7 accepted papers
Code language models (LMs) represent valuable intellectual property (IP) as their training involves immense investments, including large-scale code corpora, proprietary annotations, extensive computational resources, and specialized designs. Hence the threat of model IP infringements such as unautho…
Current federated-learning models deteriorate under heterogeneous (non-I.I.D.) client data, as their feature representations diverge and pixel- or patch-level objectives fail to capture the global topology which is essential for high-dimensional visual tasks. We propose FedTopo, a framework that int
Conventional data compression methods typically model the information source as an i.i.d. stochastic process, thereby establishing the fundamental limit as entropy for lossless compression and as mutual information for lossy compression. However, the source in the real world (e.g., text, music, and…
Federated learning (FL) is a machine learning paradigm that aggregates knowledge and utilizes computational power from multiple participants to train a global model. However, a commonplace challenge—non-independent and identically distributed (non-i.i.d.) data across participants—can lead to signifi…
We investigate the entity alignment (EA) problem with unlabeled dangling cases, meaning that partial entities have no counterparts in the other knowledge graph (KG), yet these entities are unlabeled. The problem arises when the source and target graphs are of different scales, and it is much cheaper…
Revolutionizing the field of deep learning Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to shuffling but are limited to inter-token permutation in the forward propagation. In this work we propose our definition of…
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such adversarial inference, yet without too much accuracy degradation. We propose a generic method to revise the neural network…