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Tianyi Ma

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9 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

AutoData: A Multi-Agent System for Open Web Data Collection

Tianyi Ma
Yiyue Qian
Zheyuan Zhang
Zehong Wang
Xiaoye Qian
Feifan Bai
Yifan Ding
Xuwei Luo

The exponential growth of data-driven systems and AI technologies has intensified the demand for high-quality web-sourced datasets. While existing datasets have proven valuable, conventional web data collection approaches face significant limitations in terms of human effort and scalability. Current data collecting solutions fall into two categories: wrapper-based methods that struggle with adaptability and reproducibility, and large language model (LLM)-based approaches that incur substantial computational and financial costs. To address these challenges, we propose AutoData, a novel multi-agent system for Automated web Data collection, that requires minimal human intervention, i. e. , only necessitating a natural language instruction specifying the desired dataset. In addition, AutoData is designed for a robust multi-agent architecture, featuring a novel oriented message hypergraph coordinated by a central task manager, to efficiently organize agents across research and development squads. Besides, we introduce a novel hypergraph cache system to advance the multi-agent collaboration process that enables efficient automated data collection and mitigates the token cost issues prevalent in existing LLM-based systems. Moreover, we introduce Instruct2DS, a new benchmark dataset supporting live data collection from web sources across three domains: academic, finance, and sports. Comprehensive evaluations over Instruct2DS and three existing benchmark datasets demonstrate AutoData's superior performance compared to baseline methods. Case studies on challenging tasks such as picture book collection and paper extraction from surveys further validate its applicability.

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ICML Conference 2025 Conference Paper

Beyond Message Passing: Neural Graph Pattern Machine

Zehong Wang
Zheyuan Zhang
Tianyi Ma
Nitesh V. Chawla
Chuxu Zhang
Yanfang Ye 0001

Graph learning tasks often hinge on identifying key substructure patterns—such as triadic closures in social networks or benzene rings in molecular graphs—that underpin downstream performance. However, most existing graph neural networks (GNNs) rely on message passing, which aggregates local neighborhood information iteratively and struggles to explicitly capture such fundamental motifs, like triangles, $k$-cliques, and rings. This limitation hinders both expressiveness and long-range dependency modeling. In this paper, we introduce the Neural Graph Pattern Machine (GPM), a novel framework that bypasses message passing by learning directly from graph substructures. GPM efficiently extracts, encodes, and prioritizes task-relevant graph patterns, offering greater expressivity and improved ability to capture long-range dependencies. Empirical evaluations across four standard tasks—node classification, link prediction, graph classification, and graph regression—demonstrate that GPM outperforms state-of-the-art baselines. Further analysis reveals that GPM exhibits strong out-of-distribution generalization, desirable scalability, and enhanced interpretability. Code and datasets are available at: https: //github. com/Zehong-Wang/GPM.

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NeurIPS Conference 2025 Conference Paper

Generative Graph Pattern Machine

Zehong Wang
Zheyuan Zhang
Tianyi Ma
Chuxu Zhang
Yanfang Ye

Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fundamental limitations---including constrained expressiveness, over-smoothing, over-squashing, and limited capacity to model long-range dependencies. These issues hinder scalability: increasing data size or model size often fails to yield improved performance. To this end, we explore pathways beyond message-passing and introduce Generative Graph Pattern Machine (G$^2$PM), a generative Transformer pre-training framework for graphs. G$^2$PM represents graph instances (nodes, edges, or entire graphs) as sequences of substructures, and employs generative pre-training over the sequences to learn generalizable and transferable representations. Empirically, G$^2$PM demonstrates strong scalability: on the ogbn-arxiv benchmark, it continues to improve with model sizes up to 60M parameters, outperforming prior generative approaches that plateau at significantly smaller scales (e. g. , 3M). In addition, we systematically analyze the model design space, highlighting key architectural choices that contribute to its scalability and generalization. Across diverse tasks---including node/link/graph classification, transfer learning, and cross-graph pretraining---G$^2$PM consistently outperforms strong baselines, establishing a compelling foundation for scalable graph learning. The code and dataset are available at https: //github. com/Zehong-Wang/G2PM.

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IROS Conference 2025 Conference Paper

Normalized Triangulation for Calibrated Dual-View 3D Human Pose Estimation

Zijian Zhang
Muqing Wu
Tianyi Ma

In this work, we decouple calibrated dual-view 3D human pose estimation (HPE) into the well-studied problems of 2D pose estimation, and 2D-to-3D pose lifting, focusing on the latter task. The key challenges stem from: 1) 2D pose is noisy and unreliable due to occlusion and motion blur, and 2) the trained model cannot generalize well to unseen camera configurations. To overcome these limitations, we propose three interconnected innovations: First, a Normalized Triangulation that transforms the 2D pose from pixel space to 3D normalized rays, which makes our approach robust to the camera parameters change. Second, a hybrid neural-geometry framework (i. e. , including refinement and triangulation) that explicitly incorporates multi-view geometry into our models. Third, an analytical inverse kinematics (AnalyIK) solver that decomposes articulated motion with human topology, which simultaneously considers symmetry constraint and joint angle limit. Experiments show that the proposed framework achieves state-of-the-art performance on two widely used benchmarks (i. e. , Huamn3. 6M and HumanEva-I). Code is available at: https://github.com/Z-Z-J/Normalized-Triangulation.

