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Ziang Zhou

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6 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

SteerConf: Steering LLMs for Confidence Elicitation

Ziang Zhou
Tianyuan Jin
Jieming Shi
Qing Li

Large Language Models (LLMs) exhibit impressive performance across diverse domains but often suffer from overconfidence, limiting their reliability in critical applications. We propose SteerConf, a novel framework that systematically steers LLMs' confidence scores to improve their calibration and reliability. SteerConf introduces three key components: (1) a steering prompt strategy that guides LLMs to produce confidence scores in specified directions (e. g. , conservative or optimistic) by leveraging prompts with varying steering levels; (2) a steered confidence consistency measure that quantifies alignment across multiple steered confidences to enhance calibration; and (3) a steered confidence calibration method that aggregates confidence scores using consistency measures and applies linear quantization for answer selection. SteerConf operates without additional training or fine-tuning, making it broadly applicable to existing LLMs. Experiments on seven benchmarks spanning professional knowledge, common sense, ethics, and reasoning tasks, using advanced LLM models (GPT-3. 5, LLaMA 3, GPT-4), demonstrate that SteerConf significantly outperforms existing methods, often by a significant margin. Our findings highlight the potential of steering the confidence of LLMs to enhance their reliability for safer deployment in real-world applications. The implementation is at \url{https: //github. com/scottjiao/SteerConf}.

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ICML Conference 2025 Conference Paper

TINED: GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation

Ziang Zhou
Zhihao Ding
Jieming Shi 0001
Qing Li 0001
Shiqi Shen

Graph Neural Networks (GNNs) are pivotal in graph-based learning, particularly excelling in node classification. However, their scalability is hindered by the need for multi-hop data during inference, limiting their application in latency-sensitive scenarios. Recent efforts to distill GNNs into multi-layer perceptrons (MLPs) for faster inference often underutilize the layer-level insights of GNNs. In this paper, we present TINED, a novel approach that distills GNNs to MLPs on a layer-by-layer basis using Teacher Injection and Dirichlet Energy Distillation techniques. We focus on two key operations in GNN layers: feature transformation (FT) and graph propagation (GP). We recognize that FT is computationally equivalent to a fully-connected (FC) layer in MLPs. Thus, we propose directly transferring teacher parameters from an FT in a GNN to an FC layer in the student MLP, enhanced by fine-tuning. In TINED, the FC layers in an MLP replicate the sequence of FTs and GPs in the GNN. We also establish a theoretical bound for GP approximation. Furthermore, we note that FT and GP operations in GNN layers often exhibit opposing smoothing effects: GP is aggressive, while FT is conservative. Using Dirichlet energy, we develop a DE ratio to measure these effects and propose Dirichlet Energy Distillation to convey these characteristics from GNN layers to MLP layers. Extensive experiments show that TINED outperforms GNNs and leading distillation methods across various settings and seven datasets. Source code are available at https: //github. com/scottjiao/TINED_ICML25/.

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EAAI Journal 2024 Journal Article

EAFNet: Extraction-amplification-fusion network for tiny cracks detection

Ziang Zhou
Wensong Zhao
Kechen Song
Yanyan Wang
Jun Li

Tiny cracks are often overlooked in the inspection process, causing huge economic losses and dangerous accidents. Therefore, tiny cracks should be detected in a timely and accurate manner to eliminate the disease at the initial stage. Inspired by the fact that humans are more likely to capture conspicuous information when observing objects, we propose a novel three-stage Extraction-Amplification-Fusion network (EAFNet). Specifically, in the extraction stage, we utilize an effective backbone network for feature extraction of tiny cracks. In the amplification stage, we design a Tiny Feature Amplification (TFA) module to amplify the extracted features. In the fusion stage, we propose a Two-Branch Fusion (TBF) module to fully fuse the feature maps at different resolutions. To make tiny crack information more ‘conspicuous’, we propose an activation function TinyReLU to enhance the contrast of the tiny cracks with the background. In addition, we construct a Tiny Crack (T-CRACK) dataset with six different backgrounds and a Cross-scale Crack (C-CRACK) dataset. On both datasets, EAFNet achieves an advantage over the existing 8 advanced networks. The two datasets are available at: https: //github. com/EAFNet/EAFNet.

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JMLR Journal 2023 Journal Article

Multi-Consensus Decentralized Accelerated Gradient Descent

Haishan Ye
Luo Luo
Ziang Zhou
Tong Zhang

his paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory. We propose novel algorithms that achieve optimal computation complexity and near optimal communication complexity. Our theoretical results give affirmative answers to the open problem on whether there exists an algorithm that can achieve a communication complexity (nearly) matching the lower bound depending on the global condition number instead of the local one. Furthermore, the linear convergence of our algorithms only depends on the strong convexity of global objective and it does not require the local functions to be convex. The design of our methods relies on a novel integration of well-known techniques including Nesterov's acceleration, multi-consensus and gradient-tracking. Empirical studies show the outperformance of our methods for machine learning applications. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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ICML Conference 2023 Conference Paper

SlotGAT: Slot-based Message Passing for Heterogeneous Graphs

Ziang Zhou
Jieming Shi 0001
Renchi Yang
Yuanhang Zou
Qing Li 0001

Heterogeneous graphs are ubiquitous to model complex data. There are urgent needs on powerful heterogeneous graph neural networks to effectively support important applications. We identify a potential semantic mixing issue in existing message passing processes, where the representations of the neighbors of a node v are forced to be transformed to the feature space of v for aggregation, though the neighbors are in different types. That is, the semantics in different node types are entangled together into node v’s representation. To address the issue, we propose SlotGAT with separate message passing processes in slots, one for each node type, to maintain the representations in their own node-type feature spaces. Moreover, in a slot-based message passing layer, we design an attention mechanism for effective slot-wise message aggregation. Further, we develop a slot attention technique after the last layer of SlotGAT, to learn the importance of different slots in downstream tasks. Our analysis indicates that the slots in SlotGAT can preserve different semantics in various feature spaces. The superiority of SlotGAT is evaluated against 13 baselines on 6 datasets for node classification and link prediction. Our code is at https: //github. com/scottjiao/SlotGAT_ICML23/.

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NeurIPS Conference 2020 Conference Paper

Decentralized Accelerated Proximal Gradient Descent

Haishan Ye
Ziang Zhou
Luo Luo
Tong Zhang

Decentralized optimization has wide applications in machine learning, signal processing, and control. In this paper, we study the decentralized composite optimization problem with a non-smooth regularization term. Many proximal gradient based decentralized algorithms have been proposed in the past. However, these algorithms do not achieve near optimal computational complexity and communication complexity. In this paper, we propose a new method which establishes the optimal computational complexity and a near optimal communication complexity. Our empirical study shows that the proposed algorithm outperforms existing state-of-the-art algorithms.

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