Author name cluster

Guo Ye

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7 papers

2 author rows

ICML Conference 2025 Conference Paper

Fast and Low-Cost Genomic Foundation Models via Outlier Removal

Haozheng Luo
Chenghao Qiu
Maojiang Su
Zhihan Zhou 0001
Zoe Mehta
Guo Ye
Jerry Yao-Chieh Hu
Han Liu 0001

To address the challenge of scarce computational resources in genomic modeling, we introduce GERM, a genomic foundation model optimized for accessibility and adaptability. GERM improves upon models like DNABERT-2 by eliminating outliers that hinder low-rank adaptation and post-training quantization, enhancing both efficiency and robustness. We replace the vanilla attention layer with an outlier-free mechanism inspired by associative memory models. By removing outliers during both pre-training and fine-tuning, this approach accelerates adaptation, reduces computational costs, and enhances quantization robustness within acceptable loss margins. Additionally, we propose GERM-T, a strategy that employs small-step continual learning within the outlier-free framework, leveraging original checkpoints to avoid retraining from scratch. Empirically, GERM improves fine-tuning performance by 37. 98% and quantization by 64. 34% over the baseline model. It also reduces average kurtosis by 92. 14% and maximum infinity norm by 82. 77%. Compared to leading methods, GERM consistently delivers superior performance, offering a practical solution for genomic modeling in resource-constrained settings.

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AAAI Conference 2025 Conference Paper

Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation

Wei Chen
Guo Ye
Yakun Wang
Zhao Zhang
Libang Zhang
Daixin Wang
Zhiqiang Zhang
Fuzhen Zhuang

Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs. Existing UGDA methods primarily focus on aligning features in the latent space learned by graph neural networks (GNNs) across domains, often overlooking structural shifts, resulting in limited effectiveness when addressing structurally complex transfer scenarios. Given the sensitivity of GNNs to local structural features, even slight discrepancies between source and target graphs could lead to significant shifts in node embeddings, thereby reducing the effectiveness of knowledge transfer. To address this issue, we introduce a novel approach for UGDA called Target-Domain Structural Smoothing (TDSS). TDSS is a simple and effective method designed to perform structural smoothing directly on the target graph, thereby mitigating structural distribution shifts and ensuring the consistency of node representations. Specifically, by integrating smoothing techniques with neighbor- hood sampling, TDSS maintains the structural coherence of the target graph while mitigating the risk of over-smoothing. Our theoretical analysis shows that TDSS effectively reduces target risk by improving model smoothness. Empirical results on three real-world datasets demonstrate that TDSS outperforms recent state-of-the-art baselines, achieving significant improvements across six transfer scenarios.

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NeurIPS Conference 2024 Conference Paper

Collaborative Refining for Learning from Inaccurate Labels

Bin Han
Yi-Xuan Sun
Ya-Lin Zhang
Libang Zhang
Haoran Hu
Longfei Li
Jun Zhou
Guo Ye

This paper considers the problem of learning from multiple sets of inaccurate labels, which can be easily obtained from low-cost annotators, such as rule-based annotators. Previous works typically concentrate on aggregating information from all the annotators, overlooking the significance of data refinement. This paper presents a collaborative refining approach for learning from inaccurate labels. To refine the data, we introduce the annotator agreement as an instrument, which refers to whether multiple annotators agree or disagree on the labels for a given sample. For samples where some annotators disagree, a comparative strategy is proposed to filter noise. Through theoretical analysis, the connections among multiple sets of labels, the respective models trained on them, and the true labels are uncovered to identify relatively reliable labels. For samples where all annotators agree, an aggregating strategy is designed to mitigate potential noise. Guided by theoretical bounds on loss values, a sample selection criterion is introduced and modified to be more robust against potentially problematic values. Through these two methods, all the samples are refined during training, and these refined samples are used to train a lightweight model simultaneously. Extensive experiments are conducted on benchmark and real-world datasets to demonstrate the superiority of our methods.

