Haoran Deng

EAAI Journal 2025 Journal Article

A robotic grasping method of box-shaped objects based on Dual-Stream You Only Look Once framework

• Haoran Deng
• Jinlong Shi
• Jiale Cui
• Suqin Bai
• Xin Zuo

ICLR Conference 2025 Conference Paper

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery

• Yingyu Lin
• Yuxing Huang
• Wenqin Liu
• Haoran Deng
• Ignavier Ng
• Kun Zhang 0001
• Mingming Gong
• Yian Ma

Real-world data often violates the equal-variance assumption (homoscedasticity), making it essential to account for heteroscedastic noise in causal discovery. In this work, we explore heteroscedastic symmetric noise models (HSNMs), where the effect $Y$ is modeled as $Y = f(X) + \sigma(X)N$, with $X$ as the cause and $N$ as independent noise following a symmetric distribution. We introduce a novel criterion for identifying HSNMs based on the skewness of the score (i.e., the gradient of the log density) of the data distribution. This criterion establishes a computationally tractable measurement that is zero in the causal direction but nonzero in the anticausal direction, enabling the causal direction discovery. We extend this skewness-based criterion to the multivariate setting and propose \texttt{SkewScore}, an algorithm that handles heteroscedastic noise without requiring the extraction of exogenous noise. We also conduct a case study on the robustness of \texttt{SkewScore} in a bivariate model with a latent confounder, providing theoretical insights into its performance. Empirical studies further validate the effectiveness of the proposed method.

ICLR Conference 2024 Conference Paper

Fast Updating Truncated SVD for Representation Learning with Sparse Matrices

• Haoran Deng
• Yang Yang 0009
• Jiahe Li 0008
• Cheng Chen
• Weihao Jiang
• Shiliang Pu

Updating truncated Singular Value Decomposition (SVD) has extensive applications in representation learning. The continuous evolution of massive-scaled data matrices in practical scenarios highlights the importance of aligning SVD-based models with fast-paced updates. Recent methods for updating truncated SVD can be recognized as Rayleigh-Ritz projection procedures where their projection matrices are augmented based on the original singular vectors. However, the updating process in these methods densifies the update matrix and applies the projection to all singular vectors, resulting in inefficiency. This paper presents a novel method for dynamically approximating the truncated SVD of a sparse and temporally evolving matrix. The proposed method takes advantage of sparsity in the orthogonalization process of the augment matrices and employs an extended decomposition to store projections in the column space of singular vectors independently. Numerical experimental results on updating truncated SVD for evolving sparse matrices show an order of magnitude improvement in the efficiency of our proposed method while maintaining precision comparing to previous methods.

ICRA Conference 2024 Conference Paper

Parameter-efficient Prompt Learning for 3D Point Cloud Understanding

• Hongyu Sun 0006
• Yongcai Wang
• Wang Chen
• Haoran Deng
• Deying Li 0001

This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on timeconsuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method. Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i. e. , point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://github.com/auniquesun/PPT.

IJCAI Conference 2022 Conference Paper

Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network

• Yifei Sun
• Haoran Deng
• Yang Yang
• Chunping Wang
• Jiarong Xu
• Renhong Huang
• Linfeng Cao
• Yang Wang

Graph neural networks (GNNs) have been intensively studied in various real-world tasks. However, the homophily assumption of GNNs' aggregation function limits their representation learning ability in heterophily graphs. In this paper, we shed light on the path level patterns in graphs that can explicitly reflect rich semantic and structural information. We therefore propose a novel Structure-aware Path Aggregation Graph Neural Network (PathNet) aiming to generalize GNNs for both homophily and heterophily graphs. Specifically, we first introduce a maximal entropy path sampler, which helps us sample a number of paths containing structural context. Then, we introduce a structure-aware recurrent cell consisting of order-preserving and distance-aware components to learn the semantic information of neighborhoods. Finally, we model the preference of different paths to target node after path encoding. Experimental results demonstrate that our model achieves superior performance in node classification on both heterophily and homophily graphs.