1 citations · 2 across the 6 of their papers we have counts for
7 papers
Finite Sample Analyses for Continuous-time Linear Systems: System Identification and Online Control
Hongyi Zhou, Jingwei Li, Jingzhao Zhang
Real world evolves in continuous time but computations are done from finite samples. Therefore, we study algorithms using finite observations in continuous-time linear dynamical sy…
NeuralDB: Scaling Knowledge Editing in LLMs to 100,000 Facts with Neural KV Database
Weizhi Fei, Hao Shi, Jing Xu +7
Efficiently editing knowledge stored in large language models (LLMs) enables model updates without large-scale training. One possible solution is Locate-and-Edit (L\&E), allowing s…
Understanding Nonlinear Implicit Bias via Region Counts in Input Space
Jingwei Li, Jing Xu, Zifan Wang +2
One explanation for the strong generalization ability of neural networks is implicit bias. Yet, the definition and mechanism of implicit bias in non-linear contexts remains little…
Scalable Model Merging with Progressive Layer-wise Distillation
Jing Xu, Jiazheng Li, Jingzhao Zhang
Model merging offers an effective way to integrate the capabilities of multiple fine-tuned models. However, the performance degradation of the merged model remains a challenge, par…
Random Masking Finds Winning Tickets for Parameter Efficient Fine-tuning
Jing Xu, Jingzhao Zhang
Fine-tuning large language models (LLM) can be costly. Parameter-efficient fine-tuning (PEFT) addresses the problems by training a fraction of the parameters, whose success reveals…
Towards Black-Box Membership Inference Attack for Diffusion Models
Jingwei Li, Jing Dong, Tianxing He +1
Given the rising popularity of AI-generated art and the associated copyright concerns, identifying whether an artwork was used to train a diffusion model is an important research t…
Iteratively Learn Diverse Strategies with State Distance Information
Wei Fu, Weihua Du, Jingwei Li +3
In complex reinforcement learning (RL) problems, policies with similar rewards may have substantially different behaviors. It remains a fundamental challenge to optimize rewards wh…