hinge loss
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A Unified Framework for Rank-based Loss Minimization
The empirical risk function is the cornerstone of machine learning.
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6e73c39cc428c7d264d9820319f31e79-Paper-Conference.pdf
We study benign overfitting in two-layer ReLU networks trained using gradient descent and hinge loss on noisy data for binary classification.
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6e2986deda273d8fb903342841fcc4dc-Paper-Conference.pdf
These kinds of attacks are known as poisoning attacks .
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Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation
Neural Information Processing Systems http://nips.cc/
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Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
Dylan J. Foster, Akshay Krishnamurthy
Neural Information Processing Systems http://nips.cc/
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Minimizers of the Empirical Risk and Risk Monotonicity
Marco Loog, Tom Viering, Alexander Mey
Neural Information Processing Systems http://nips.cc/
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