Fetching the paper…
Reading the bibliography…
Due to the increasing use of machine learning in practice it becomes more and more important to be able to explain the prediction and behavior of machine learning models.
Case-based reasoning: Foundational issues, methodological variations, and systemapproaches
A. Aamodt and E. Plaza · 1994
Earlier work this paper cites.
Python tutorial
Guido Van Rossum and Fred L Drake Jr · 1995
Earlier work this paper cites.
Convex Optimization
Stephen Boyd and Lieven Vandenberghe · 2004
Earlier work this paper cites.
Mixed Integer Programming
Laurence A. Wolsey · 2008
Earlier work this paper cites.
Adaptive relevance matrices in learning vector quantization
Petra Schneider, Michael Biehl, and Barbara Hammer · 2009
Earlier work this paper cites.
Distance learning in discriminative vector quantization
Petra Schneider, Michael Biehl, and Barbara Hammer · 2009
Earlier work this paper cites.
Consumer credit-risk models via machine-learning algorithms
Amir E. Khandani, Adlar J. Kim, and Andrew Lo · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
The numpy array: A structure for efficient numerical computation
Stéfan van der Walt, S. Chris Colbert, and Gaël Varoquaux · 2011
Earlier work this paper cites.
A review of learning vector quantization classifiers
David Nova and Pablo A. Estévez · 2014
Earlier work this paper cites.
Regulation (eu) 2016/679 of the european parliament and of the council of 27 april 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing directive 95/46/ec (general data protection regulation)
European parliament and council · 2016
Earlier work this paper cites.
”why should i trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Oluwasanmi Koyejo, and Rajiv Khanna · 2016
Earlier work this paper cites.
A survey on decision tree algorithms of classification in data mining
Himani Sharma and Sunil Kumar · 2016
Cited alongside, same era.
CVXPY: A Python-embedded modeling language for convex optimization
Steven Diamond and Stephen Boyd · 2016
Cited alongside, same era.
Wojciech Samek, Thomas Wiegand, and Klaus-Robert Müller · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the GDPR
Sandra Wachter, Brent D. Mittelstadt, and Chris Russell · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
Cited alongside, same era.
Comparison-based inverse classification for interpretability in machine learning
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki · 2018
Later among the works it cites.
Exploring applications of deep reinforcement learning for real-world autonomous driving systems
Victor Talpaert, Ibrahim Sobh, B. Ravi Kiran, Patrick Mannion, Senthil Yogamani, Ahmad El Sallab, and Patrick Perez · 2019
Closest in time.
Deep learning for audio signal processing
Hendrik Purwins, Bo Li, Tuomas Virtanen, Jan Schlüter, Shuo-Yiin Chang, and Tara N. Sainath · 2019
Closest in time.
A survey of deep learning-based object detection
Licheng Jiao, Fan Zhang, Fang Liu, Shuyuan Yang, Lingling Li, Zhixi Feng, and Rong Qu · 2019
Closest in time.
Interpretable Machine Learning
Christoph Molnar · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jaehyun Park and Stephen Boyd · 2017
Cited alongside, same era.
Interpretable predictions of tree-based ensembles via actionable feature tweaking
Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines, and Mounia Lalmas · 2017
Cited alongside, same era.
A survey of the usages of deep learning in natural language processing
Daniel W. Otter, Julian R. Medina, and Jugal K. Kalita · 2018
Cited alongside, same era.
Examining deep learning architectures for crime classification and prediction
Panagiotis Stalidis, Theodoros Semertzidis, and Petros Daras · 2018
Cited alongside, same era.
Explaining explanations: An overview of interpretability of machine learning
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
Cited alongside, same era.
A simple and effective model-based variable importance measure
Brandon M. Greenwell, Bradley C. Boehmke, and Andrew J. McCarthy · 2018
Cited alongside, same era.
Aaron Fisher, Cynthia Rudin, and Francesca Dominici · 2018
Cited alongside, same era.
Closest in time.
A survey on explainable artificial intelligence (XAI): towards medical XAI
Erico Tjoa and Cuntai Guan · 2019
Closest in time.
Causal interpretations of black-box models
Qingyuan Zhao and Trevor Hastie · 2019
Closest in time.
Efficient computation of counterfactual explanations of LVQ models
André Artelt and Barbara Hammer · 2019
Closest in time.
Interpretable counterfactual explanations guided by prototypes
Arnaud Van Looveren and Janis Klaise · 2019
Closest in time.
FACE: feasible and actionable counterfactual explanations
Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, and Peter A. Flach · 2019
Closest in time.
Shubham Sharma, Jette Henderson, and Joydeep Ghosh · 2019
Closest in time.
Efficient search for diverse coherent explanations
Chris Russell · 2019
Closest in time.
Ceml: Counterfactuals for explaining machine learning models - a python toolbox
André Artelt · 2019
Closest in time.