15 citations · 29 across the 13 of their papers we have counts for
14 papers
CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation
Chenlu Ding, Jiancan Wu, Yanchen Luo +4
Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must remove target information w…
PrunePath: Towards Highly Structured Sparse Language Models
Zhexuan Gu, Zixun Fu, Yancheng Yuan
Feed-forward networks (FFNs) dominate the parameter count and computation of modern language models, yet existing pruning methods often struggle to convert sparsity into hardware-f…
Teaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning
Chenlu Ding, Jiancan Wu, Yanchen Luo +3
Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that became availab…
Efficient and provably convergent end-to-end training of deep neural networks with linear constraints
Zonglin Yang, Zhexuan Gu, Yancheng Yuan
Training a deep neural network with the outputs of selected layers satisfying linear constraints is required in many contemporary data-driven applications. While this can be achiev…
MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering
Chenlu Ding, Jiancan Wu, Leheng Sheng +4
Multimodal large language models (MLLMs) have demonstrated remarkable capabilities across vision-language tasks, yet their large-scale deployment raises pressing concerns about mem…
Addressing Missing Data Issue for Diffusion-based Recommendation
Wenyu Mao, Zhengyi Yang, Jiancan Wu +4
Diffusion models have shown significant potential in generating oracle items that best match user preference with guidance from user historical interaction sequences. However, the…
Delayed Feedback Modeling with Influence Functions
Chenlu Ding, Jiancan Wu, Yancheng Yuan +5
In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial. A major challenge is delayed feedback, where conversions may…
Unified Parameter-Efficient Unlearning for LLMs
Chenlu Ding, Jiancan Wu, Yancheng Yuan +5
The advent of Large Language Models (LLMs) has revolutionized natural language processing, enabling advanced understanding and reasoning capabilities across a variety of tasks. Fin…
RosePO: Aligning LLM-based Recommenders with Human Values
Jiayi Liao, Xiangnan He, Ruobing Xie +5
Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for recommendation systems, which usually adapt a pre-trained LLM to the recommendation scena…
Adaptive Self-supervised Robust Clustering for Unstructured Data with Unknown Cluster Number
Chen-Lu Ding, Jiancan Wu, Wei Lin +3
We introduce a novel self-supervised deep clustering approach tailored for unstructured data without requiring prior knowledge of the number of clusters, termed Adaptive Self-super…
LLaRA: Large Language-Recommendation Assistant
Jiayi Liao, Sihang Li, Zhengyi Yang +4
Sequential recommendation aims to predict users' next interaction with items based on their past engagement sequence. Recently, the advent of Large Language Models (LLMs) has spark…
Large Language Model Can Interpret Latent Space of Sequential Recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo +5
Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a c…
Generate What You Prefer: Reshaping Sequential Recommendation via Guided Diffusion
Zhengyi Yang, Jiancan Wu, Zhicai Wang +3
Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studie…
Differentiable Invariant Causal Discovery
Yu Wang, An Zhang, Xiang Wang +3
Learning causal structure from observational data is a fundamental challenge in machine learning. However, the majority of commonly used differentiable causal discovery methods are…