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ICRA Conference 2024 Conference Paper

DOS ®: A Deployment Operating System for Robots

Guo Ye
Qinjie Lin
Zening Luo
Han Liu 0001

We propose a new system named DOS ® (Deployment Operating System for Robots) for reliably deploying any data-driven robots in both production and simulation environments. Compared to existing systems, DOS ® features a unique CI/CD (continuous integration and continuous deployment) architecture which allows us to seamlessly integrate agile development and reliable operation in a fully automated fashion. With this CI/CD architecture, this paper mainly introduces three essential components that uniquely differentiate DOS ® from existing robotic systems: (i) An environment adapter that provides a systematic and robust approach to handle the deployment complexity in real world environments; (ii) A data replay reservoir that provides a unified data model supporting arbitrary robotic decision models; (iii) An analytical profiler that collects any set of user-defined performance metrics for system optimization. DOS ® significantly increases the reliability and maintainability of the deployed robotic systems. To illustrate this point, we compare DOS ® with more traditional approaches on deploying a navigational robot in a challenging working environment with many new corner case scenarios. Our results show that DOS ® outperforms traditional approach in great magnitudes in terms of deployment time and operational robustness.

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ICRA Conference 2023 Conference Paper

EMS®: A Massive Computational Experiment Management System towards Data-driven Robotics

Qinjie Lin
Guo Ye
Han Liu 0001

We propose EMS®, a cloud-enabled massive computational experiment management system supporting high-throughput computational robotics research. Compared to existing systems, EMS® features a sky-based pipeline orchestrator which allows us to exploit heterogeneous computing environments painlessly (e. g. , on-premise clusters, public clouds, edge devices) to optimally deploy large-scale computational jobs (e. g. , with more than millions of computational hours) in an integrated fashion. Cornerstoned on this sky-based pipeline orchestrator, this paper introduces three abstraction layers of the EMS® software architecture: (i) Configuration management layer focusing on automatically enumerating experimental configurations; (ii) Dependency management layer focusing on managing the complex task dependencies within each experimental configuration; (iii) Computation management layer focusing on optimally executing the computational tasks using the given computing resource. Such an architectural design greatly increases the scalability and reproducibility of data-driven robotics research leading to much-improved productivity. To demonstrate this point, we compare EMS® with more traditional approaches on an offline reinforcement learning problem for training mobile robots. Our results show that EMS® outperforms more traditional approaches in two magnitudes of orders (in terms of experimental high throughput and cost) with only several lines of code change. We also exploit EMS® to develop mobile robot, robot arm, and bipedal applications, demonstrating its applicability to numerous robot applications.

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ICRA Conference 2020 Conference Paper

Collision-free Navigation of Human-centered Robots via Markov Games

Guo Ye
Qinjie Lin
Tzung-Han Juang
Han Liu 0001

We exploit Markov games as a framework for collision-free navigation of human-centered robots. Unlike the classical methods which formulate robot navigation as a single-agent Markov decision process with a static environment, our framework of Markov games adopts a multi-agent formulation with one primary agent representing the robot and the remaining auxiliary agents form a dynamic or even competing environment. Such a framework allows us to develop a path-following type adversarial training strategy to learn a robust decentralized collision avoidance policy. Through thorough experiments on both simulated and real-world mobile robots, we show that the learnt policy outperforms the state-of-the-art algorithms in both sample complexity and runtime robustness.

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ICLR Conference 2020 Conference Paper

Learning to Plan in High Dimensions via Neural Exploration-Exploitation Trees

Binghong Chen
Bo Dai 0001
Qinjie Lin
Guo Ye
Han Liu 0001
Le Song

We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces. Compared to more classical sampling-based methods like RRT, our approach achieves much better sample efficiency in high-dimensions and can benefit from prior experience of planning in similar environments. More specifically, NEXT exploits a novel neural architecture which can learn promising search directions from problem structures. The learned prior is then integrated into a UCB-type algorithm to achieve an online balance between \emph{exploration} and \emph{exploitation} when solving a new problem. We conduct thorough experiments to show that NEXT accomplishes new planning problems with more compact search trees and significantly outperforms state-of-the-art methods on several benchmarks.

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