Fetching the paper…
Reading the bibliography…
Post-hoc explanation techniques refer to a posteriori methods that can be used to explain how black-box machine learning models produce their outcomes.
R. L. Rivest, “Learning decision lists,” Machine learning , vol. 2, no. 3, pp. 229–246, 1987
1987
Earlier work this paper cites.
D. Angluin, “Queries and concept learning,” Machine learning , vol. 2, no. 4, pp. 319–342, 1988
1988
Earlier work this paper cites.
M. Craven and J. W. Shavlik, “Extracting tree-structured representations of trained networks,” in Advances in neural information processing systems , 1996, pp. 24–30
1996
Earlier work this paper cites.
A. Blum and T. Mitchell, “Combining labeled and unlabeled data with co-training,” in Proceedings of the eleventh annual conference on Computational learning theory , 1998, pp. 92–100
1998
Earlier work this paper cites.
L. Breiman, “Random forests,” Machine learning , vol. 45, no. 1, pp. 5–32, 2001
2001
Earlier work this paper cites.
J. Li, H. Shen, and R. Topor, “Mining the optimal class association rule set,” Knowledge-Based Systems , vol. 15, no. 7, pp. 399–405, 2002
2002
Earlier work this paper cites.
D. Lowd and C. Meek, “Adversarial learning,” in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , 2005, pp. 641–647
2005
Earlier work this paper cites.
H. C. Koh, W. C. Tan, and C. P. Goh, “A two-step method to construct credit scoring models with data mining techniques,” International Journal of Business and Information , vol. 1, no. 1, 2006
2006
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International conference on theory and applications of models of computation . Springer, 2008, pp. 1–19
2008
Earlier work this paper cites.
D. Erhan, Y. Bengio, A. Courville, and P. Vincent, “Visualizing higher-layer features of a deep network,” University of Montreal , vol. 1341, no. 3, p. 1, 2009
2009
Earlier work this paper cites.
2009
Earlier work this paper cites.
P. R. Rijnbeek and J. A. Kors, “Finding a short and accurate decision rule in disjunctive normal form by exhaustive search,” Machine learning , vol. 80, no. 1, pp. 33–62, 2010
2010
Earlier work this paper cites.
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller, “How to explain individual classification decisions,” The Journal of Machine Learning Research , vol. 11, pp. 1803–1831, 2010
2010
Earlier work this paper cites.
A. Frank and A. Asuncion, “Uci machine learning repository [http://archive. ics. uci. edu/ml]. irvine, ca: University of california,” School of information and computer science , vol. 213, pp. 2–2, 2010
2010
Earlier work this paper cites.
T. McCormick, C. Rudin, and D. Madigan, “A hierarchical model for association rule mining of sequential events: An approach to automated medical symptom prediction,” 2011
2011
Earlier work this paper cites.
N. Siddiqi, Credit risk scorecards: developing and implementing intelligent credit scoring . John Wiley & Sons, 2012, vol. 3
2012
Earlier work this paper cites.
K. P. Murphy, Machine learning: a probabilistic perspective . MIT press, 2012
2012
Earlier work this paper cites.
T. Tieleman and G. Hinton, “Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude,” COURSERA: Neural networks for machine learning , vol. 4, no. 2, pp. 26–31, 2012
2012
Earlier work this paper cites.
P. Cortez and M. J. Embrechts, “Using sensitivity analysis and visualization techniques to open black box data mining models,” Information Sciences , vol. 225, pp. 1–17, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart, “Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing,” in 23rd { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 14) , 2014, pp. 17–32
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
C. C. Miller, “Can an algorithm hire better than a human?” Jun 2015
2015
Earlier work this paper cites.
F. Wang and C. Rudin, “Falling rule lists,” in Artificial Intelligence and Statistics , 2015, pp. 1013–1022
2015
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , 2015, pp. 1322–1333
2015
Earlier work this paper cites.
B. Ustun and C. Rudin, “Supersparse linear integer models for optimized medical scoring systems,” Machine Learning , vol. 102, no. 3, pp. 349–391, 2016
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “Why should I trust you?: Explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’16) . ACM, 2016, pp. 1135–1144
2016
Earlier work this paper cites.
J. Krause, A. Perer, and K. Ng, “Interacting with predictions: Visual inspection of black-box machine learning models,” in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems , 2016, pp. 5686–5697
2016
Earlier work this paper cites.
B. Kim, R. Khanna, and O. O. Koyejo, “Examples are not enough, learn to criticize! criticism for interpretability,” in Advances in neural information processing systems , 2016, pp. 2280–2288
2016
Earlier work this paper cites.
I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning . MIT Press, 2016
2016
Earlier work this paper cites.
J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine bias,” ProPublica, May , vol. 23, 2016
2016
Cited alongside, same era.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” in 25th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 16) , 2016, pp. 601–618
2016
Cited alongside, same era.
J. Kleinberg, H. Lakkaraju, J. Leskovec, J. Ludwig, and S. Mullainathan, “Human decisions and machine predictions,” The quarterly journal of economics , vol. 133, no. 1, pp. 237–293, 2017
2017
Cited alongside, same era.
R. Wexler, “When a computer program keeps you in jail: How computers are harming criminal justice,” New York Times , 2017
2017
Cited alongside, same era.
