2019年、俺の読んだ論文50本全部解説(俺的ベスト3付き) 綿岡晃輝 https://qiita.com/wataoka/items/ae782defabc3706b5c93
深層学習五十論文 by 綿岡晃輝 単語帳(shell, awk), 英語(25) docker(101) https://qiita.com/kaizen_nagoya/items/670d4d332e07fd2e5fc2
https://hub.docker.com/u/kaizenjapan $ docker run -v /tmp/docker:/tmp/docker -it kaizenjapan/qc-nakamori /bin/bash
BMI
Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) Defense Advanced Research Projects Agency (DARPA) Diversity in Faces (DiF) Equal Credit Opportunity Acts (ECOA) Fair Housing Acts(FHA) labeled faces in the wild (LFW) maximum mean discrepancy (MMD) price of fairness (POF) reducing bias amplification (RBA) Sentence Encoder Association Test (SEAT) Science, Technology, Engineering, and Math (STEM) Variational Auto Encoders(VAE) Word Embedding Association Test (WEAT)
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GPU
Batch Normalization (BN) NVIDIA Collective Communication Library (NCCL)3 stochastic gradient descent(SGD)
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Constant Uncertainty Dropout (CUDO). Continuous Ranked Probability Score (CRPS) cross-validation (CV) Monte Carlo Dropout (MCDO) Monte Carlo Batch Normalization (MCBN) Multiplicative Normalizing Flows for variational Bayesian networks (MNF) Log likelihood (PLL) variational inference (VI) Probabilistic backpropagation (PBP) Kullback-Leibler (KL)
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NGO BER
Disparate Impact(DI)
US Equal Employment Opportunity Commission (EEOC)
Logistic Regression(LR)
Regularized Logistic Regression(RLR)
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ResNets
Class-Balanced(CB) Loss Convolutional Neural Networks(CNNs) Softmax (SM) Sigmoid(SGM)
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correlation-based feature selection (CFS) cost-sensitive feature selection(CSFS) Information-Theoretic Feature Ranking (ITFR) Minimum Redundancy Maximum Relevance (mRMR) Multi-Label ReliefF (MLReliefF) Non-Convex Feature Learning (NCFS) Robust Feature Selection (RFS) support vector machine recursive feature elimination (SVM-RFE)
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Discrimination-Aware Ensemble (DAE) naive Bayes (NBS) Reject Option based Classification (ROC),
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Army Research Laboratory(ARL) Deep Learning (DL)
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CMC PRID CUHK
Canada Foundation for Innovation (CFI)
domain adaptation (DA)
Deep Metric Learning (DM)
Fonds de recherche du Quebec Nature et technologies (FRQNT)
gradient reversal layer (GRL)
marginalized Stacked Autoencoders (mSDA)
National Science and Engineering Research Council (NSERC)
Proxy A-distances (PAD)
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ERM
FERM
ACC
Equal Opportunity (EO)
Reproducing Kernel Hilbert Space (RKHS)
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RBF L-BFGS LSTMs MP-DBM
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16 Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
EU FEDER LSP LSPFS SMOTE USP
semi-supervised learning(SSL) Advancement of Artificial Intelligence (aaai) Missing Completely At Random (MCAR) Spanish Ministry of Economy and Competitiveness (MINECO) SUrvey Network for Deep Imaging Analysis and Learning (SUNDIAL)
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Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P. Gummadi. Fairness constraints: Mechanisms for fair classi cation. In Proceedings of the 20th International Conference on Arti cial Intelligence and Statistics, 2017.
