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Areas under ROC (AUROC) and precision-recall curves (AUPRC) are common metrics for evaluating classification performance for imbalanced problems.
The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
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Auc optimization vs. error rate minimization
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Optimising area under the ROC curve using gradient descent
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A support vector method for multivariate performance measures
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The Relationship Between Precision-Recall and ROC Curves
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Gleaner: Creating ensembles of firstorder clauses to improve recall-precision curves
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Fast objective and duality gap convergence for non-convex strongly-concave min-max problems
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Learning to rank with nonsmooth cost functions
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A support vector method for optimizing average precision
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A general approximation framework for direct optimization of information retrieval measures
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Ranking measures and loss functions in learning to rank
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Attentional biased stochastic gradient for imbalanced classification
Qi, Q., Xu, Y., Jin, R., Yin, W., and Yang, T · 2012
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Area under the precision-recall curve: Point estimates and confidence intervals
Boyd, K., Eng, K. H., and Page, C. D · 2013
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One-pass auc optimization
Gao, W., Jin, R., Zhu, S., and Zhou, Z.-H · 2013
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Efficient optimization for average precision svm
Mohapatra, P., Jawahar, C., and Kumar, M. P · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Training deep neural networks via direct loss minimization
Song, Y., Schwing, A., Richard, and Urtasun, R · 2016
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Stochastic online auc maximization
Ying, Y., Wen, L., and Lyu, S · 2016
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Scalable learning of non-decomposable objectives
Eban, E., Schain, M., Mackey, A., Gordon, A., Saurous, R. A., and Elidan, G · 2017
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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End-to-end training of object class detectors for mean average precision
Henderson, P. and Ferrari, V · 2017
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Semi-supervised classification with graph convolutional networks
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Finite-sum composition optimization via variance reduced gradient descent
Lian, X., Wang, M., and Liu, J · 2017
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Focal loss for dense object detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Dollár, P · 2017
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Yu, Y. and Huang, L · 2017
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Optimizing generalized rate metrics with three players
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How powerful are graph neural networks?
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A composite randomized incremental gradient method
Zhang, J. and Xiao, L · 2019
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Stochastic multi-level composition optimization algorithms with level-independent convergence rates
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Smooth-ap: Smoothing the path towards large-scale image retrieval
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Ap-loss for accurate one-stage object detection
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Large-scale learnable graph convolutional networks
Gao, H., Wang, Z., and Ji, S · 2018
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Accelerated method for stochastic composition optimization with nonsmooth regularization
Huo, Z., Gu, B., Liu, J., and Huang, H · 2018
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Improved oracle complexity for stochastic compositional variance reduced gradient
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Efficient optimization for rank-based loss functions
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Stochastic proximal algorithms for auc maximization
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MoleculeNet: a benchmark for molecular machine learning
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Stochastic auc maximization with deep neural networks
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A ranking-based, balanced loss function unifying classification and localisation in object detection
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A simple and effective framework for pairwise deep metric learning
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A patient-centric dataset of images and metadata for identifying melanomas using clinical context
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A deep learning approach to antibiotic discovery
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Optimal epoch stochastic gradient descent ascent methods for min-max optimization
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Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization
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Soap code for reproducing results
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An online method for a class of distributionally robust optimization with non-convex objectives
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Advanced graph and sequence neural networks for molecular property prediction and drug discovery, 2021
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Stochastic constrained dro with a complexity independent of sample size
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