SurvNAM: The machine learning survival model explanation

18 April 2021

Papers citing "SurvNAM: The machine learning survival model explanation"

48 / 48 papers shown

Title
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 231 674 0 20 Mar 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 141 173 0 13 Jan 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 207 684 0 28 Dec 2020
Interpretable Machine Learning with an Ensemble of Gradient Boosting Machines A. Konstantinov Lev V. Utkin FedML AI4CE 42 150 0 14 Oct 2020
Principles and Practice of Explainable Machine Learning Vaishak Belle I. Papantonis FaML 67 449 0 18 Sep 2020
How Interpretable and Trustworthy are GAMs? C. Chang S. Tan Benjamin J. Lengerich Anna Goldenberg R. Caruana FAtt 114 79 0 11 Jun 2020
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds M. Kovalev Lev V. Utkin AAML 75 31 0 05 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 107 379 0 30 Apr 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 99 420 0 29 Apr 2020
Adaptive Explainable Neural Networks (AxNNs) Jie Chen J. Vaughan V. Nair Agus Sudjianto 23 21 0 05 Apr 2020
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 272 91 0 18 Mar 2020
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 153 130 0 16 Mar 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 100 359 0 17 Jan 2020
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 130 6,321 0 22 Oct 2019
Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph Johannes Rabold Hannah Deininger M. Siebers Ute Schmid FAtt 53 57 0 04 Oct 2019
InterpretML: A Unified Framework for Machine Learning Interpretability Harsha Nori Samuel Jenkins Paul Koch R. Caruana AI4CE 164 489 0 19 Sep 2019
Towards Understanding the Importance of Shortcut Connections in Residual Networks Tianyi Liu Minshuo Chen Mo Zhou S. Du Enlu Zhou T. Zhao 35 45 0 10 Sep 2019
NormLime: A New Feature Importance Metric for Explaining Deep Neural Networks Isaac Ahern Adam Noack Luis Guzman-Nateras Dejing Dou Boyang Albert Li Jun Huan FAtt 32 39 0 10 Sep 2019
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 72 393 0 06 Sep 2019
ALIME: Autoencoder Based Approach for Local Interpretability Sharath M. Shankaranarayana D. Runje FAtt 53 104 0 04 Sep 2019
Multiple instance dense connected convolution neural network for aerial image scene classification Qi Bi K. Qin Zhili Li Han Zhang Kai Xu 99 114 0 22 Aug 2019
Measurable Counterfactual Local Explanations for Any Classifier Adam White Artur Garcez FAtt 64 99 0 08 Aug 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 80 387 0 03 Jul 2019
DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems Muhammad Rehman Zafar N. Khan FAtt 114 158 0 24 Jun 2019
c-Eval: A Unified Metric to Evaluate Feature-based Explanations via Perturbation Minh Nhat Vu Truc D. T. Nguyen Nhathai Phan Ralucca Gera My T. Thai AAML FAtt 58 22 0 05 Jun 2019
Counterfactual Visual Explanations Yash Goyal Ziyan Wu Jan Ernst Dhruv Batra Devi Parikh Stefan Lee CML 80 512 0 16 Apr 2019
Interpreting Black Box Models via Hypothesis Testing Collin Burns Jesse Thomason Wesley Tansey FAtt 50 9 0 29 Mar 2019
Generalized Sparse Additive Models Asad Haris N. Simon Ali Shojaie 45 16 0 11 Mar 2019
Techniques for Interpretable Machine Learning Mengnan Du Ninghao Liu Helen Zhou FaML 93 1,092 0 31 Jul 2018
Grounding Visual Explanations Lisa Anne Hendricks Ronghang Hu Trevor Darrell Zeynep Akata FAtt 59 230 0 25 Jul 2018
Contrastive Explanations with Local Foil Trees J. V. D. Waa M. Robeer J. Diggelen Matthieu J. S. Brinkhuis Mark Antonius Neerincx FAtt 69 82 0 19 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 188 1,176 0 19 Jun 2018
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP) Linwei Hu Jie Chen V. Nair Agus Sudjianto FAtt 57 63 0 02 Jun 2018
A Deep Active Survival Analysis Approach for Precision Treatment Recommendations: Application of Prostate Cancer Milad Zafar Nezhad Najibesadat Sadati Kai Yang D. Zhu 46 65 0 10 Apr 2018
Attention-based Deep Multiple Instance Learning Maximilian Ilse Jakub M. Tomczak Max Welling 182 1,827 0 13 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 148 3,979 0 06 Feb 2018
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 135 2,366 0 01 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,090 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 83 1,525 0 11 Apr 2017
Understanding Black-box Predictions via Influence Functions Pang Wei Koh Percy Liang TDI 219 2,910 0 14 Mar 2017
An Efficient Training Algorithm for Kernel Survival Support Vector Machines Sebastian Polsterl Nassir Navab A. Katouzian 45 40 0 21 Nov 2016
Deep Survival Analysis Rajesh Ranganath A. Perotte Noémie Elhadad David M. Blei 167 199 0 06 Aug 2016
DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network Jared Katzman Uri Shaham Jonathan Bates A. Cloninger Tingting Jiang Y. Kluger BDL CML OOD 500 1,261 0 02 Jun 2016
Unbiased split variable selection for random survival forests using maximally selected rank statistics Marvin N. Wright Theresa Dankowski A. Ziegler 90 129 0 11 May 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,071 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,510 0 10 Dec 2015
On the use of Harrell's C for clinical risk prediction via random survival forests M. Schmid Marvin N. Wright A. Ziegler 86 99 0 11 Jul 2015
Sparse Additive Models Pradeep Ravikumar John D. Lafferty Han Liu Larry A. Wasserman 506 573 0 28 Nov 2007