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AALIM AIDS. CA DMNS ECGs HIV
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COLT ICML ITCS NIPS SIAM
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distributionally robust optimization (DRO) empirical risk minimization (ERM)
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#21 Focal Loss for Dense Object Detection
COCO HOG SSD YOLO
cross entropy (CE) Focal Loss (FL) Feature Pyramid Network (FPN) online hard example mining (OHEM)
Facebook AI Research (FAIR)
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#22 Generative Adversarial Minority Oversampling
Conditional Transient Mapping Unit (cTMU) deep oversampling framework (DOS) Generative Adversarial Minority Oversampling (GAMO) Generative adversarial networks (GANs)
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LeNet ? VGG-11 Fast Gradient Sign Method (FGSM) Iterative FGSM (I-FGSM)
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#34 On Learning Density Aware Embeddings
3D, three dimensions
Density Aware Quadruplet Loss (DAQL) Density Aware Triplet Loss (DATL) Kernel Density Estimate (KDE)
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#35 On the Legal Comparability of Fairness Definitions
Housing and Urban Development (HUD).
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#36 One-network Adversarial Fairness
Fair adversarial discriminative (FAD) minibatch diversity(MD) maximum mean discrepancy (MMD)
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Feldman, M.; Friedler, S.; Moeller, J.; Scheidegger, C.; and Venkatasubramanian, S. 2015. Certifying and removing disparate impact. KDD. Fish, B.; Kun, J.; and Lelkes, A. 2016. A confidence-based approach for balancing fairness and accuracy. SDM. Friedler, S.; Scheidegger, C.; Venkatasubramanian, S.; Choudhary, S.; Hamilton, E.; and Roth, D. 2018. A comparative study of fairness-enhancing interventions in machine learning. arXiv preprint arXiv:1802.04422. Ganin, Y., and Lempitsky, V. 2015. Unsupervised domain adaptation by backpropagation. ICML 32. Ganin, Y.; Ustinova, E.; Ajakan, H.; Germain, P.; Larochelle, H.; Laviolette, F.; Marchand, M.; and Lempitsky, V. 2016. Domainadversarial training of neural networks. JMLR 17(59):1–35. Goel, S.; Rao, J.; and Shroff, R. 2015. Precinct or prejudice? Understanding racial disparities in New York City’s stop-and-frisk policy. Annals of Applied Statistics. Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde- Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014. Generative adversarial nets. NIPS 2672–2680. Goodfellow, I. 2016. NIPS 2016 tutorial: Generative adversarial networks. arXiv preprint arXiv:1701.00160. Gretton, A.; Borgwardt, K.; Rasch, M.; Scholkopf, B.; and Smola, A. 2006. A kernel method for the two-sample-problem. NIPS. Gretton, A.; Borgwardt, K.; Rasch, M.; Scholkopf, B.; and Smola, A. 2012. A kernel two-sample test. JMLR 13. Grgic-Hlaca, N.; Redmiles, E.; Gummadi, K.; and Weller, A. 2018a. Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction. WWW. Grgic-Hlaca, N.; Zafar, M.; Gummadi, K.; and Weller, A. 2018b. Beyond distributive fairness in algorithmic decision making: Feature selection for procedurally fair learning. AAAI. Hajian, S.; Domingo-Ferrer, J.; Monreale, A.; Pedreschi, D.; and Giannotti, F. 2015. Discrimination and privacy-aware patterns. Data Mining and Knowledge Discovery 1733–1782. Hardt, M.; Price, E.; and Srebro, N. 2016. Equality of opportunity in supervised learning. NIPS. Kamishima, T.; Akaho, S.; Asoh, H.; and Sakuma, J. 2012. Fairness-aware classifier with prejudice remover regularizer. Kearns, M.; Neel, S.; Roth, A.; and Wu, Z. 2018. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. ICML. Khandani, A.; Kim, A.; and Lo, A. 2010. Consumer credit-risk models via machine-learning algorithms. JBF 34:2767–2787. Kifer, D.; Ben-David, S.; and Gehrke, J. 2004. Detecting change in data streams. VLDB 180–191. Kim, T.; Cha, M.; Kim, H.; Lee, J.; and Kim, J. 2017. Learning to discover cross-domain relations with generative adversarial networks. ICML. Kingma, D., and Ba, J. 2015. Adam: A Method for Stochastic Optimization. ICLR. Koltchinskii, V., and Panchenko, D. 2000. Rademacher processes and bounding the risk of function learning. HDP. Komiyama, J.; Takeda, A.; Honda, J.; and Shimao, H. 2018. Nonconvex optimization for regression with fairness constraints. ICML. Kusner, M.; Loftus, J.; Russell, C.; and Silva, R. 2017. Counterfactual fairness. NIPS. Larson, J.; Mattu, S.; Kirchner, L.; and Angwin, J. 2016. https://github.com/propublica/compas-analysis. Lloyd, J., and Ghahramani, Z. 2015. Statistical model criticism using kernel two sample tests. NIPS. Louizos, C.; Swerky, K.; Li, Y.; Welling, M.; and Zemel, R. 2016. The variational fair autoencoder. ICLR. Louppe, G.; Kagan, M.; and Cranmer, K. 2017. Learning to pivot with adversarial networks. NIPS. Madras, D.; Creager, E.; Pitassi, T.; and Zemel, R. 2018. Learning adversarially fair and transferable representations. ICML. Mansour, Y.; Mohri, M.; and Rostamizadeh, A. 2009. Domain adaptation: Learning bounds and algorithms. COLT. Metz, L.; Poole, B.; Pfau, D.; and Sohl-Dickstein, J. 2017. Unrolled generative adversarial networks. ICLR. Mohri, M., and Afshin, R. 2008a. Rademacher complexity bounds for non-IID processes. NIPS. Narasimhan, H. 2018. Learning with complex loss functions and constraints. AISTATS 1646–1654. Primus, R. 2010. The future of disparate impact. Mich. Law Rev. Rosca, M.; Lakshminarayanan, B.; Warde-Farley, D.; and Mohamed, S. 2017. Variational approaches for auto-encoding generative adversarial networks. arXiv preprint arXiv:1706.04987. Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; and Radford, A. 2016. Improved techniques for training GANs. NIPS. Scholkopf, B.; Williamson, R.; Smola, A.; Shawe-Taylor, J.; and Platt, J. 2000. Support vector method for novelty detection. NIPS. Shalev-Shwartz, S., and Ben-David, S. 2014. Understanding machine learning: From theory to algorithms. Cambridge Univ. Press. Srivastava, A.; Valkov, L.; Russell, C.; Gutmann, M.; and Sutton, C. 2017. VEEGAN: Reducing mode collapse in GANs using implicit variational learning. NIPS. Wadsworth, C.; Vera, F.; and Piech, C. 2018. Achieving fairness through adversarial learning: an application to recidivism prediction. FAT/ML Workshop. Xu, D.; Yuan, S.; Zhang, L.; and Wu, X. 2018. FairGAN: Fairness-aware generative adversarial networks. arXiv preprint arXiv:1805.11202. Zafar, M.; Valera, I.; Rodriguez, M.; Gummadi, K.; and Weller, A. 2017a. From parity to preference-based notions of fairness in classification. NIPS. Zafar, M.; Valera, I.; Rodriguez, M.; and Gummadi, K. 2017b. Fairness beyond disparate treatment and disparate impact: Learning classification without disparate mistreatment. WWW. Zafar, M.; Valera, I.; Rodriguez, M.; and Gummadi, K. 2017c. Fairness constraints: Mechanisms for fair classification. AISTATS. Zemel, R.; Wu, Y.; Swersky, K.; Pitassi, T.; and Dwork, C. 2013. Learning fair representations. ICML 325–333. Zhang, B.; Lemoine, B.; and Mitchell, M. 2018. Mitigating unwanted biases with adversarial learning. arXiv:1801.07593.
