Search Results for author: Zhanke Zhou

Found 18 papers, 17 papers with code

Efficient Hyper-parameter Search for Knowledge Graph Embedding

1 code implementation ACL 2022 Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li

Based on the analysis, we propose an efficient two-stage search algorithm KGTuner, which efficiently explores HP configurations on small subgraph at the first stage and transfers the top-performed configurations for fine-tuning on the large full graph at the second stage.

AutoML Knowledge Graph Embedding

Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models

1 code implementation28 Mar 2025 Zhanke Zhou, Zhaocheng Zhu, Xuan Li, Mikhail Galkin, Xiao Feng, Sanmi Koyejo, Jian Tang, Bo Han

We showcase this advantage by adapting our tool to a lightweight verifier that evaluates the correctness of reasoning paths.

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond

no code implementations26 Feb 2025 Qizhou Wang, Jin Peng Zhou, Zhanke Zhou, Saebyeol Shin, Bo Han, Kilian Q. Weinberger

Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements.

Noisy Test-Time Adaptation in Vision-Language Models

1 code implementation20 Feb 2025 Chentao Cao, Zhun Zhong, Zhanke Zhou, Tongliang Liu, Yang Liu, Kun Zhang, Bo Han

Leveraging the zero-shot capability of pre-trained vision-language models (VLMs), this paper introduces Zero-Shot Noisy TTA (ZS-NTTA), focusing on adapting the model to target data with noisy samples during test-time in a zero-shot manner.

Test-time Adaptation

Eliciting Causal Abilities in Large Language Models for Reasoning Tasks

1 code implementation19 Dec 2024 Yajing Wang, Zongwei Luo, Jingzhe Wang, Zhanke Zhou, Yongqiang Chen, Bo Han

In SCIE, the instructions are treated as the treatment, and textual features are used to process natural language, establishing causal relationships through treatments between instructions and downstream tasks.

Causal Inference

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models

1 code implementation18 Dec 2024 Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, ChangShui Zhang

Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge.

Model Inversion Attacks: A Survey of Approaches and Countermeasures

1 code implementation15 Nov 2024 Zhanke Zhou, Jianing Zhu, Fengfei Yu, Xuan Li, Xiong Peng, Tongliang Liu, Bo Han

These attacks highlight the vulnerability of neural networks and raise awareness about the risk of privacy leakage within the research community.

Survey

Mind the Gap Between Prototypes and Images in Cross-domain Finetuning

1 code implementation16 Oct 2024 Hongduan Tian, Feng Liu, Zhanke Zhou, Tongliang Liu, Chengqi Zhang, Bo Han

However, in this paper, we find that there naturally exists a gap, which resembles the modality gap, between the prototype and image instance embeddings extracted from the frozen pre-trained backbone, and simply applying the same transformation during the adaptation phase constrains exploring the optimal representations and shrinks the gap between prototype and image representations.

Cross-Domain Few-Shot

Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection

1 code implementation2 Jun 2024 Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han

In this paper, we propose to tackle this constraint by leveraging the expert knowledge and reasoning capability of large language models (LLM) to Envision potential Outlier Exposure, termed EOE, without access to any actual OOD data.

Out-of-Distribution Detection

Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs

1 code implementation15 Mar 2024 Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han

To deduce new facts on a knowledge graph (KG), a link predictor learns from the graph structure and collects local evidence to find the answer to a given query.

Knowledge Graphs Link Prediction +1

DeepInception: Hypnotize Large Language Model to Be Jailbreaker

1 code implementation6 Nov 2023 Xuan Li, Zhanke Zhou, Jianing Zhu, Jiangchao Yao, Tongliang Liu, Bo Han

Large language models (LLMs) have succeeded significantly in various applications but remain susceptible to adversarial jailbreaks that void their safety guardrails.

Language Modeling Language Modelling +2

Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel

1 code implementation2 Nov 2023 Xuan Li, Zhanke Zhou, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han

To tackle this issue, we propose a method to abstract the collective information of atomic groups into a few $\textit{Neural Atoms}$ by implicitly projecting the atoms of a molecular.

Drug Discovery

Combating Bilateral Edge Noise for Robust Link Prediction

1 code implementation NeurIPS 2023 Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, Li He, Liang Wang, Bo Zheng, Bo Han

To address this dilemma, we propose an information-theory-guided principle, Robust Graph Information Bottleneck (RGIB), to extract reliable supervision signals and avoid representation collapse.

Denoising Link Prediction +2

Understanding Fairness Surrogate Functions in Algorithmic Fairness

1 code implementation17 Oct 2023 Wei Yao, Zhanke Zhou, Zhicong Li, Bo Han, Yong liu

To mitigate such bias while achieving comparable accuracy, a promising approach is to introduce surrogate functions of the concerned fairness definition and solve a constrained optimization problem.

Fairness

On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation

1 code implementation15 Jun 2023 Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han

Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored.

Graph Reconstruction Reconstruction Attack

AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning

2 code implementations30 May 2022 Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han

An important design component of GNN-based KG reasoning methods is called the propagation path, which contains a set of involved entities in each propagation step.

Graph Neural Network +1

KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning

2 code implementations5 May 2022 Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li

While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently.

Graph Learning

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