B. Goodman and S. Flaxman, “European union regulations on algorithmic decision-making and a “right to explanation”,” AI Magazine , vol. 38, no. 3, pp. 50–57, 2017
C. Rudin, “Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead,” Nature Machine Intelligence , vol. 1, no. 5, pp. 206–215, 2019
2019
Later among the works it cites.
U. Aïvodji, H. Arai, O. Fortineau, S. Gambs, S. Hara, and A. Tapp, “Fairwashing: the risk of rationalization,” in International Conference on Machine Learning , 2019, pp. 161–170
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Laugel, M.-J. Lesot, C. Marsala, X. Renard, and M. Detyniecki, “The dangers of post-hoc interpretability: unjustified counterfactual explanations,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence . AAAI Press, 2019, pp. 2801–2807
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2017
Cited alongside, same era.
B. Lepri, N. Oliver, E. Letouzé, A. Pentland, and P. Vinck, “Fair, transparent, and accountable algorithmic decision-making processes,” Philosophy & Technology , pp. 1–17, 2017
2017
Cited alongside, same era.
E. Angelino, N. Larus-Stone, D. Alabi, M. Seltzer, and C. Rudin, “Learning certifiably optimal rule lists,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . Halifax, NS, Canada: ACM, 2017, pp. 35–44
2017
Cited alongside, same era.
L. Breiman, Classification and regression trees . Routledge, 2017
2017
Cited alongside, same era.
H. Yang, C. Rudin, and M. Seltzer, “Scalable bayesian rule lists,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 3921–3930
2017
Cited alongside, same era.
J. Zeng, B. Ustun, and C. Rudin, “Interpretable classification models for recidivism prediction,” Journal of the Royal Statistical Society: Series A (Statistics in Society) , vol. 180, no. 3, pp. 689–722, 2017
2017
Cited alongside, same era.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS’17) , 2017, pp. 4765–4774
2017
Cited alongside, same era.
S. Wachter, B. Mittelstadt, and C. Russell, “Counterfactual explanations without opening the black box: Automated decisions and the gdpr,” Harv. JL & Tech. , vol. 31, p. 841, 2017
2017
Cited alongside, same era.
A. Ghorbani, A. Abid, and J. Zou, “Interpretation of neural networks is fragile,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 3681–3688
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
S. Milli, L. Schmidt, A. D. Dragan, and M. Hardt, “Model reconstruction from model explanations,” in Proceedings of the Conference on Fairness, Accountability, and Transparency , 2019, pp. 1–9
2019
Later among the works it cites.
C. Russell, “Efficient search for diverse coherent explanations,” in Proceedings of the Conference on Fairness, Accountability, and Transparency , 2019, pp. 20–28
2019
Later among the works it cites.
B. Ustun, A. Spangher, and Y. Liu, “Actionable recourse in linear classification,” in Proceedings of the Conference on Fairness, Accountability, and Transparency , 2019, pp. 10–19
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Elsken, J. H. Metzen, and F. Hutter, “Neural architecture search: A survey,” Journal of Machine Learning Research , vol. 20, pp. 1–21, 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
T. Orekondy, B. Schiele, and M. Fritz, “Knockoff nets: Stealing functionality of black-box models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 4954–4963
2019
Later among the works it cites.
S. J. Oh, B. Schiele, and M. Fritz, “Towards reverse-engineering black-box neural networks,” in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning . Springer, 2019, pp. 121–144
2019
Later among the works it cites.
2019
Later among the works it cites.
M. Juuti, S. Szyller, S. Marchal, and N. Asokan, “Prada: protecting against dnn model stealing attacks,” in 2019 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2019, pp. 512–527
2019
Later among the works it cites.
X. Zhang, N. Wang, H. Shen, S. Ji, X. Luo, and T. Wang, “Interpretable deep learning under fire,” in 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) , 2020
2020
Closest in time.
M. Pawelczyk, K. Broelemann, and G. Kasneci, “Learning model-agnostic counterfactual explanations for tabular data,” in Proceedings of The Web Conference 2020 , 2020, pp. 3126–3132
2020
Closest in time.
R. K. Mothilal, A. Sharma, and C. Tan, “Explaining machine learning classifiers through diverse counterfactual explanations,” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , 2020, pp. 607–617
2020
Closest in time.
A.-H. Karimi, G. Barthe, B. Balle, and I. Valera, “Model-agnostic counterfactual explanations for consequential decisions,” in International Conference on Artificial Intelligence and Statistics , 2020, pp. 895–905
2020
Closest in time.
M. Jagielski, N. Carlini, D. Berthelot, A. Kurakin, and N. Papernot, “High accuracy and high fidelity extraction of neural networks,” in 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) , 2020
2020
Closest in time.
V. Chandrasekaran, K. Chaudhuri, I. Giacomelli, S. Jha, and S. Yan, “Exploring connections between active learning and model extraction,” in 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) , 2020, pp. 1309–1326
2020
Closest in time.
2020
Closest in time.
E. Le Merrer, P. Perez, and G. Trédan, “Adversarial frontier stitching for remote neural network watermarking,” Neural Computing and Applications , vol. 32, no. 13, pp. 9233–9244, 2020
2020
Closest in time.
L. Hancox-Li, “Robustness in machine learning explanations: does it matter?” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency , 2020, pp. 640–647
2020
Closest in time.