Ximing Li

Estimating and Orthogonalizing Unknown Pre-training Gradients for Continual Fine-tuning of Large Language Models

Continual fine-tuning is essential for large language models (LLMs) to dynamically adapt to real-world environments, yet it inevitably suffers from catastrophic forgetting, particularly the performance degradation of previous tasks and LLMs'general-purpose knowledge. Although existing methods, such as orthogonal gradie...

Label Confidence Recovery with High-order Label Correlation in Partial Multi-label Learning

Toward Robust Weakly Supervised Text Classification: Weak Supervision Generation and Correlation-Aware Supervision Propagation.

A novel weakly supervised (WS) learning MLTC framework consisting of a novel category word selection method, namely category word selection with significance ranking and crowd-sourcing (Cws-src), and a generic WS learning MLTC method, namely WS multilabel text classification with correlation-aware label propagation (Wm...

Weakly-Supervised Learning with Partial Multi-Labels by Leveraging Dual Label Correlation Perspectives

A novel PML method, namely Wasserstein Partial Multi-Label Learning with dual Label Correlation Perspectives (Wpml3cp), solved by the gradient descent with an augmented Lagrange multiplier technique, and empirical results demonstrate that Wpml3cp and Wpml3cp-D can outperform the PML baselines in various noisy levels.

A Closer Look to Positive-Unlabeled Learning from Fine-grained Perspectives: An Empirical Study

A comprehensive study to investigate the basic characteristics of current Positive-Unlabeled learning methods and proposes a general framework of PU learning by integrating the set-aware empirical risk with pseudo-labeling.