13 papers
One-Shot Federated Model Editing for Device Dynamics at the Edge
Youming Tao, Weijun Yin, Huanyi Xie +5
Standard federated learning (FL) requires persistent device connectivity across multiple communication rounds, making it unsuitable for dynamic environments such as vehicular netwo…
Predicting LLM Output Length via Entropy-Guided Representations
Huanyi Xie, Yubin Chen, Liangyu Wang +2
The long-tailed distribution of sequence lengths in LLM serving and reinforcement learning (RL) sampling causes significant computational waste due to excessive padding in batched…
Differentially Private Non-convex Distributionally Robust Optimization
Difei Xu, Meng Ding, Zebin Ma +4
Real-world deployments routinely face distribution shifts, group imbalances, and adversarial perturbations, under which the traditional Empirical Risk Minimization (ERM) framework…
Understanding the Impact of Differentially Private Training on Memorization of Long-Tailed Data
Jiaming Zhang, Huanyi Xie, Meng Ding +3
Recent research shows that modern deep learning models achieve high predictive accuracy partly by memorizing individual training samples. Such memorization raises serious privacy c…
PAHQ: Accelerating Automated Circuit Discovery through Mixed-Precision Inference Optimization
Xinhai Wang, Shu Yang, Liangyu Wang +4
Circuit discovery, which involves identifying sparse and task-relevant subnetworks in pre-trained language models, is a cornerstone of mechanistic interpretability. Automated Circu…
Attributing Data for Sharpness-Aware Minimization
Chenyang Ren, Yifan Jia, Huanyi Xie +5
Sharpness-aware Minimization (SAM) improves generalization in large-scale model training by linking loss landscape geometry to generalization. However, challenges such as mislabele…
DistZO2: High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing
Liangyu Wang, Huanyi Xie, Di Wang
Fine-tuning large language models (LLMs) remains resource-intensive due to their sheer scale. While zeroth-order (ZO) optimization provides a memory-efficient alternative by elimin…
Infinite Sampling: Efficient and Stable Grouped RL Training for Large Language Models
Liangyu Wang, Huanyi Xie, Xinhai Wang +3
Group-based reinforcement learning algorithms such as Group Reward Policy Optimization (GRPO) have proven effective for fine-tuning large language models (LLMs) with human feedback…
Efficient Text-Attributed Graph Learning through Selective Annotation and Graph Alignment
Huanyi Xie, Lijie Hu, Lu Yu +6
In the realm of Text-attributed Graphs (TAGs), traditional graph neural networks (GNNs) often fall short due to the complex textual information associated with each node. Recent me…
ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory
Liangyu Wang, Jie Ren, Hang Xu +4
Fine-tuning large pre-trained LLMs generally demands extensive GPU memory. Traditional first-order optimizers like SGD encounter substantial difficulties due to increased memory re…
Evaluating Data Influence in Meta Learning
Chenyang Ren, Huanyi Xie, Shu Yang +3
As one of the most fundamental models, meta learning aims to effectively address few-shot learning challenges. However, it still faces significant issues related to the training da…
Dissecting Representation Misalignment in Contrastive Learning via Influence Function
Lijie Hu, Chenyang Ren, Huanyi Xie +5
Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled…
Semi-supervised Concept Bottleneck Models
Lijie Hu, Tianhao Huang, Huanyi Xie +6
Concept Bottleneck Models (CBMs) have garnered increasing attention due to their ability to provide concept-based explanations for black-box deep learning models while achieving hi…