2 citations · 3 across the 12 of their papers we have counts for
12 papers
Inference-Time Concept Suppression and Video-Centric Evaluation for Text-to-Video Models
Wenxuan Chen, Wenjie Feng
Text-to-video (T2V) generators can synthesize realistic and temporally coherent videos, but controllably removing a target concept from a generator remains difficult. Unlike text-t…
AdaPCLA: Adaptive Prior-Calibrated Logit Adjustment for Long-Tailed Longitudinal EHR Generation
Shuai Cui, Chen Wenxuan, Wenjie Du +3
Generative modeling of longitudinal Electronic Health Records is increasingly important for privacy-preserving research, yet standard autoregressive models tend to underrepresent t…
Signal-Guided Optimization for Machine Unlearning
Xujia Li, Dan Li, Jian Lou +1
Current machine unlearning methods predominantly rely on global, coarse-grained intervention strategies. They lack precise pilot signals to guide the unlearning process and fail to…
Activation Steering Induces Emergent Misalignment: Steering Vector Construction and Comprehensive Evaluation
Qi Cao, Jian Lou, Meiting Liu +4
Activation steering (AS) has emerged as a popular inference-time technique for modulating the behavior of large language models (LLMs). AS modulates model behavior in a flexible an…
Exploiting Verification-Generation Gap: Test-Time Reinforcement Learning with Confidence-Conditioned Verification
Jiahui Li, Jianfeng Shan, Wenpei Chen +5
Test-time reinforcement learning has emerged as a promising paradigm for enhancing the complex reasoning abilities of large language models in a completely label-free manner. Despi…
Empowering VLMs for Few-Shot Multimodal Time Series Classification via Tailored Agentic Reasoning
Lin Li, Jiawei Huang, Qihao Quan +7
In this paper, we propose the first VL\underline{\textbf{M}} \underline{\textbf{a}}gentic \underline{\textbf{r}}easoning framework for few-\underline{\textbf{s}}hot multimodal \und…
Rethinking Machine Unlearning in Image Generation Models
Renyang Liu, Wenjie Feng, Tianwei Zhang +3
With the surge and widespread application of image generation models, data privacy and content safety have become major concerns and attracted great attention from users, service p…
Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgment
Shunyu Wu, Dan Li, Wenjie Feng +3
High-quality time series (TS) data are essential for ensuring TS model performance, rendering research on rating TS data quality indispensable. Existing methods have shown promisin…
ID: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
Shen Li, Jianqing Xu, Jiaying Wu +7
Synthetic face recognition (SFR) aims to generate synthetic face datasets that mimic the distribution of real face data, which allows for training face recognition models in a priv…
Data-Free Federated Class Incremental Learning with Diffusion-Based Generative Memory
Naibo Wang, Yuchen Deng, Wenjie Feng +2
Federated Class Incremental Learning (FCIL) is a critical yet largely underexplored issue that deals with the dynamic incorporation of new classes within federated learning (FL). E…
One-Shot Sequential Federated Learning for Non-IID Data by Enhancing Local Model Diversity
Naibo Wang, Yuchen Deng, Wenjie Feng +3
Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential fed…
SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks
Brian Formento, Wenjie Feng, Chuan Sheng Foo +2
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern. While current research has ex…