#37 Online Learning with an Unknown Fairness Metric
xReferences Yasin Abbasi-Yadkori, D´avid P´al, and Csaba Szepesv´ari. Improved algorithms for linear stochastic bandits. In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain., pages 2312–2320, 2011. URL http://papers.nips.cc/paper/4417-improved-algorithms-for-linear-stochastic-bandits. Richard Berk, Hoda Heidari, Shahin Jabbari,Michael Kearns, and Aaron Roth. Fairness in criminal justice risk assessments: the state of the art. arXiv preprint arXiv:1703.09207, 2017. Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. arXiv preprint arXiv:1703.00056, 2017. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214–226. ACM, 2012. Sorelle A Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. On the (im) possibility of fairness. arXiv preprint arXiv:1609.07236, 2016. Sara Hajian and Josep Domingo-Ferrer. A methodology for direct and indirect discrimination prevention in data mining. IEEE transactions on knowledge and data engineering, 25(7):1445– 1459, 2013. Moritz Hardt, Eric Price, and Nathan Srebro. Equality of opportunity in supervised learning. Advances in Neural Information Processing Systems, 2016. Ursula H´ebert-Johnson, Michael P Kim, Omer Reingold, and Guy N Rothblum. Calibration for the (computationally-identifiable) masses. arXiv preprint arXiv:1711.08513, 2017. Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American statistical association, 58(301):13–30, 1963. Shahin Jabbari, Matthew Joseph, Michael Kearns, Jamie Morgenstern, and Aaron Roth. Fairness in reinforcement learning. In International Conference on Machine Learning, pages 1617–1626, 2017. Prateek Jain, Brian Kulis, Inderjit S Dhillon, and Kristen Grauman. Online metric learning and fast similarity search. In Advances in neural information processing systems, pages 761–768, 2009. Matthew Joseph, Michael Kearns, Jamie H Morgenstern, and Aaron Roth. Fairness in learning: Classic and contextual bandits. pages 325–333, 2016a. 22 Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. Fair algorithms for infinite and contextual bandits. CoRR, abs/1610.09559, 2016b. URL http://arxiv.org/abs/1610.09559. Faisal Kamiran and Toon Calders. Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1):1–33, 2012. Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei StevenWu. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. arXiv preprint arXiv:1711.05144, 2017. Michael P Kim, Omer Reingold, and Guy N Rothblum. Fairness through computationallybounded awareness. arXiv preprint arXiv:1803.03239, 2018. Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. In Proceedings of the 2017 ACM Conference on Innovations in Theoretical Computer Science, Berkeley, CA, USA, 2017, 2017. Brian Kulis et al. Metric learning: A survey. Foundations and Trends® in Machine Learning, 5(4): 287–364, 2013. Yang Liu, Goran Radanovic, Christos Dimitrakakis, Debmalya Mandal, and David C Parkes. Calibrated fairness in bandits. arXiv preprint arXiv:1707.01875, 2017. Ilan Lobel, Renato Paes Leme, and Adrian Vladu. Multidimensional binary search for contextual decision-making. In Proceedings of the 2017 ACM Conference on Economics and Computation, EC ’17, Cambridge, MA, USA, June 26-30, 2017, page 585, 2017. doi: 10.1145/3033274.3085100. URL http://doi.acm.org/10.1145/3033274.3085100. Guy N Rothblum and Gal Yona. Probably approximately metric-fair learning. arXiv preprint arXiv:1803.03242, 2018. Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of the 26th International Conference on World Wide Web, pages 1171–1180. InternationalWorldWide Web Conferences Steering Committee, 2017. Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork. Learning fair representations. In International Conference on Machine Learning, pages 325–333, 2013.
