Fetching the paper…
Reading the bibliography…
Recently, there has been a growing demand for the deployment of Explainable Artificial Intelligence (XAI) algorithms in real-world applications.
M. G. Kendall, “A new measure of rank correlation,” Biometrika , vol. 30, no. 1/2, pp. 81–93, 1938
1938
Earlier work this paper cites.
H. W. Kuhn and A. W. Tucker, Contributions to the Theory of Games . Princeton University Press, 1953, vol. 2
1953
Earlier work this paper cites.
C. W. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: journal of the Econometric Society , pp. 424–438, 1969
1969
Earlier work this paper cites.
M. Grabisch and M. Roubens, “An axiomatic approach to the concept of interaction among players in cooperative games,” International Journal of game theory , vol. 28, no. 4, pp. 547–565, 1999
1999
Earlier work this paper cites.
C. Ai and E. C. Norton, “Interaction terms in logit and probit models,” Economics letters , vol. 80, no. 1, pp. 123–129, 2003
2003
Earlier work this paper cites.
J. Castro, D. Gómez, and J. Tejada, “Polynomial calculation of the shapley value based on sampling,” Computers & Operations Research , vol. 36, no. 5, pp. 1726–1730, 2009
2009
Earlier work this paper cites.
2013
Earlier work this paper cites.
H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin et al. , “Ad click prediction: a view from the trenches,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining , 2013, pp. 1222–1230
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
M. Hausknecht, P. Mupparaju, S. Subramanian, S. Kalyanakrishnan, and P. Stone, “Half field offense: An environment for multiagent learning and ad hoc teamwork,” in AAMAS Adaptive Learning Agents (ALA) Workshop , vol. 3. sn, 2016
2016
Earlier work this paper cites.
W. Masson, P. Ranchod, and G. Konidaris, “Reinforcement learning with parameterized actions,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Earlier work this paper cites.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proceedings of the 31st international conference on neural information processing systems , 2017, pp. 4768–4777
2017
Earlier work this paper cites.
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
Earlier work this paper cites.
M. Sundararajan, A. Taly, and Q. Yan, “Axiomatic attribution for deep networks,” in International Conference on Machine Learning . PMLR, 2017, pp. 3319–3328
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 618–626
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Dabkowski and Y. Gal, “Real time image saliency for black box classifiers,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
M. J. Kusner, J. Loftus, C. Russell, and R. Silva, “Counterfactual fairness,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Tsang, D. Cheng, and Y. Liu, “Detecting statistical interactions from neural network weights,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=ByOfBggRZ
2018
Earlier work this paper cites.
J. Chen, L. Song, M. Wainwright, and M. Jordan, “Learning to explain: An information-theoretic perspective on model interpretation,” in International Conference on Machine Learning . PMLR, 2018, pp. 883–892
2018
Earlier work this paper cites.
M. Tsang, H. Liu, S. Purushotham, P. Murali, and Y. Liu, “Neural interaction transparency (nit): Disentangling learned interactions for improved interpretability,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. A. Teich and P. R. Teich, “Plaster: A framework for deep learning performance,” Tech. rep. TIRIAS Research, Tech. Rep., 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. Du, N. Liu, and X. Hu, “Techniques for interpretable machine learning,” Communications of the ACM , vol. 63, no. 1, pp. 68–77, 2019
2019
Earlier work this paper 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
Earlier work this paper cites.
M. Ancona, C. Oztireli, and M. Gross, “Explaining deep neural networks with a polynomial time algorithm for shapley value approximation,” in International Conference on Machine Learning . PMLR, 2019, pp. 272–281
2019
Earlier work this paper cites.
C. Russell, “Efficient search for diverse coherent explanations,” in Proceedings of the Conference on Fairness, Accountability, and Transparency , 2019, pp. 20–28
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Kanehira and T. Harada, “Learning to explain with complemental examples,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 8603–8611
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
E. Albini, A. Rago, P. Baroni, and F. Toni, “Influence-driven explanations for bayesian network classifiers,” in Pacific Rim International Conference on Artificial Intelligence . Springer, 2021, pp. 88–100
2021
Later among the works it cites.
A. V. Looveren and J. Klaise, “Interpretable counterfactual explanations guided by prototypes,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2021, pp. 650–665
2021
Later among the works it cites.
2021
Later among the works it cites.
N. Jethani, M. Sudarshan, I. C. Covert, S.-I. Lee, and R. Ranganath, “Fastshap: Real-time shapley value estimation,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Jia, M. Zaharia, and A. Aiken, “Beyond data and model parallelism for deep neural networks.” Proceedings of Machine Learning and Systems , vol. 1, pp. 1–13, 2019
2019
Cited alongside, same era.
X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang, “Lightgcn: Simplifying and powering graph convolution network for recommendation,” in Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , 2020, pp. 639–648
2020
Cited alongside, same era.
