6 accepted papers
Recent efforts to apply Large Language Models (LLMs) to time-series anomaly detection (TSAD) have yielded limited success, often performing worse than even simple methods. While prior work has focused solely on downstream performance evaluation, the fundamental question—why do LLMs struggle with TSA…
In class-imbalanced learning (CIL), post-hoc logit adjustment (LA) effectively mitigates class imbalance by adjusting biased logits according to label frequencies. Given the success of LA in CIL, recent class-imbalanced semi-supervised learning (CISSL) algorithms incorporated LA, leading to improved…
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecasting. While LLMs have shown potential in zero-shot forecasting through prompting alone, recent studies suggest that LLMs la…
Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a class-imbalanced set face two cascading challenges: 1) Classifiers tend to be biased towards majority classes and 2) Biased pseudo-labels are used for training. It is difficult to appropriately re-balance the classifiers in SS…
"Despite significant advancements in class-imbalanced semi-supervised learning (CISSL), many existing algorithms explicitly or implicitly assume that the class distribution of unlabeled data matches that of labeled data. However, when this assumption fails in practice, the classification performance…
Existing semi-supervised learning (SSL) algorithms typically assume class-balanced datasets, although the class distributions of many real world datasets are imbalanced. In general, classifiers trained on a class-imbalanced dataset are biased toward the majority classes. This issue becomes more prob…