#38 Oversampling for Imbalanced Data via Optimal Transport
ADASYN DANGER MWMOTE GRF HKRGC LIBSVM1, https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
Optimal Transport for OverSampling (OTOS) true positive (TP) false positive (FP) false negative (FN) true negative (TN)
References Abdi, L., and Hashemi, S. 2016. To combat multi-class imbalanced problems by means of over-sampling techniques. IEEE Transactions on Knowledge and Data Engineering 28(1):238–251. Arjovsky, M.; Chintala, S.; and Bottou, L. 2017. Wasserstein generative adversarial networks. In ICML, 214–223. Barua, S.; Islam, M. M.; Yao, X.; and Murase, K. 2014. MWMOTE–majority weighted minority oversampling technique for imbalanced data set learning. IEEE Transactions on Knowledge and Data Engineering 26(2):405–425. Bellinger, C.; Drummond, C.; and Japkowicz, N. 2018. Manifoldbased synthetic oversampling with manifold conformance estimation. Machine Learning 107(3):605–637. Benamou, J.-D.; Carlier, G.; Cuturi, M.; Nenna, L.; and Peyr´e, G. 2015. Iterative bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing 37(2):A1111– A1138. Bhattacharya, S.; Rajan, V.; and Shrivastava, H. 2017. Icu mortality prediction: A classification algorithm for imbalanced datasets. In AAAI, 1288–1294. Branco, P.; Torgo, L.; and Ribeiro, R. P. 2016. A survey of predictive modeling on imbalanced domains. ACM Computing Surveys 49(2):31. Chawla, N. V.; Bowyer, K. W.; Hall, L. O.; and Kegelmeyer, W. P. 2002. SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–357. Courty, N.; Flamary, R.; Habrard, A.; and Rakotomamonjy, A. 2017a. Joint distribution optimal transportation for domain adaptation. In NIPS, 3733–3742. Courty, N.; Flamary, R.; Tuia, D.; and Rakotomamonjy, A. 2017b. Optimal transport for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(9):1853–1865. Cuturi, M., and Doucet, A. 2014. Fast computation of wasserstein barycenters. In ICML, 685–693. Cuturi, M. 2013. Sinkhorn distances: Lightspeed computation of optimal transport. In NIPS, 2292–2300. Das, B.; Krishnan, N. C.; and Cook, D. J. 2015. Racog and wracog: Two probabilistic oversampling techniques. IEEE Transactions on Knowledge and Data Dngineering 27(1):222. Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009. Imagenet: A large-scale hierarchical image database. In CVPR, 248–255. Fan, R.-E.; Chang, K.-W.; Hsieh, C.-J.; Wang, X.-R.; and Lin, C.- J. 2008. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research 9(Aug):1871–1874. Fern´andez, A.; Garcia, S.; Herrera, F.; and Chawla, N. V. 2018. SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. Journal of Artificial Intelligence Research 61:863–905. Gonz´alez, S.; Garc´ıa, S.; Li, S.-T.; and Herrera, F. 2019. Chain based sampling for monotonic imbalanced classification. Information Sciences 474:187–204. Han, H.; Wang,W.-Y.; and Mao, B.-H. 2005. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In ICIC, 878–887. He, H., and Garcia, E. A. 2008. 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The American Mathematical Monthly 74(4):402–405. Villani, C. 2008. Optimal transport: old and new, volume 338. Springer Science & Business Media. Yan, Y.; Li,W.;Wu, H.; Min, H.; Tan, M.; andWu, Q. 2018. Semisupervised optimal transport for heterogeneous domain adaptation. In IJCAI, 737–753. Zhang, Y.; Zhao, P.; Cao, J.; Ma, W.; Huang, J.; Wu, Q.; and Tan, M. 2018. Online adaptive asymmetric active learning for budgeted imbalanced data. In SIGKDD, 2768–2777.
