Understanding Black-box Predictions via Influence Functions

14 March 2017

Pang Wei Koh

Papers citing "Understanding Black-box Predictions via Influence Functions"

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Title
Certified Data Removal from Machine Learning Models Chuan Guo Tom Goldstein Awni Y. Hannun Laurens van der Maaten MU 52 420 0 08 Nov 2019
Optimizing Millions of Hyperparameters by Implicit Differentiation Jonathan Lorraine Paul Vicol David Duvenaud DD 45 404 0 06 Nov 2019
On Second-Order Group Influence Functions for Black-Box Predictions S. Basu Xuchen You S. Feizi TDI 27 68 0 01 Nov 2019
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning Xuanqing Liu Si Si Xiaojin Zhu Yang Li Cho-Jui Hsieh AAML 35 78 0 30 Oct 2019
Distribution Density, Tails, and Outliers in Machine Learning: Metrics and Applications Nicholas Carlini Ulfar Erlingsson Nicolas Papernot OOD OODD 26 62 0 29 Oct 2019
CXPlain: Causal Explanations for Model Interpretation under Uncertainty Patrick Schwab W. Karlen FAtt CML 40 206 0 27 Oct 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 41 6,125 0 22 Oct 2019
On Completeness-aware Concept-Based Explanations in Deep Neural Networks Chih-Kuan Yeh Been Kim Sercan O. Arik Chun-Liang Li Tomas Pfister Pradeep Ravikumar FAtt 122 297 0 17 Oct 2019
The Local Elasticity of Neural Networks Hangfeng He Weijie J. Su 45 44 0 15 Oct 2019
Can I Trust the Explainer? Verifying Post-hoc Explanatory Methods Oana-Maria Camburu Eleonora Giunchiglia Jakob N. Foerster Thomas Lukasiewicz Phil Blunsom FAtt AAML 34 60 0 04 Oct 2019
Hidden Trigger Backdoor Attacks Aniruddha Saha Akshayvarun Subramanya Hamed Pirsiavash 36 613 0 30 Sep 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 32 437 0 26 Sep 2019
Data Valuation using Reinforcement Learning Jinsung Yoon Sercan O. Arik Tomas Pfister TDI 36 174 0 25 Sep 2019
On Model Stability as a Function of Random Seed Pranava Madhyastha Dhruv Batra 45 62 0 23 Sep 2019
Measure Contribution of Participants in Federated Learning Guan Wang Charlie Xiaoqian Dang Ziye Zhou FedML 47 195 0 17 Sep 2019
Rewarding High-Quality Data via Influence Functions A. Richardson Aris Filos-Ratsikas Boi Faltings FedML TDI 35 40 0 30 Aug 2019
Regional Tree Regularization for Interpretability in Black Box Models Mike Wu S. Parbhoo M. C. Hughes R. Kindle Leo Anthony Celi Maurizio Zazzi Volker Roth Finale Doshi-Velez 23 37 0 13 Aug 2019
LoRMIkA: Local rule-based model interpretability with k-optimal associations Dilini Sewwandi Rajapaksha Christoph Bergmeir Wray Buntine 35 31 0 11 Aug 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 32 67 0 16 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 29 380 0 03 Jul 2019
Quantitative Verification of Neural Networks And its Security Applications Teodora Baluta Shiqi Shen Shweta Shinde Kuldeep S. Meel P. Saxena AAML 24 104 0 25 Jun 2019
Data Cleansing for Models Trained with SGD Satoshi Hara Atsushi Nitanda Takanori Maehara TDI 34 68 0 20 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 36 120 0 19 Jun 2019
Poisoning Attacks with Generative Adversarial Nets Luis Muñoz-González Bjarne Pfitzner Matteo Russo Javier Carnerero-Cano Emil C. Lupu AAML 21 63 0 18 Jun 2019
Membership Privacy for Machine Learning Models Through Knowledge Transfer Virat Shejwalkar Amir Houmansadr 22 10 0 15 Jun 2019
Interpretable Neural Network Decoupling Yuchao Li Rongrong Ji Shaohui Lin Baochang Zhang Chenqian Yan Yongjian Wu Feiyue Huang Ling Shao 37 2 0 04 Jun 2019
Do Human Rationales Improve Machine Explanations? Julia Strout Ye Zhang Raymond J. Mooney 19 57 0 31 May 2019
A backdoor attack against LSTM-based text classification systems Jiazhu Dai Chuanshuai Chen SILM 8 320 0 29 May 2019
Discovering Conditionally Salient Features with Statistical Guarantees Jaime Roquero Gimenez James Zou CML 16 12 0 29 May 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 6 235 0 24 May 2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder Ji Feng Qi-Zhi Cai Zhi-Hua Zhou AAML 19 104 0 22 May 2019
The Audio Auditor: User-Level Membership Inference in Internet of Things Voice Services Yuantian Miao Minhui Xue Chao Chen Lei Pan Jinchao Zhang Benjamin Zi Hao Zhao Dali Kaafar Yang Xiang 21 34 0 17 May 2019
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks Zhengwei Wang Qi She Alan F. Smeaton T. Ward Graham Healy EGVM 21 11 0 10 May 2019
Interpretability with Accurate Small Models Abhishek Ghose Balaraman Ravindran 23 1 0 04 May 2019
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach Ki Hyun Tae Yuji Roh Young H. Oh Hyunsub Kim Steven Euijong Whang 19 71 0 22 Apr 2019
Regression Concept Vectors for Bidirectional Explanations in Histopathology Mara Graziani Vincent Andrearczyk Henning Muller 47 78 0 09 Apr 2019
Data Shapley: Equitable Valuation of Data for Machine Learning Amirata Ghorbani James Zou TDI FedML 42 756 0 05 Apr 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
GNNExplainer: Generating Explanations for Graph Neural Networks Rex Ying Dylan Bourgeois Jiaxuan You Marinka Zitnik J. Leskovec LLMAG 37 1,291 0 10 Mar 2019
Towards Efficient Data Valuation Based on the Shapley Value R. Jia David Dao Wei Ping F. Hubis Nicholas Hynes Nezihe Merve Gürel Bo Li Ce Zhang D. Song C. Spanos TDI 22 400 0 27 Feb 2019
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines Aritra Chowdhury M. Magdon-Ismail B. Yener 35 0 0 21 Feb 2019
Towards Automatic Concept-based Explanations Amirata Ghorbani James Wexler James Zou Been Kim FAtt LRM 38 19 0 07 Feb 2019
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions Hao Wang Berk Ustun Flavio du Pin Calmon FaML 36 83 0 29 Jan 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 39 449 0 27 Jan 2019
Quantifying Interpretability and Trust in Machine Learning Systems Philipp Schmidt F. Biessmann 16 112 0 20 Jan 2019
Towards Aggregating Weighted Feature Attributions Umang Bhatt Pradeep Ravikumar José M. F. Moura FAtt TDI 14 13 0 20 Jan 2019
Visual Entailment: A Novel Task for Fine-Grained Image Understanding Ning Xie Farley Lai Derek Doran Asim Kadav CoGe 56 322 0 20 Jan 2019
Explaining Explanations to Society Leilani H. Gilpin Cecilia Testart Nathaniel Fruchter Julius Adebayo XAI 24 34 0 19 Jan 2019
Class-Balanced Loss Based on Effective Number of Samples Huayu Chen Menglin Jia Nayeon Lee Yang Song Serge J. Belongie 91 2,233 0 16 Jan 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 52 1,423 0 14 Jan 2019