Y.-N. Chuang, C.-M. Chen, C.-J. Wang, M.-F. Tsai, Y. Fang, and E.-P. Lim, “Tpr: Text-aware preference ranking for recommender systems,” in Proceedings of the 29th ACM International Conference on Information & Knowledge Management , 2020, pp. 215–224
2020
Cited alongside, same era.
R. Dabre, C. Chu, and A. Kunchukuttan, “A survey of multilingual neural machine translation,” ACM Computing Surveys (CSUR) , vol. 53, no. 5, pp. 1–38, 2020
2020
Cited alongside, same era.
S. M. Lundberg, G. Erion, H. Chen, A. DeGrave, J. M. Prutkin, B. Nair, R. Katz, J. Himmelfarb, N. Bansal, and S.-I. Lee, “From local explanations to global understanding with explainable ai for trees,” Nature machine intelligence , vol. 2, no. 1, pp. 56–67, 2020
2020
Cited alongside, same era.
M. Sundararajan, K. Dhamdhere, and A. Agarwal, “The shapley taylor interaction index,” in International conference on machine learning . PMLR, 2020, pp. 9259–9268
2020
Cited alongside, same era.
2020
Cited alongside, same era.
M. Tsang, S. Rambhatla, and Y. Liu, “How does this interaction affect me? interpretable attribution for feature interactions,” Advances in neural information processing systems , vol. 33, pp. 6147–6159, 2020
2020
Cited alongside, same era.
F. Yang, N. Liu, M. Du, and X. Hu, “Generative counterfactuals for neural networks via attribute-informed perturbation,” ACM SIGKDD Explorations Newsletter , vol. 23, no. 1, pp. 59–68, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
P. Rodríguez, M. Caccia, A. Lacoste, L. Zamparo, I. Laradji, L. Charlin, and D. Vazquez, “Beyond trivial counterfactual explanations with diverse valuable explanations,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 1056–1065
2021
Later among the works it cites.
2021
Later among the works it cites.
V. Kaffes, D. Sacharidis, and G. Giannopoulos, “Model-agnostic counterfactual explanations of recommendations,” in Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization , 2021, pp. 280–285
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
W. Fu, M. Wang, M. Du, N. Liu, S. Hao, and X. Hu, “Differentiated explanation of deep neural networks with skewed distributions,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 6, pp. 2909–2922, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
R. Hesse, S. Schaub-Meyer, and S. Roth, “Fast axiomatic attribution for neural networks,” Advances in Neural Information Processing Systems , vol. 34, pp. 19 513–19 524, 2021
2021
Later among the works it cites.
N. Jethani, M. Sudarshan, Y. Aphinyanaphongs, and R. Ranganath, “Have we learned to explain?: How interpretability methods can learn to encode predictions in their interpretations.” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 1459–1467
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
N. Chandolikar, C. Joshi, P. Roy, A. Gawas, and M. Vishwakarma, “Voice recognition: A comprehensive survey,” in 2022 International Mobile and Embedded Technology Conference (MECON) . IEEE, 2022, pp. 45–51
2022
Later among the works it cites.
C. Molnar, “Interpretable machine learning: A guide for making black box models explainable,” 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
R. Mitchell, J. Cooper, E. Frank, and G. Holmes, “Sampling permutations for shapley value estimation,” 2022
2022
Later among the works it cites.
E. Albini, J. Long, D. Dervovic, and D. Magazzeni, “Counterfactual shapley additive explanations,” in 2022 ACM Conference on Fairness, Accountability, and Transparency , 2022, pp. 1054–1070
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Khorram and L. Fuxin, “Cycle-consistent counterfactuals by latent transformations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 10 203–10 212
2022
Later among the works it cites.
S. Verma, K. Hines, and J. P. Dickerson, “Amortized generation of sequential algorithmic recourses for black-box models,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 8, 2022, pp. 8512–8519
2022
Later among the works it cites.
A. Artelt and B. Hammer, “Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers,” Neurocomputing , vol. 470, pp. 304–317, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
A. V. Konstantinov and L. V. Utkin, “Attention-like feature explanation for tabular data,” International Journal of Data Science and Analytics , pp. 1–26, 2022
2022
Later among the works it cites.
V. Guyomard, F. Fessant, T. Guyet, T. Bouadi, and A. Termier, “Vcnet: A self-explaining model for realistic counterfactual generation,” in Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) , 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
J. L. C. Bárcena, M. Daole, P. Ducange, F. Marcelloni, A. Renda, F. Ruffini, and A. Schiavo, “Fed-xai: Federated learning of explainable artificial intelligence models,” 2022
2022
Later among the works it cites.
Y.-N. Chuang, G. Wang, F. Yang, Z. Quan, P. Tripathi, X. Cai, and X. Hu, “CoRTX: Contrastive framework for real-time explanation,” in Openreview , 2023. [Online]. Available: https://openreview.net/forum?id=L2MUOUp0beo
2023
Closest in time.