Adversarial robustness is an increasingly critical property of classifiers in applications. The design of robust algorithms relies on surrogate losses since the optimization of the adversarial loss with most hypothesis sets is NP-hard. But, which surrogate losses should be used and when do they benefit from theoretical guarantees? We present an extensive study of this question, including a detailed analysis of the H-calibration and H-consistency of adversarial surrogate losses. We show that convex loss functions, or the supremum-based convex losses often used in applications, are not H-calibrated for common hypothesis sets used in machine learning.

Many unsupervised methods have recently been proposed for multivariate time series anomaly detection. However, existing works mainly focus on stable data yet often omit the drift generated from non-stationary environments, which may lead to numerous false alarms. We propose Dynamic Decomposition with Diffusion Reconstruction (D3R), a novel anomaly detection network for real-world unstable data to fill the gap. D3R tackles the drift via decomposition and reconstruction. In the decomposition procedure, we utilize data-time mix-attention to dynamically decompose long-period multivariate time series, overcoming the limitation of the local sliding window.

Recall that for the n-way multiple choice setting, n 1 choices are negative pairs and only one pair is positive. Accordingly, for n = 4, 3 distractors are sampled, each with an incorrect pose embedding, while the 4th choice contains the matching pose embedding for the given vision and audio embeddings. In other words, the fusion embedding consisting of the vision and audio embeddings is kept as the anchor while negatives are sampled from the pose embeddings only. Of the 3 negative pose embeddings, 2 are considered "easy" negatives, sampled randomly from the entire training set, while the last one is a "hard" negative, sampled randomly from a pool of 25 embeddings corresponding to the 25 nearest neighbours of the anchor vision embedding. In the n = 3case, 2 hard negatives and no easy negatives are used, with the same nearest neighbour sampling method based on the anchorshared weights embedding.