3 accepted papers
The problem of learning from long-tailed noisy data, referred to as Long-Tailed Noisy Label Learning (LTNLL), presents significant challenges in deep learning. LTNLL datasets are typically affected by two primary issues: class imbalance and label noise. While previous methods have addressed these pr…
Complementary label learning (CLL) is a weakly supervised learning paradigm that constructs a multi-class classifier only with complementary labels, specifying classes that the instance does not belong to. We reformulate CLL as an inverse problem that infers the full label information from the outpu…
Recent advancements in large language models (LLMs) have shown promise in generating psychotherapeutic dialogues, particularly in the context of motivational interviewing (MI). However, the inherent lack of transparency in LLM outputs presents significant challenges given the sensitive nature of psy…