Classical methods, such as weighted cross-entropy, fail when training deep nets to the terminal phase of training (TPT), that is training beyond zero training error.

Given an unsupervised novelty detection task on a new dataset, how can we automatically select a "best" detection model while simultaneously controlling the

While this may seem of mostly theoretical interest, in this work, we show that it can be used in practice to the benefit of BNNs.

The result of PC 2 vs. PC 3 (Figure 1) suggests that the first quadrant represents concepts describing

As proved by Feng et al. (2021), the binary cross-entropy loss We include more results on teacher model and teacher model + {DRO (Hashimoto et al., 2018) /ARL (Lahoti et al., 2020) / FairRF (Zhao et al., 2022) /our knowledge distillation} in Tab. Effect of our label smoothing can be observed by comparing between "Teacher (with hard label)" and "Teacher (with softmax/linear label)" in the 6 tables. Here the capacity is the same, the only difference is the label smoothing. Here the training method is the same, only difference is capacity. Table 8: Results on COMP AS dataset with sensitive attribute race .

Machine learning has been widely adopted in real-world scenarios that has great social influence, and fairness in automatic decision-making systems has become an arising concern.

This work develops central limit theorems for cross-validation and consistent estimators of its asymptotic variance under weak stability conditions on the learning algorithm. Together, these results provide practical, asymptotically-exact confidence intervals for k -fold test error and valid, powerful hypothesis tests of whether one learning algorithm has smaller k -fold test error than another. These results are also the first of their kind for the popular choice of leave-one-out cross-validation. In our real-data experiments with diverse learning algorithms, the resulting intervals and tests outperform the most popular alternative methods from the literature.