2 citations · 6 across the 12 of their papers we have counts for
13 papers
Quality Degradation Attack in Synthetic Data
Qinyi Liu, Dong Liu, Sam Urmian +2
Synthetic Data Generation (SDG) can be used to facilitate privacy-preserving data sharing. However, most existing research focuses on privacy attacks where the adversary is the rec…
From Betti Numbers to Persistence Diagrams: A Hybrid Quantum Algorithm for Topological Data Analysis
Dong Liu
Persistence diagrams serve as a core tool in topological data analysis, playing a crucial role in pathological monitoring, drug discovery, and materials design. However, existing q…
Fiber Bundle Networks: A Geometric Machine Learning Paradigm
Dong Liu
We propose Fiber Bundle Networks (FiberNet), a novel machine learning framework integrating differential geometry with machine learning. Unlike traditional deep neural networks rel…
Re-Key-Free, Risky-Free: Adaptable Model Usage Control
Zihan Wang, Zhongkui Ma, Xinguo Feng +6
Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their development. To protect these assets in…
TinyServe: Query-Aware Cache Selection for Efficient LLM Serving
Dong Liu, Yanxuan Yu
Serving large language models (LLMs) efficiently remains challenging due to the high memory and latency overhead of key-value (KV) cache access during autoregressive decoding. We p…
Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
Like Jian, Dong Liu
Federated learning (FL) enables decentralized clients to train a model collaboratively without sharing local data. A key distinction between FL and centralized learning is that cli…
QuickMerge++: Fast Token Merging with Autoregressive Prior
Dong Liu, Yanxuan Yu
As generative models scale to larger inputs across language, vision, and video domains, the cost of token-level computation has become a key bottleneck. While prior work suggests t…
PiKV: KV Cache Management System for Mixture of Experts
Dong Liu, Yanxuan Yu, Ben Lengerich +1
As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache storage has become a major bottleneck…
FastCache: Fast Caching for Diffusion Transformer Through Learnable Linear Approximation
Dong Liu, Yanxuan Yu, Jiayi Zhang +3
Diffusion Transformers (DiT) are powerful generative models but remain computationally intensive due to their iterative structure and deep transformer stacks. To alleviate this ine…
LLMEasyQuant: Scalable Quantization for Parallel and Distributed LLM Inference
Dong Liu, Yanxuan Yu
As large language models (LLMs) grow in size and deployment scale, quantization has become an essential technique for reducing memory footprint and improving inference efficiency.…
MT2ST: Adaptive Multi-Task to Single-Task Learning
Dong Liu, Yanxuan Yu
Efficient machine learning (ML) has become increasingly important as models grow larger and data volumes expand. In this work, we address the trade-off between generalization in mu…
GraphSnapShot: Caching Local Structure for Fast Graph Learning
Dong Liu, Roger Waleffe, Meng Jiang +1
In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for f…
Efficient Graph Optimization via Distance-Aware Graph Representation Learning
Dong Liu, Yanxuan Yu
We propose an efficient framework that integrates distance-aware multi-hop message passing with dynamic topology refinement. Unlike standard GNNs that rely on shallow, fixed-hop ag…