53 citations · 113 across the 15 of their papers we have counts for
15 papers
Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making
Guangfeng Cai, Kaibing Yang, Shuo He +4
Recent studies on world modeling for Large Language Model (LLM) agents typically formulate the learning objective as next-observation prediction. However, this objective ties super…
A Unified Framework for Knowledge Transfer in Bidirectional Model Scaling
Jianlu Shen, Fu Feng, Jiaze Xu +3
Transferring pre-trained knowledge from a source model to a target model of a different architectural size is a key challenge for flexible and efficient model scaling. However, cur…
iScript: A Domain-Adapted Large Language Model and Benchmark for Physical Design Tcl Script Generation
Ning Xu, Zhaoyang Zhang, Senlin Shu +10
Modern EDA flows rely heavily on Tcl scripting, yet general LLMs perform poorly in this domain due to extreme data scarcity, domain-specific semantics, and the high reliability req…
Breaking the Scale Barrier: One-Shot Knowledge Transfer via Frequency Transform
Jianlu Shen, Fu Feng, Yucheng Xie +2
Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a pre-trained model is tightly coup…
TokenSwap: Backdoor Attack on the Compositional Understanding of Large Vision-Language Models
Zhifang Zhang, Qiqi Tao, Jiaqi Lv +3
Large vision-language models (LVLMs) have achieved impressive performance across a wide range of vision-language tasks, while they remain vulnerable to backdoor attacks. Existing b…
Harmonizing Generalization and Personalization in Ring-topology Decentralized Federated Learning
Shunxin Guo, Jiaqi Lv, Xin Geng
We introduce Ring-topology Decentralized Federated Learning (RDFL) for distributed model training, aiming to avoid the inherent risks of centralized failure in server-based FL. How…
GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning
Shunxin Guo, Jiaqi Lv, Qiufeng Wang +1
Real-world \underline{F}ederated \underline{L}earning systems often encounter \underline{D}ynamic clients with \underline{A}gnostic and highly heterogeneous data distributions (DAF…
Towards Effective Visual Representations for Partial-Label Learning
Shiyu Xia, Jiaqi Lv, Ning Xu +2
Under partial-label learning (PLL) where, for each training instance, only a set of ambiguous candidate labels containing the unknown true label is accessible, contrastive learning…
Progressive Purification for Instance-Dependent Partial Label Learning
Ning Xu, Biao Liu, Jiaqi Lv +2
Partial label learning (PLL) aims to train multiclass classifiers from the examples each annotated with a set of candidate labels where a fixed but unknown candidate label is corre…
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement
Ning Xu, Congyu Qiao, Jiaqi Lv +2
Multi-label learning (MLL) learns from the examples each associated with multiple labels simultaneously, where the high cost of annotating all relevant labels for each training exa…
Learning with Proper Partial Labels
Zhenguo Wu, Jiaqi Lv, Masashi Sugiyama
Partial-label learning is a kind of weakly-supervised learning with inexact labels, where for each training example, we are given a set of candidate labels instead of only one true…
On the Robustness of Average Losses for Partial-Label Learning
Jiaqi Lv, Biao Liu, Lei Feng +6
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (I…
Compact Learning for Multi-Label Classification
Jiaqi Lv, Tianran Wu, Chenglun Peng +3
Multi-label classification (MLC) studies the problem where each instance is associated with multiple relevant labels, which leads to the exponential growth of output space. MLC enc…
Provably Consistent Partial-Label Learning
Lei Feng, Jiaqi Lv, Bo Han +5
Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL metho…
Progressive Identification of True Labels for Partial-Label Learning
Jiaqi Lv, Miao Xu, Lei Feng +3
Partial-label learning (PLL) is a typical weakly supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the tr…