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Loss Functions

Cross-entropy (aka log loss, conditional log-likelihood, CRF loss)
- Lots of different ways to write this loss function. One way is minimize $L(\mathcal{D}) = -\sum_{i=1}^{N} log(p(y_i|x_i))$, where $p(y|x) = \frac{e^{score(x,y)}}{\sum_{y} e^{score(x,y)}}$, where $p(y|x) = \frac{e^{score(x,y)}}{\sum_{y} e^{score(x,y)}}$
- The cross-entropy version writes it as $L(\mathcal{D}) = -\sum_{i=1}^{N}\sum_{y} p(y|x_i) log(p_\theta(y|x_i))$, but usually we put in the empirical distribution $p(y|x_i) = I[y=y_i]$ which gives us the log-loss above.
- The minimum of cross-entropy loss does not always exist, and does not exist if the data training data can be completely separated. See for example, section 1.1 of this paper.
Perceptron loss \[ L(\theta,\mathcal{D}) = \sum_{(x_i,y_i)\in\mathcal{D}} \Big( -score_\theta(x_i,y_i) + \max_{y \in \mathcal{Y}(x_i)} score_\theta(x_i,y) \Big) \]
Hinge (SVM) loss
Softmax margin
- Gimple & Smith 2010 - Softmax-Margin CRFs: Training Log-Linear Models with Cost Functions
- Large-Margin Softmax Loss for Convolutional Neural Networks L-Softmax. Doesn't cite Gimple & Smith. I suspect it may be different, but need to check.
Ramp loss
Soft ramp loss
Infinite ramp loss
Squared error loss
- Hui & Belkin 2020 - Evaluation of Neural Architectures Trained with Square Loss vs Cross-Entropy in Classification Tasks
Squentropy (Cross-entropy + squared error)
- Hui et al 2023 - Cut your Losses with Squentropy

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