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Bowen Pang

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

RayFusion: Ray Fusion Enhanced Collaborative Visual Perception

Shaohong Wang
Lu Bin
Xinyu Xiao
Hanzhi Zhong
Bowen Pang
Tong Wang
Zhiyu Xiang
Hangguan Shan

Collaborative visual perception methods have gained widespread attention in the autonomous driving community in recent years due to their ability to address sensor limitation problems. However, the absence of explicit depth information often makes it difficult for camera-based perception systems, e. g. , 3D object detection, to generate accurate predictions. To alleviate the ambiguity in depth estimation, we propose RayFusion, a ray-based fusion method for collaborative visual perception. Using ray occupancy information from collaborators, RayFusion reduces redundancy and false positive predictions along camera rays, enhancing the detection performance of purely camera-based collaborative perception systems. Comprehensive experiments show that our method consistently outperforms existing state-of-the-art models, substantially advancing the performance of collaborative visual perception. Our code will be made publicly available.

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ICLR Conference 2024 Conference Paper

L2P-MIP: Learning to Presolve for Mixed Integer Programming

Chang Liu 0021
Zhichen Dong
Haobo Ma
Weilin Luo
Xijun Li
Bowen Pang
Jia Zeng
Junchi Yan

Modern solvers for solving mixed integer programming (MIP) often rely on the branch-and-bound (B&B) algorithm which could be of high time complexity, and presolving techniques are well designed to simplify the instance as pre-processing before B&B. However, such presolvers in existing literature or open-source solvers are mostly set by default agnostic to specific input instances, and few studies have been reported on tailoring presolving settings. In this paper, we aim to dive into this open question and show that the MIP solver can be indeed largely improved when switching the default instance-agnostic presolving into instance-specific presolving. Specifically, we propose a combination of supervised learning and classic heuristics to achieve efficient presolving adjusting, avoiding tedious reinforcement learning. Notably, our approach is orthogonal from many recent efforts in incorporating learning modules into the B&B framework after the presolving stage, and to our best knowledge, this is the first work for introducing learning to presolve in MIP solvers. Experiments on multiple real-world datasets show that well-trained neural networks can infer proper presolving for arbitrary incoming MIP instances in less than 0.5s, which is neglectable compared with the solving time often hours or days.

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EAAI Journal 2023 Journal Article

A representation-learning-based approach to predict stock price trend via dynamic spatiotemporal feature embedding

Bowen Pang
Wei Wei
Xing Li
Xiangnan Feng
Chao Li

Stock price trend prediction is a fascinating but difficult research topic. Recently, GNN-based models have been continuously proposed, which are believed to be more effective since they consider the information about stocks themselves and the information between stocks. However, the graph data are often static, unstructured and not all-inclusive, which cannot dynamically reflect all relationships between stocks. Therefore, we propose a novel model PriceExploration-Network (PE-Net), effectively utilizing both temporal and cross-sectional information contained in price to predict the price trend. PE-Net only requires the price data effectively saving the trouble of fetching alternative data and is able to capture the dynamic implicit relations between stocks by combining clustering techniques and GAT architecture. The effectiveness of PE-Net is examined on real-world S&P 500 constituents and the results demonstrate that PE-Net can outperform state-of-the-art models w. r. t. both accuracy and AUC.

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