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On Second-Order Group Influence Functions for Black-Box Predictions
[article]
2020
arXiv
pre-print
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In this paper, we address this issue and propose second-order influence functions for identifying influential groups in test-time predictions. ...
We also show that second-order influence functions could be used with optimization techniques to improve the selection of the most influential group for a test-sample. ...
Acknowledgements This project was supported in part by NSF CAREER AWARD 1942230, HR001119S0026-GARD-FP-052, AWS Machine Learning Research Award, a sponsorship from Capital One, and Simons Fellowship on ...
arXiv:1911.00418v2
fatcat:bhbwvdqemnbtnb76ek2jtexpna
What will it take to generate fairness-preserving explanations?
[article]
2021
arXiv
pre-print
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However, the link between the fairness of the black-box model and the behavior of explanations for the black-box is unclear. ...
We focus on explanations applied to tabular datasets, suggesting that explanations do not necessarily preserve the fairness properties of the black-box algorithm. ...
Acknowledgements We would like to thank the anonymous reviewers for their insightful feedback. This work is supported in part by the NSF award #IIS-2008461, and Google. ...
arXiv:2106.13346v1
fatcat:r7oqytmvczdqnes3p4xgqh3rha
An interpretable semi-supervised classifier using two different strategies for amended self-labeling
[article]
2020
arXiv
pre-print
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Two different approaches for amending the self-labeling process are explored: a first one based on the confidence of the black box and the latter one based on measures from Rough Set Theory. ...
In this paper, we report on an extended experimental study presenting an interpretable self-labeling grey-box classifier that uses a black box to estimate the missing class labels and a white box to explain ...
The first strategy uses the confidence of the predictions made by the base black box and the second one focuses on the possible inconsistency of the enlarged dataset. ...
arXiv:2001.09502v2
fatcat:y2a6j54pxrhgbo5u432nksnsea
Payoff-based learning explains the decline in cooperation in public goods games
2015
Proceedings of the Royal Society of London. Biological Sciences
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However, an alternative explanation for the empirical observations is that individuals are mistaken, but learn, during the game, how to improve their personal payoff. ...
The results of such games have been used to argue that people are pro-social, and that humans are uniquely altruistic, willingly sacrificing their own welfare in order to benefit others. ...
We thank Jay Biernaskie, Innes Cuthill, Claire El Mouden, Nichola Raihani and two anonymous referees for comments. ...
doi:10.1098/rspb.2014.2678
pmid:25589609
pmcid:PMC4309006
fatcat:u6trasnxwfgzrjcuf5pxkrutne
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis
[article]
2021
arXiv
pre-print
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Importantly, we show that the proposed method leads to favorable scores on standard benchmarks for vision (and language models) while drastically reducing the computing time compared to other black-box ...
prediction through the lens of variance. ...
The computing hardware was supported in part by NIH Office of the Director grant #S10OD025181 via the Center for Computation and Visualization. ...
arXiv:2111.04138v1
fatcat:p4jv36ankncipm7fmbnnypwtqm
Speech Understanding Through Syntactic and Semantic Analysis
1973
International Joint Conference on Artificial Intelligence
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We already have begun work on a second ver-Stanford Research Institute is participating in a sion that will use a new parser, now under development, major program of research on the analysis of continuous ...
The goal is the development of a new system is still in the process of construction, alspeech understanding system capable of engaging a human though the parser is far enough along for us to present operator ...
ACKNOWLEDGEMENTS The following people have been involved in the research on speech understanding at SRI reflected in this paper: ...
dblp:conf/ijcai/Walker73
fatcat:p2eu3ehbtvhurksd5bft7wxw64
Fifty shades of black
2021
Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
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The rapid growth of machine learning (black-box) techniques and computing capacity has started to transform many research domains, including building performance analysis. ...
These insights present a starting point for further work towards highly scalable models yielding new characterizations of residential buildings. ...
ACKNOWLEDGMENTS This work is funded by the Dutch Research Council (NWO), in the context of the call for Energy System Integration & Big Data (ESI-bida). ...
doi:10.1145/3486611.3491120
fatcat:mz4xd2jui5aw7fzxmylyl6fj5i
Auditing Black-box Models for Indirect Influence
[article]
2016
arXiv
pre-print
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Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. ...