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ICLR Conference 2025 Conference Paper

Satisficing Regret Minimization in Bandits

Qing Feng
Tianyi Ma
Ruihao Zhu

Motivated by the concept of satisficing in decision-making, we consider the problem of satisficing exploration in bandit optimization. In this setting, the learner aims at finding a satisficing arm whose mean reward exceeds a certain threshold. The performance is measured by satisficing regret, which is the cumulative deficit of the chosen arm's mean reward compared to the threshold. We propose $\texttt{SELECT}$, a general algorithmic template for Satisficing REgret Minimization via SampLing and LowEr Confidence bound Testing, that attains constant satisficing regret for a wide variety of bandit optimization problems in the realizable case (i.e., whenever a satisficing arm exists). Specifically, given a class of bandit optimization problems and a corresponding learning oracle with sub-linear (standard) regret upper bound, $\texttt{SELECT}$ iteratively makes use of the oracle to identify a potential satisficing arm. Then, it collects data samples from this arm, and continuously compares the lower confidence bound of the identified arm's mean reward against the threshold value to determine if it is a satisficing arm. As a complement, $\texttt{SELECT}$ also enjoys the same (standard) regret guarantee as the oracle in the non-realizable case. Finally, we conduct numerical experiments to validate the performance of $\texttt{SELECT}$ for several popular bandit optimization settings.

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ICML Conference 2025 Conference Paper

Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees

Zehong Wang
Zheyuan Zhang
Tianyi Ma
Nitesh V. Chawla
Chuxu Zhang
Yanfang Ye 0001

Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks—such as contours, textures, and edges in images, or tokens and sentences in text. In contrast, discovering such generalities in graph-structured data, especially across heterogeneous graph tasks, remains an open challenge. To address this, we propose a novel approach to cross-task generalization in graphs via task-trees, which serve as unified learning instances aligning node-, edge-, and graph-level tasks. We theoretically analyze the stability, transferability, and generalization properties of task-trees, showing that pretraining a graph neural network (GNN) on diverse task-trees with a reconstruction objective induces transferable knowledge. This enables efficient adaptation to downstream tasks with minimal fine-tuning. To validate our framework, we introduce Graph Generality Identifier on Task-Trees (GIT), a graph foundation model that demonstrates strong performance on over 30 graphs across five domains via fine-tuning, in-context learning, and zero-shot generalization. Code and data are available at https: //github. com/Zehong-Wang/GIT.

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IJCAI Conference 2019 Conference Paper

Design and Implementation of a Disambiguity Framework for Smart Voice Controlled Devices

Kehua Lei
Tianyi Ma
Jia Jia
Cunjun Zhang
Zhihan Yang

With about 100 million people using it recently, SVCD(Smart Voice Controlled Device) are becoming demotic. Whether at home or in an office, usually, multiple appliances are under the control of a single SVCD and several people may manipulate an SVCD simultaneously. However, present SVCD fails to handle them appropriately. In this paper, we propose a novel framework for SVCD to eliminate orders’ ambiguity for single user or multi-user. We also design an algorithm combining Word2Vec and emotion detection for the device to wipe off ambiguity. Finally, we apply our framework into a virtual smart home scene and the performance of it indicates that our strategy resolves the problems commendably.

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IROS Conference 2018 Conference Paper

Acoustic Tag State Estimation with Unsynchronized Hydrophones on AUVs

Jingnan Shi
Tianyi Ma
Chi-Yen Lee
Eyassu Shimelis
Charles Van Eijk
Christopher M. Clark
Christopher G. Lowe

This paper presents an underwater robotic sensor system for localizing acoustic transmitters when the robot's hydrophones cannot be time-synchronized. The development of the system is motivated by applications where tracking of marine animals that are tagged with an underwater acoustic transmitter is required. The system uses two novel real-time calibration algorithms that improve the accuracy of time of flight (TOF) and time difference of arrival (TDOA) measurements. The first algorithm corrects non-linear clock skews in TOF measurements based on temperature variation. The second algorithm compensates the localized relative clock skew between clocks using a mixed integer linear program. To validate the system's performance, an Autonomous Underwater Vehicle (AUV) was deployed to track a moving tag where GPS data was used as ground truth. Compared to traditional TOF and TDOA filtering methods, the results show that the proposed system can achieve reduction of mean localization errors by 59%, and a reduction of the standard deviation of measurements by 44%.

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TIST Journal 2016 Journal Article

CITY FEED

Linlin You
Gianmario Motta
Kaixu Liu
Tianyi Ma

Crowdsourcing implies user collaboration and engagement, which fosters a renewal of city governance processes. In this article, we address a subset of crowdsourcing, named citizen-sourcing, where citizens interact with authorities collaboratively and actively. Many systems have experimented citizen-sourcing in city governance processes; however, their maturity levels are mixed. In order to focus on the service maturity, we introduce a city service maturity framework that contains five levels of service support and two levels of information integration. As an example, we introduce CITY FEED, which implements citizen-sourcing in city issue management process. In order to support such process, CITY FEED supports all levels of the maturity framework (publishing, transacting, interacting, collaborating, and evaluating) and integrates related information relationally and heterogeneously. In order to integrate heterogeneous information, it implements a threefold feed deduplication mechanism based on the geographic, text semantic, and image similarities of feeds. Currently, CITY FEED is in a pilot stage.

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