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Weiwei Pan
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Cited by
Year
Quality of uncertainty quantification for Bayesian neural network inference
J Yao, W Pan, S Ghosh, F Doshi-Velez
arXiv preprint arXiv:1906.09686, 2019
1172019
Promises and pitfalls of black-box concept learning models
A Mahinpei, J Clark, I Lage, F Doshi-Velez, W Pan
arXiv preprint arXiv:2106.13314, 2021
552021
Optimizing the multiclass F-measure via biconcave programming
H Narasimhan, W Pan, P Kar, P Protopapas, HG Ramaswamy
2016 IEEE 16th international conference on data mining (ICDM), 1101-1106, 2016
492016
Cruds: Counterfactual recourse using disentangled subspaces
M Downs, JL Chu, Y Yacoby, F Doshi-Velez, W Pan
ICML WHI 2020, 1-23, 2020
422020
Power constrained bandits
J Yao, E Brunskill, W Pan, S Murphy, F Doshi-Velez
Machine Learning for Healthcare Conference, 209-259, 2021
312021
Ensembles of locally independent prediction models
A Ross, W Pan, L Celi, F Doshi-Velez
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5527-5536, 2020
302020
Wide mean-field bayesian neural networks ignore the data
B Coker, WP Bruinsma, DR Burt, W Pan, F Doshi-Velez
International Conference on Artificial Intelligence and Statistics, 5276-5333, 2022
182022
Failure modes of variational autoencoders and their effects on downstream tasks
Y Yacoby, W Pan, F Doshi-Velez
arXiv preprint arXiv:2007.07124, 2020
172020
Learning qualitatively diverse and interpretable rules for classification
AS Ross, W Pan, F Doshi-Velez
arXiv preprint arXiv:1806.08716, 2018
142018
Bacoun: Bayesian classifers with out-of-distribution uncertainty
T Guénais, D Vamvourellis, Y Yacoby, F Doshi-Velez, W Pan
arXiv preprint arXiv:2007.06096, 2020
132020
Latent projection bnns: Avoiding weight-space pathologies by learning latent representations of neural network weights
MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez
Workshop on Bayesian Deep Learning, NIPS, 2018
112018
Deep variational transfer: Transfer learning through semi-supervised deep generative models
M Belhaj, P Protopapas, W Pan
arXiv preprint arXiv:1812.03123, 2018
102018
Wide mean-field variational bayesian neural networks ignore the data
B Coker, W Pan, F Doshi-Velez
arXiv preprint arXiv:2106.07052, 2021
92021
Uncertainty-aware (una) bases for deep bayesian regression using multi-headed auxiliary networks
S Thakur, C Lorsung, Y Yacoby, F Doshi-Velez, W Pan
arXiv preprint arXiv:2006.11695, 2020
92020
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
MF Pradier, W Pan, J Yao, S Ghosh, F Doshi-Velez
arXiv preprint arXiv:1811.07006, 2018
92018
A characterization of the non-uniqueness of nonnegative matrix factorizations
W Pan, F Doshi-Velez
arXiv preprint arXiv:1604.00653, 2016
82016
Quality of Uncertainty Quantificatio n for Bayesian Neural Network Inference. arXiv
J Yao, W Pan, S Ghosh, F Doshi-Velez
arXiv preprint arXiv:1906.09686, 2019
72019
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations
Z Chen, V Subhash, M Havasi, W Pan, F Doshi-Velez
Workshop on Trustworthy and Socially Responsible Machine Learning, NeurIPS 2022, 2022
62022
Characterizing and avoiding problematic global optima of variational autoencoders
Y Yacoby, W Pan, F Doshi-Velez
Symposium on Advances in Approximate Bayesian Inference, 1-17, 2020
42020
Quality of uncertainty quantification for Bayesian neural network inference. arXiv 2019
J Yao, W Pan, S Ghosh, F Doshi-Velez
arXiv preprint arXiv:1906.09686, 0
4
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