Our work focuses on the problem of indirect influence: how some features might indirectly influence outcomes via other, related features. ...
Direct and Indirect Influence Much of the modern literature on black-box auditing (See Section II for details) focuses on what we call direct influence: how does a feature (or a group of features) directly ...
arXiv:1602.07043v2
fatcat:fnfcr6n46ra4tme3psuocpczqe
RelEx: A Model-Agnostic Relational Model Explainer
[article]
2020
arXiv
pre-print
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In this work, we develop RelEx, a model-agnostic relational explainer to explain black-box relational models with only access to the outputs of the black-box. ...
This is essential, as complex deep learning models with millions of parameters produce state of the art results, but it can be nearly impossible to explain their predictions. ...
The first group finds important data points, which have high influence on learnt model behavior, including influence function (IF) [4] , and representer points [5] . ...
arXiv:2006.00305v1
fatcat:roqfurwfrng4tdq2akzovltawi
Auditing Black-Box Models for Indirect Influence
2016
2016 IEEE 16th International Conference on Data Mining (ICDM)
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Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. ...
Our work focuses on the problem of indirect influence: how some features might indirectly influence outcomes via other, related features. ...
Direct and Indirect Influence Much of the modern literature on black-box auditing (See Section II for details) focuses on what we call direct influence: how does a feature (or a group of features) directly ...
doi:10.1109/icdm.2016.0011
dblp:conf/icdm/AdlerFFRSSV16
fatcat:b3lvgpugkrgh5ohip6m6hpia3m
Auction optimization using regression trees and linear models as integer programs
2017
Artificial Intelligence
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We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. ...
This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings. ...
Second, we translate each of the item sets into constraints for both the black-box and white-box optimization solvers. ...
doi:10.1016/j.artint.2015.05.004
fatcat:kh67ekf245an3jva7gno7ovmoe
On The Importance Of Deep Learning Regularization Techniques In Knowledge Discovery
2018
Zenodo
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The impact of regularization on knowledge discovery process is in the focus of this paper. In order to illustrate the effect of regularization in knowledge discovery, a case study is presented. ...
Nowadays, in the era of complex data, the knowledge discovery process became one of the key challenges in the science. ...
The knowledge in the black box of DNN is result of the learning process, allowed by activation function. ...
doi:10.5281/zenodo.1447167
fatcat:453e4d4rtbbf5anwyriirkn5vu
A Survey Of Methods For Explaining Black Box Models
[article]
2018
arXiv
pre-print
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The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective. ...
Given a problem definition, a black box type, and a desired explanation this survey should help the researcher to find the proposals more useful for his own work. ...
The first one is mostly focused on describing how black boxes work, while the second one is more interested in explaining the decisions even without understanding the details on how the opaque decision ...
arXiv:1802.01933v3
fatcat:n6ly5sqyjfhwjbcdx3h32cmiqi
Emotionality ratings and open-field behavior
1969
Psychonomic Science
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On the fourth rating day, S was placed in the start box within the fjeld. Thirty seconds later the box was gently lifted from the open-field, and S was observed for 3 min. ...
Within the limits of the procedures employed, no prediction of Ss' open-field behavior on the basis of emotionality rating could be made. ...
On the fourth rating day, S was placed in the start box within the fjeld. Thirty seconds later the box was gently lifted from the open-field, and S was observed for 3 min. ...
doi:10.3758/bf03332740
fatcat:yiwbs34s5bgtroirgeypv4opau
Estimating extremely low probability of stochastic defect in extreme ultraviolet lithography from critical dimension distribution measurement
2019
Journal of Micro/Nanolithography
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We discuss a method for predicting stochastic defect probabilities from a histogram of feature sizes for patterns several orders of magnitude fewer than the number of features to inspect. ...
The defect probabilities in the order between 10 −7 and 10 −5 were predicted from 10 5 measurement data for real EUV-exposed wafers, suggesting the effectiveness of the model and its potential for defect ...
De Bisschop and IMEC for the sample preparation and for their support of this work. ...
doi:10.1117/1.jmm.18.2.024002
fatcat:m2sqqu4z2fgjvkpodqxeoae2k4
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