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Hongxu Ma

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

Invariant Feature Learning for Counterfactual Watch-time Prediction in Video Recommendation

Chenghou Jin
Yixin Ren
Hongxu Ma
Yewei Xia
Yi Guan
Hao Zhang
Jiandong Ding
Jihong Guan

Video recommendation systems heavily rely on user watch time feedback, making accurate watch time prediction a crucial task. However, this task inherently suffers from bias, as recommendation models tend to favor long-duration videos to maximize watch time. This issue, known as duration bias in the watch-time prediction context, can be explained from a causal perspective, where video duration acts as a confounder. Recent works address this bias using backdoor adjustment, isolating the direct effect of content on watch time from observational data. These methods typically discretize video duration into groups, estimate group-wise effects, and then aggregate them via a unified prediction model. However, this aggregation strategy is prone to model misspecification due to feature distribution shift across groups. In this paper, we reinterpret the problem through the lens of invariant learning and propose a novel framework: Duration-Invariant Feature Learning (DIFL). DIFL employs a kernel-based regularization that enforces representation invariance across duration groups, reducing sensitivity to group design and improving generalization. This enables more accurate modeling of the direct causal effect and making counterfactual inference. Extensive experiments on both public and real large-scale production datasets demonstrate the effectiveness of our approach, which achieves SOTA performance.

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IROS Conference 2025 Conference Paper

Enhancing Multi-Task Motion Planning Based on Improved DMPs for Lower Limb Prostheses

Honglei An
Yongshan Huang
Yiming Nie
Hongxu Ma

Achieving natural locomotion across diverse environments with prosthetic limbs remains a significant challenge for amputees. Intelligent prosthetics leverage motion planning techniques using phase variables to emulate natural gait aligned with human movement intentions. However, traditional phase variable-based planning, which utilizes geometric human motion models, often lacks robustness when encountering external disturbances. Additionally, models derived from human walking data can only approximate a limited set of discrete tasks, hindering the construction of a comprehensive model. In this study, we present an advanced prosthetic motion planning approach that integrates Dynamic Motion Primitives (DMPs) to ensure robust performance across multiple tasks. We demonstrate that DMPs with human-in-the-loop effectively simulate human joint movement trajectories under various task conditions. Furthermore, we introduce a novel Multi-Task Dynamic Motion Primitives with Singular Value Decomposition (DMPs-SVD) method, which incorporates multiple feature trajectory learning. This approach constructs a coherent task model using a limited dataset of typical human walking patterns, enabling joint motion planning across diverse task scenarios. Experimental results validate the viability and efficacy of the proposed human-in-loop DMPs and DMPs-SVD techniques in prosthetic applications.

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NeurIPS Conference 2024 Conference Paper

TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning

Nemin Wu
Qian Cao
Zhangyu Wang
Zeping Liu
Yanlin Qi
Jielu Zhang
Joshua Ni
Xiaobai Yao

Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e. g. , points, polylines, polygons, networks, images, etc. ) in their native formats. Learning good spatial representations is a fundamental problem for various downstream applications such as species distribution modeling, weather forecasting, trajectory generation, geographic question answering, etc. Even though SRL has become the foundation of almost all geospatial artificial intelligence (GeoAI) research, we have not yet seen significant efforts to develop an extensive deep learning framework and benchmark to support SRL model development and evaluation. To fill this gap, we propose TorchSpatial, a learning framework and benchmark for location (point) encoding, which is one of the most fundamental data types of spatial representation learning. TorchSpatial contains three key components: 1) a unified location encoding framework that consolidates 15 commonly recognized location encoders, ensuring scalability and reproducibility of the implementations; 2) the LocBench benchmark tasks encompassing 7 geo-aware image classification and 10 geo-aware imageregression datasets; 3) a comprehensive suite of evaluation metrics to quantify geo-aware models’ overall performance as well as their geographic bias, with a novel Geo-Bias Score metric. Finally, we provide a detailed analysis and insights into the model performance and geographic bias of different location encoders. We believe TorchSpatial will foster future advancement of spatial representationlearning and spatial fairness in GeoAI research. The TorchSpatial model framework and LocBench benchmark are available at https: //github. com/seai-lab/TorchSpatial, and the Geo-Bias Score evaluation framework is available at https: //github. com/seai-lab/PyGBS.

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