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Influence estimation analyzes how changes to the training data can lead to different model predictions; this analysis can help us better understand these predictions, the models making those predictions, and the data sets they're trained on.
The value of n-person games
LS Shapley · 1953
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The infinitesimal jackknife
Louis A. Jaeckel · 1972
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The influence curve and its role in robust estimation
Frank R. Hampel · 1974
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Residuals and Influence in Regression
R Dennis Cook and Sanford Weisberg · 1982
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I-C Yeh · 1998
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F. Pedregosa, G. Varoquaux, A. Gramfort, et al · 2011
Earlier work this paper cites.
Local and global learning methods for predicting power of a combined gas & steam turbine
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Alex Davies and Zoubin Ghahramani · 2014
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Sérgio Moro, Paulo Cortez, et al · 2014
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Impact of HbA1c measurement on hospital readmission rates: Analysis of 70,000 clinical database patient records
Beata Strack, Jonathan P DeShazo, et al · 2014
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Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods
Pınar Tüfekci · 2014
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HSpam14: A collection of 14 million tweets for hashtag-oriented spam research
Surendra Sedhai and Aixin Sun · 2015
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Supervised neighborhoods for distributed nonparametric regression
Adam Bloniarz, Ameet Talwalkar, Bin Yu, and Christopher Wu · 2016
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Machine learning approaches for improving condition-based maintenance of naval propulsion plants
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Examples are not enough, learn to criticize! Criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O. Koyejo · 2016
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Fifty years of pulsar candidate selection: from simple filters to a new principled real-time classification approach
Robert J Lyon, BW Stappers, Sally Cooper, John Martin Brooke, and Joshua D Knowles · 2016
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“Why should I trust you?” Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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LightGBM: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, et al · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang · 2017
Deep partition aggregation: Provable defense against general poisoning attacks
Alexander Levine and Soheil Feizi · 2020
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Identifying mislabeled data using the area under the margin ranking
Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, and Kilian Weinberger · 2020
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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Airline on-time performance and causes of flight delays
Research and Innovative Technology Administration · 2020
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The many shapley values for model explanation
Mukund Sundararajan and Amir Najmi · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi · 2020
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Consistent individualized feature attribution for tree ensembles
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Model agnostic supervised local explanations
Gregory Plumb, Denali Molitor, and Ameet S Talwalkar · 2018
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CatBoost: Unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, et al · 2018
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Finding influential training samples for gradient boosted decision trees
Boris Sharchilev, Yury Ustinovskiy, Pavel Serdyukov, and Maarten Rijke · 2018
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Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Sik Kim, Ian EH Yen, and Pradeep Ravikumar · 2018
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Tree space prototypes: Another look at making tree ensembles interpretable
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UCI machine learning repository
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Evaluation of similarity-based explanations
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Medical appointment no shows
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Understanding instance-based interpretability of variational auto-encoders
Zhifeng Kong and Kamalika Chaudhuri · 2021
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How we analyzed the COMPAS recidivism algorithm
Jeff Larsen, Surya Mattu, Lauren Kirchner, and Julia Angwin · 2021
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Influence selection for active learning
Zhuoming Liu, Hao Ding, Huaping Zhong, Weijia Li, Jifeng Dai, and Conghui He · 2021
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Uncertainty in gradient boosting via ensembles
Andrey Malinin, Liudmila Prokhorenkova, and Aleksei Ustimenko · 2021
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COMPAS recidivism racial bias
Dan Ofer · 2021
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Influence-guided data augmentation for neural tensor completion
Sejoon Oh, Sungchul Kim, Ryan A. Rossi, and Srijan Kumar · 2021
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Life expectancy (WHO)
Kumar Rajarshi · 2021
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CDC data: Nutrition, physical activity, & obesity
Suzanne · 2021
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Influence estimation for generative adversarial networks
Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, and Takashi Kanemaru · 2021
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On sample based explanation methods for NLP: Faithfulness, efficiency and semantic evaluation
Wei Zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, and Fan Zhang · 2021
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If influence functions are the answer, then what is the question?
Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger Grosse · 2022
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Influence based re-weighing for labeling noise in medical imaging
Joschka Braun, Micha Kornreich, JinHyeong Park, Jayashri Pawar, James Browning, Richard Herzog, Benjamin Odry, and Li Zhang · 2022
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Reducing certified regression to certified classification for general poisoning attacks
Zayd Hammoudeh and Daniel Lowd · 2023
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