Multi-instance partial-label learning (MIPL) is a paradigm where each training example is encapsulated as a multi-instance bag associated with the candidate label set, which includes one true label and several false positives. Current MIPL algorithms typically assume that all instances are independent, thereby neglecting the dependencies and heterogeneity inherent in MIPL data. Moreover, these algorithms often prove to be excessively time-consuming when dealing with complex datasets, significantly limiting the practical application of MIPL. In this paper, we propose FastMIPL, a framework that employs mixed-effects model to explicitly capture the dependencies and heterogeneity among instances and bags. FastMIPL is able to learn from MIPL data both effectively and efficiently by utilizing the predefined dependencies modeling module and leveraging the posterior predictive probability disambiguation strategy. Experiments show that the performance of FastMIPL is highly competitive to state-of-the-art methods, while significantly reducing computational time in benchmark and the real-world datasets.

Multi-instance partial-label learning (MIPL) tackles scenarios where each training sample is represented as a multiinstance bag associated with a candidate label set. This set contains one true label and several false positives. Existing MIPL algorithms have predominantly focused on mapping multiinstance bags to candidate label sets for disambiguation. However, these algorithms may not be adequately generalizable in intricate real-world situations due to their reliance on heuristic methods for identifying true labels. In this paper, we propose PROMIPL, i.e., a PRObabilistic generative model for Multi-instance partiallabel learning, to address these challenges. PROMIPL is the first attempt to explore the probabilistic generative model to infer latent ground-truth labeling information from the data generation process in multi-instance partial-label learning. Besides, the discovered underlying structures also provide improved explanations of the classification predictions. To circumvent the computationally intensive process of training the generative model, we formulate a unified variational lower bound within the stochastic gradient variational Bayesian framework for the model parameters. Experimental results from benchmark and realworld datasets show that our proposed PROMIPL is competitive or superior to the state-of-the-art methods.

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.