962
0
19 Jun 2017
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Percy Liang
TDI
134
2,854
0
14 Mar 2017
Restricted Strong Convexity Implies Weak Submodularity
Restricted Strong Convexity Implies Weak Submodularity
Ethan R. Elenberg
Rajiv Khanna
A. Dimakis
S. Negahban
30
159
0
02 Dec 2016
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning
Martín Abadi
P. Barham
Jianmin Chen
Zhiwen Chen
Andy Davis
...
Vijay Vasudevan
Pete Warden
Martin Wicke
Yuan Yu
Xiaoqiang Zhang
GNN
AI4CE
324
18,300
0
27 May 2016
Coresets for Scalable Bayesian Logistic Regression
Coresets for Scalable Bayesian Logistic Regression
Jonathan H. Huggins
Trevor Campbell
Tamara Broderick
36
217
0
20 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAtt
FaML
519
16,765
0
16 Feb 2016
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning
  and Prototype Classification
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
Been Kim
Cynthia Rudin
J. Shah
43
321
0
03 Mar 2015
Explaining and Harnessing Adversarial Examples
Explaining and Harnessing Adversarial Examples
Ian Goodfellow
Jonathon Shlens
Christian Szegedy
AAML
GAN
158
18,922
0
20 Dec 2014
Optimally-Weighted Herding is Bayesian Quadrature
Ferenc Huszár
David Duvenaud
40
107
0
09 Aug 2014
On the Equivalence between Herding and Conditional Gradient Algorithms
On the Equivalence between Herding and Conditional Gradient Algorithms
Francis R. Bach
Simon Lacoste-Julien
G. Obozinski
119
169
0
20 Mar 2012
Super-Samples from Kernel Herding
Super-Samples from Kernel Herding
Yutian Chen
Max Welling
Alex Smola
114
335
0
15 Mar 2012
Submodular meets Spectral: Greedy Algorithms for Subset Selection,
  Sparse Approximation and Dictionary Selection
Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection
Abhimanyu Das
David Kempe
124
485
0
19 Feb 2011
A Kernel Method for the Two-Sample Problem
A Kernel Method for the Two-Sample Problem
Arthur Gretton
Karsten Borgwardt
Malte J. Rasch
Bernhard Schölkopf
Alex Smola
158
2,348
0
15 May 2008
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