5 citations · 5 across the 9 of their papers we have counts for
10 papers
MemForm: Benchmarking Memory Formation Poisoning in LLM Agents
Haowen Pan, Pengyang Shao, Yunshan Ma +4
Long-term memory enables LLM agents to learn from past interactions but exposes memory formation to adversarial influence. Existing studies primarily organize memory poisoning by a…
Provable Sparse Inversion and Token Relabel Enhanced One-shot Federated Learning with ViTs
Li Shen, Xiaolei Hao, Qinglun Li +3
One-Shot Federated Learning, where a central server learns a global model in a single communication round, has emerged as a promising paradigm. However, under extremely non-IID set…
Reducing Class-Wise Performance Disparity via Margin Regularization
Beier Zhu, Kesen Zhao, Jiequan Cui +4
Deep neural networks often exhibit substantial disparities in class-wise accuracy, even when trained on class-balanced data, posing concerns for reliable deployment. While prior ef…
BalDRO: A Distributionally Robust Optimization based Framework for Large Language Model Unlearning
Pengyang Shao, Naixin Zhai, Lei Chen +4
As Large Language Models (LLMs) increasingly shape online content, removing targeted information from well-trained LLMs (also known as LLM unlearning) has become critical for web g…
Maximizing Local Entropy Where It Matters: Prefix-Aware Localized LLM Unlearning
Naixin Zhai, Pengyang Shao, Binbin Zheng +4
Machine unlearning aims to forget sensitive knowledge from Large Language Models (LLMs) while maintaining general utility. However, existing approaches typically treat all tokens i…
Parallel Diffusion Solver via Residual Dirichlet Policy Optimization
Ruoyu Wang, Ziyu Li, Beier Zhu +5
Diffusion models (DMs) have achieved state-of-the-art generative performance but suffer from high sampling latency due to their sequential denoising nature. Existing solver-based a…
Not All Pixels Are Equal: Pixel-wise Meta-Learning for Medical Segmentation with Noisy Labels
Chenyu Mu, Guihai Chen, Xun Yang +2
Medical image segmentation is crucial for clinical applications, but it is frequently disrupted by noisy annotations and ambiguous anatomical boundaries, limiting its application i…
Fading to Grow: Growing Preference Ratios via Preference Fading Discrete Diffusion for Recommendation
Guoqing Hu, An Zhang. Shuchang Liu, Wenyu Mao +7
Recommenders aim to rank items from a discrete item corpus in line with user interests, yet suffer from extremely sparse user preference data. Recent advances in diffusion models h…
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
Rong Dai, Yonggang Zhang, Ang Li +3
One-shot Federated Learning (OFL) has become a promising learning paradigm, enabling the training of a global server model via a single communication round. In OFL, the server mode…
InstanT: Semi-supervised Learning with Instance-dependent Thresholds
Muyang Li, Runze Wu, Haoyu Liu +4
Semi-supervised learning (SSL) has been a fundamental challenge in machine learning for decades. The primary family of SSL algorithms, known as pseudo-labeling, involves assigning…