#39 Practical Black-Box Attacks against Machine Learning
API
REFERENCES [1] Marco Barreno, et al. Can machine learning be secure? In Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security. [2] Battista Biggio, et al. Evasion attacks against machine learning at test time. In Machine Learning and Knowledge Discovery in Databases, pages 387{402. Springer, 2013. [3] Ian Goodfellow, et al. Deep learning. Book in preparation for MIT Press (www.deeplearningbook.org), 2016. [4] Ian J Goodfellow, et al. Explaining and harnessing adversarial examples. In Proceedings of the International Conference on Learning Representations, 2015. [5] Ling Huang, et al. Adversarial machine learning. In Proceedings of the 4th ACM workshop on Security and arti cial intelligence, pages 43{58, 2011. [6] Alexey Kurakin, et al. Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533, 2016. [7] Yann LeCun et al. The mnist database of handwritten digits, 1998. [8] Erich L. Lehmann, et al. Testing Statistical Hypotheses. Springer Texts in Statistics, August 2008. [9] Nicolas Papernot, et al. The limitations of deep learning in adversarial settings. In Proceedings of the 1st IEEE European Symposium on Security and Privacy, 2016. [10] Nicolas Papernot, et al. Distillation as a defense to adversarial perturbations against deep neural networks. In Proceedings of the 37th IEEE Symposium on Security and Privacy. [11] Mahmood Sharif, et al. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2016. [12] Nedim Srndic, et al. Practical evasion of a learning-based classi er: A case study. In Proceeding of the 35th IEEE Symposium on Security and Privacy. [13] Johannes Stallkamp, et al. Man vs. computer: Benchmarking machine learning algorithms for tra�c sign recognition. Neural networks, 32:323{332, 2012. [14] Christian Szegedy, et al. Intriguing properties of neural networks. In Proceedings of the International Conference on Learning Representations, 2014. [15] Florian Tram�er, et al. Stealing machine learning models via prediction apis. In 25th USENIX Security Symposium, 2016. [16] Je�rey S Vitter. Random sampling with a reservoir. ACM Transactions on Mathematical Software, 1985. [17] D Warde-Farley, et al. Adversarial perturbations of deep neural networks. Advanced Structured Prediction, 2016. [18] Weilin Xu, et al. Automatically evading classi ers. In Proceedings of the 2016 Network and Distributed Systems Symposium.
#40 Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
decision-maker’s (DM’s) minimum subgroup accuracy (MSA)
References [1] Josh Attenberg, Panagiotis G Ipeirotis, and Foster J Provost. Beat the machine: Challenging workers to find the unknown unknowns. Human Computation, 11(11), 2011. [2] Yahav Bechavod and Katrina Ligett. Learning Fair Classifiers: A Regularization- Inspired Approach. Workshop on Fairness, Accountability, and Transparency in Machine Learning, June 2017. arXiv: 1707.00044.
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#41 PyTorch: An Imperative Style, High-Performance Deep Learning Library
APL CNTK NumPy SciPy Pandas FIFO CPU
References [1] Yangqing "Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, and Trevor" Darrell. "caffe: Convolutional architecture for fast feature embedding". "arXiv preprint arXiv:1408.5093", "2014". [2] Frank Seide and Amit Agarwal. Cntk: Microsoft’s open-source deep-learning toolkit. In Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 2135–2135, New York, NY, USA, 2016. ACM. 9 [3] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. 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#42 Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?
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#43 SphereFace: Deep Hypersphere Embedding for Face Recognition
Labeled Face in the Wild (LFW)
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#44 Striking the Right Balance with Uncertainty
CFP, Celebrities in Frontal Profile Balanced Classification Accuracy (BCA) uncertainty-based margin (UMM) sample-level uncertainty modeling (SUM).
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#45 The Cost of Fairness in Binary Classification
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#46 The Limitations of Deep Learning in Adversarial Settings
SPAM
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#47 Trainable Undersampling for Class-Imbalance Learning
AUCPRC ALLKNN
area under the curve (AUC) Diabetic Retinopathy (DR) Evolutionary Undersampling (EUS) geometric mean (GM) gated recurrent unit (GRU) k-nearest neighbor (KNN) Matthews correlation coefficient (MCC) Markov decision process (MDP) Principle Component Analysis (PCA) random majority undersampling (RUS)
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#48 Transfer of Machine Learning Fairness across Domains
false positive rates (FPR) false negative rate (FNR)
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#49 Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations
Maximal Marginal Relevance (MMR) Fairness Maximal Marginal Relevance (FMMR)
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#50 Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
local distributional smoothness (LDS) random perturbation training (RPT) Street View House Numbers (SVHN) virtual adversarial training (VAT) virtual adversarial examples (VAEs)
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