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The goal in label-imbalanced and group-sensitive classification is to optimize relevant metrics such as balanced error and equal opportunity.
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
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To understand deep learning we need to understand kernel learning
Mikhail Belkin, Siyuan Ma, and Soumik Mandal · 2018
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Empirical risk minimization under fairness constraints
Michele Donini, Luca Oneto, Shai Ben-David, John Shawe-Taylor, and Massimiliano Pontil · 2018
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Does distributionally robust supervised learning give robust classifiers?
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Risk and parameter convergence of logistic regression
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Cost-sensitive learning of deep feature representations from imbalanced data
Salman H. Khan, Munawar Hayat, Mohammed Bennamoun, Ferdous A. Sohel, and Roberto Togneri · 2018
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Focal loss for dense object detection, 2018
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Spectral algorithms for computing fair support vector machines
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The implicit bias of gradient descent on separable data
Benign overfitting in linear regression
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Provable benefits of overparameterization in model compression: From double descent to pruning neural networks, 2020
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The phase transition for the existence of the maximum likelihood estimate in high-dimensional logistic regression
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A precise performance analysis of learning with random features, 2020
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Daniel Soudry, Elad Hoffer, Mor Shpigel Nacson, Suriya Gunasekar, and Nathan Srebro · 2018
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Precise error analysis of regularized m m -estimators in high dimensions
Christos Thrampoulidis, Ehsan Abbasi, and Babak Hassibi · 2018
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Symbol error rate performance of box-relaxation decoders in massive mimo
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Additive margin softmax for face verification
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A continuous-time view of early stopping for least squares regression
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What is the effect of importance weighting in deep learning?
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Learning imbalanced datasets with label-distribution-aware margin loss
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Adjusting decision boundary for class imbalanced learning
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Analytic study of double descent in binary classification: The impact of loss
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Decoupling representation and classifier for long-tailed recognition, 2020
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A precise high-dimensional asymptotic theory for boosting and min-l1-norm interpolated classifiers
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Neural collapse with cross-entropy loss
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Optimizing millions of hyperparameters by implicit differentiation
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Long-tail learning via logit adjustment
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The role of regularization in classification of high-dimensional noisy gaussian mixture
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Neural collapse with unconstrained features, 2020
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Prevalence of neural collapse during the terminal phase of deep learning training
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An investigation of why overparameterization exacerbates spurious correlations
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Bbn: Bilateral-branch network with cumulative learning for long-tailed visual recognition, 2020
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Characterizing the implicit bias via a primal-dual analysis
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Phase transitions for one-vs-one and one-vs-all linear separability in multiclass gaussian mixtures
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