Proof of Lemma 5. Suppose M is a matrix then we can write M = UV

Proof of Lemma 5. Suppose M is a matrix then we can write M = UV

Privacy concerns in machine learning are heightened by regulations such as the GDPR, which enforces the "right to be forgotten" (RTBF), driving the emergence of machine unlearning as a critical research field. Vertical Federated Learning (VFL) enables collaborative model training by aggregating a sample's features across distributed parties while preserving data privacy at each source. This paradigm has seen widespread adoption in healthcare, finance, and other privacy-sensitive domains. However, existing VFL systems lack robust mechanisms to comply with RTBF requirements, as unlearning methodologies for VFL remain underexplored. In this work, we introduce the first VFL framework with theoretically guaranteed unlearning capabilities, enabling the removal of any data at any time. Unlike prior approaches -- which impose restrictive assumptions on model architectures or data types for removal -- our solution is model- and data-agnostic, offering universal compatibility. Moreover, our framework supports asynchronous unlearning, eliminating the need for all parties to be simultaneously online during the forgetting process. These advancements address critical gaps in current VFL systems, ensuring compliance with RTBF while maintaining operational flexibility.We make all our implementations publicly available at https://github.com/wangln19/vertical-federated-unlearning.

Partial label learning (PLL) is a complicated weakly supervised multi-classification task compounded by class imbalance. Currently, existing methods only rely on inter-class pseudo-labeling from inter-class features, often overlooking the significant impact of the intra-class imbalanced features combined with the inter-class. To address these limitations, we introduce Granular Ball Representation for Imbalanced PLL (GBRIP), a novel framework for imbalanced PLL. GBRIP utilizes coarse-grained granular ball representation and multi-center loss to construct a granular ball-based nfeature space through unsupervised learning, effectively capturing the feature distribution within each class. GBRIP mitigates the impact of confusing features by systematically refining label disambiguation and estimating imbalance distributions. The novel multi-center loss function enhances learning by emphasizing the relationships between samples and their respective centers within the granular balls. Extensive experiments on standard benchmarks demonstrate that GBRIP outperforms existing state-of-the-art methods, offering a robust solution to the challenges of imbalanced PLL.

The weather forecasting system is important for science and society, and significant achievements have been made in applying artificial intelligence (AI) to medium-range weather forecasting. However, existing AI-based weather forecasting models still rely on analysis or reanalysis products from the traditional numerical weather prediction (NWP) systems as initial conditions for making predictions, preventing them from being fully independent systems. As a crucial component of an end-to-end global weather forecasting system, data assimilation is vital in generating initial states for forecasting. In this paper, we present an AI-based data assimilation model, i.e., Adas, for global weather variables, which learns to generate the analysis from the background and sparse observations. Different from existing assimilation methods, Adas employs the gated convolution module to handle sparse observations and the gated cross-attention module for capturing the interactions between observations and background efficiently, which are guided by the confidence matrix to represent the availability and quality of observations. Then, we combine Adas with the advanced AI-based weather forecasting model (i.e., FengWu) and construct the first end-to-end AI-based global weather forecasting system: FengWu-Adas. Experiments demonstrate that Adas can assimilate the simulated global observations with the AI-generated background through a one-year simulation and generate high-quality analysis stably in a cyclic manner. Based on the generated analysis, FengWu-Adas exhibits skillful performance and outperforms the Integrated Forecasting System (IFS) in weather forecasting over seven days.

The matrix completion problem aims to reconstruct a low-rank matrix based on a revealed set of possibly noisy entries. Prior works consider completing the entire matrix with generalization error guarantees. However, the completion accuracy can be drastically different over different entries. This work establishes a new framework of partial matrix completion, where the goal is to identify a large subset of the entries that can be completed with high confidence. We propose an efficient algorithm with the following provable guarantees. Given access to samples from an unknown and arbitrary distribution, it guarantees: (a) high accuracy over completed entries, and (b) high coverage of the underlying distribution. We also consider an online learning variant of this problem, where we propose a low-regret algorithm based on iterative gradient updates. Preliminary empirical evaluations are included.

Classical object detection frameworks lack of utilizing objects' surrounding information. In this article, we introduce the graph convolutional networks (GCN) into the object detection, and propose a new framework called OD-GCN (object detection with graph convolutional network). It utilizes the category relationship to improve the detection precision. We set up a knowledge graph to reflect the co-exist relationships among objects. GCN plays the role of post-processing to adjust the output of base object detection models. It is a flexible framework that any pre-trained object detection models can be used as the base model. In the experiments, we try several popular base detection models, OD-GCN always improve mAP by 1-5 pp in COCO dataset. In addition, visualized analysis reveals the benchmark improvement is quite logical in human's opinion.