3 accepted papers
Multiple Instance Regression (MIR) and Learning from Label Proportions (LLP) are useful learning frameworks, where the training data is partitioned into disjoint sets or bags, and only an aggregate label, i.e., bag-label for each bag is available to the learner. In the case of MIR, the bag-label is
Fine-Tuning of LLMs using RLHF / RLAIF has been shown as a critical step to improve the performance of LLMs in complex generation tasks. These methods typically use response-level human or model feedback for alignment. Recent works indicate that finer sentence or span-level labels provide more accur…
In learning from aggregate labels, the training data consists of sets or “bags” of feature-vectors (instances) along with an aggregate label for each bag derived from the (usually $\\{0,1\\}$-valued) labels of its constituent instances. In *learning from label proportions* (LLP), the aggregate label