3 citations · 4 across the 21 of their papers we have counts for
21 papers
USAD: Uncertainty-aware Statistical Adversarial Detection
Zhijian Zhou, Xunye Tian, Jiacheng Zhang +5
Statistical adversarial detection (SAD) treats detection as a two-sample test. Given a reference set of clean examples (CEs) and a batch of queries, potentially containing an unkno…
CELEUS: Certifiable and Efficient LLM Evaluation via E-Processes
Zhijian Zhou, Zesheng Ye, Zhaorun Chen +2
Can we trust evaluation scores to capture an LLM's true real-world performance? Certifiable evaluation answers this question by providing guarantee for LLM evaluation. In particula…
CARPRT: Class-Aware Zero-Shot Prompt Reweighting for Black-Box Vision-Language Models
Ruijiang Dong, Zesheng Ye, Jianzhong Qi +4
Pre-trained vision-language models (VLMs) enable zero-shot image classification by computing the similarity score between an image and textual descriptions, typically formed by ins…
User-Aware Conditional Generative Total Correlation Learning for Multi-Modal Recommendation
Jing Du, Zesheng Ye, Congbo Ma +2
Multi-modal recommendation (MMR) enriches item representations by introducing item content, e.g., visual and textual descriptions, to improve upon interaction-only recommenders. Th…
Combating Data Laundering in LLM Training
Muxing Li, Zesheng Ye, Sharon Li +1
Post-hoc unauthorized-training data detection for large language models (LLMs) typically assumes a query-with-originals regime: rights holders query a target LLM with raw proprieta…
Per-parameter Task Arithmetic for Unlearning in Large Language Models
Chengyi Cai, Zesheng Ye, Jiangchao Yao +5
In large language model (LLM) unlearning, private information is required to be removed. Task arithmetic unlearns by subtracting a specific task vector (TV)--defined as the paramet…
Visual-Guided Key-Token Regularization for Multimodal Large Language Model Unlearning
Chengyi Cai, Zesheng Ye, Peike Li +3
Unlearning in Multimodal Large Language Models (MLLMs) prevents the model from revealing private information when queried about target images. Existing MLLM unlearning methods larg…
Let's Roll a BiFTA: Bi-refinement for Fine-grained Text-visual Alignment in Vision-Language Models
Yuhao Sun, Chengyi Cai, Jiacheng Zhang +3
Recent research has shown that aligning fine-grained text descriptions with localized image patches can significantly improve the zero-shot performance of pre-trained vision-langua…
Sample-Specific Noise Injection For Diffusion-Based Adversarial Purification
Yuhao Sun, Jiacheng Zhang, Zesheng Ye +2
Diffusion-based purification (DBP) methods aim to remove adversarial noise from the input sample by first injecting Gaussian noise through a forward diffusion process, and then rec…
Neural Network Reprogrammability: A Unified Theme on Model Reprogramming, Prompt Tuning, and Prompt Instruction
Zesheng Ye, Chengyi Cai, Ruijiang Dong +4
As large-scale pre-trained foundation models continue to expand in size and capability, efficiently adapting them to specific downstream tasks has become increasingly critical. Des…
Understanding Model Reprogramming for CLIP via Decoupling Visual Prompts
Chengyi Cai, Zesheng Ye, Lei Feng +2
Model reprogramming adapts pretrained models to downstream tasks by modifying only the input and output spaces. Visual reprogramming (VR) is one instance for vision tasks that adds…
Attribute-based Visual Reprogramming for Vision-Language Models
Chengyi Cai, Zesheng Ye, Lei Feng +2
Visual reprogramming (VR) reuses pre-trained vision models for downstream image classification tasks by adding trainable noise patterns to inputs. When applied to vision-language m…
Distributional Statistics Restore Training Data Auditability in One-step Distilled Diffusion Models
Muxing Li, Zesheng Ye, Sharon Li +3
The proliferation of diffusion models trained on web-scale, provenance-uncertain image collections has made it essential, yet technically unresolved, to determine whether a model h…
Bayesian-guided Label Mapping for Visual Reprogramming
Chengyi Cai, Zesheng Ye, Lei Feng +2
Visual reprogramming (VR) leverages the intrinsic capabilities of pretrained vision models by adapting their input or output interfaces to solve downstream tasks whose labels (i.e.…
Sample-specific Masks for Visual Reprogramming-based Prompting
Chengyi Cai, Zesheng Ye, Lei Feng +2
Visual reprogramming (VR) is a prompting technique that aims to re-purpose a pre-trained model (e.g., a classifier on ImageNet) to target tasks (e.g., medical data prediction) by l…
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement
Jing Du, Zesheng Ye, Bin Guo +2
Cross-Domain Recommendation (CDR) seeks to enable effective knowledge transfer across domains. Existing works rely on either representation alignment or transformation bridges, but…
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Jing Du, Zesheng Ye, Bin Guo +2
Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOC…
Adversarially Contrastive Estimation of Conditional Neural Processes
Zesheng Ye, Jing Du, Lina Yao
Conditional Neural Processes~(CNPs) formulate distributions over functions and generate function observations with exact conditional likelihoods. CNPs, however, have limited expres…
Uncertainty-Aware Pedestrian Trajectory Prediction via Distributional Diffusion
Yao Liu, Zesheng Ye, Rui Wang +3
Tremendous efforts have been put forth on predicting pedestrian trajectory with generative models to accommodate uncertainty and multi-modality in human behaviors. An individual's…
See What You See: Self-supervised Cross-modal Retrieval of Visual Stimuli from Brain Activity
Zesheng Ye, Lina Yao, Yu Zhang +1
Recent studies demonstrate the use of a two-stage supervised framework to generate images that depict human perception to visual stimuli from EEG, referring to EEG-visual reconstru…
IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
Jing Du, Zesheng Ye, Lina Yao +2
Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised…