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Jun Jin

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6 papers

2 author rows

EAAI Journal 2026 Journal Article

Laplacian-guided contextual instance learning for whole slide image classification

Jian Chen
Ziyuan Chen
Geng Chen
Mengyu Liu
Sohaib Asif
He Zhang
Jun Jin

Classification plays an important role in the diagnosis and prognosis of cancers such as endometrial and breast cancer. Achieving satisfactory performance in classifying cancer molecular subtypes from whole slide images presents a substantial challenge. This difficulty arises from diverse and complex inter-instance relationships and feature homogeneity among different molecular subtypes. To address these issues, this paper presents a novel Laplacian-guided contextual instance learning (LapCIL) framework, which focuses on learning inter-instance relationships to effectively identify molecular subtypes. The LapCIL framework consists of a dynamic contiguous masking strategy, a contextual instance learning block, and a Laplacian channel classification head. In the LapCIL framework, a dynamic contiguous masking strategy is proposed to generate more inter-instance relationships from finite data. Considering the diversity and complexity of inter-instance relationships, a contextual instance learning block is introduced, which leverages a contextual self-attention mechanism to capture the relationships between different instances. To enhance the distinguishing capability between different instances even further, especially in scenarios where feature homogeneity renders it challenging to differentiate morphologically similar cell types, the LapCIL framework incorporates a Laplacian channel classification head. The Laplacian channel classification head focuses on structured local features and dynamically attends to discriminative channel groups. Extensive experiments are conducted on the CAncer MEtastases in LYmphnOdes challeNge (CAMELYON16) breast cancer dataset, the BReAst Carcinoma Subtyping dataset, and a clinical endometrial cancer dataset to evaluate the proposed LapCIL framework. Our framework achieves significant advantages over state-of-the-art methods, both on the clinical dataset and the CAMELYON16 breast cancer dataset.

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TMLR Journal 2026 Journal Article

Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation

chao ying
Jun Jin
Haotian Zhang
Qinglong Tian
Yanyuan Ma
Sharon Li
Jiwei Zhao

We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label $Y$ and a binary background (or environment) $A$. We focus on a challenging setting in which one such subpopulation in the source domain is unobservable. Naively ignoring this unobserved group can result in biased estimates and degraded predictive performance. Despite this structured missingness, we show that the prediction in the target domain can still be recovered. Specifically, we rigorously derive both background-specific and overall predictive probabilities for the target domain. For practical implementation, we propose the distribution matching method to estimate the subpopulation proportions. We provide theoretical guarantees for the asymptotic behavior of our estimator, and establish an upper bound on the prediction error. Experiments on both synthetic and real-world datasets show that our method outperforms the naive benchmarks that do not account for this unobservable source subpopulation properly.

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IROS Conference 2025 Conference Paper

RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanning

Xi Ye
Rui Heng Yang
Jun Jin
Yinchuan Li
Amir Rasouli

Diffusion models exhibit impressive scalability in robotic task learning, yet they struggle to adapt to novel, highly dynamic environments. This limitation primarily stems from their constrained replanning ability: they either operate at a low frequency due to a time-consuming iterative sampling process, or are unable to adapt to unforeseen feedback in case of rapid replanning. To address these challenges, we propose RA-DP, a novel diffusion policy framework with training-free high-frequency replanning ability that solves the above limitations by adapting to unforeseen dynamic environments. Specifically, our method integrates guidance signals, which are often easily obtained in the new environment during the diffusion sampling process, and utilizes a novel action queue mechanism to generate replanned actions at every denoising step without retraining, thus forming a complete training-free framework for robot motion adaptation in unseen environments. We conduct extensive evaluations in both common simulation benchmarks and real-world environments. Our results indicate that RA-DP outperforms the state-of-the-art diffusion-based methods in terms of replanning frequency and success rate. At the end, we show that our framework is theoretically compatible with any training-free guidance signal, hence increasing its applicability to a wide range of robotics tasks.

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ICML Conference 2025 Conference Paper

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift

Chao Ying
Jun Jin
Yi Guo
Xiudi Li
Muxuan Liang
Jiwei Zhao

Collecting gold-standard phenotype data via manual extraction is typically labor-intensive and slow, whereas automated computational phenotypes (ACPs) offer a systematic and much faster alternative. However, simply replacing the gold-standard with ACPs, without acknowledging their differences, could lead to biased results and misleading conclusions. Motivated by the complexity of incorporating ACPs while maintaining the validity of downstream analyses, in this paper, we consider a semi-supervised learning setting that consists of both labeled data (with gold-standard) and unlabeled data (without gold-standard), under the covariate shift framework. We develop doubly robust and semiparametrically efficient estimators that leverage ACPs for general target parameters in the unlabeled and combined populations. In addition, we carefully analyze the efficiency gains achieved by incorporating ACPs, comparing scenarios with and without their inclusion. Notably, we identify that ACPs for the unlabeled data, instead of for the labeled data, drive the enhanced efficiency gains. To validate our theoretical findings, we conduct comprehensive synthetic experiments and apply our method to multiple real-world datasets, confirming the practical advantages of our approach.

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NeurIPS Conference 2023 Conference Paper

EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought

Yao Mu
Qinglong Zhang
Mengkang Hu
Wenhai Wang
Mingyu Ding
Jun Jin
Bin Wang
Jifeng Dai

Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understanding and execution capabilities. To achieve this, we have made the following efforts: (i) We craft a large-scale embodied planning dataset, termed EgoCOT. The dataset consists of carefully selected videos from the Ego4D dataset, along with corresponding high-quality language instructions. Specifically, we generate a sequence of sub-goals with the "Chain of Thoughts" mode for effective embodied planning. (ii) We introduce an efficient training approach to EmbodiedGPT for high-quality plan generation, by adapting a 7B large language model (LLM) to the EgoCOT dataset via prefix tuning. (iii) We introduce a paradigm for extracting task-related features from LLM-generated planning queries to form a closed loop between high-level planning and low-level control. Extensive experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering. Notably, EmbodiedGPT significantly enhances the success rate of the embodied control task by extracting more effective features. It has achieved a remarkable 1. 6 times increase in success rate on the Franka Kitchen benchmark and a 1. 3 times increase on the Meta-World benchmark, compared to the BLIP-2 baseline fine-tuned with the Ego4D dataset.

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NeurIPS Conference 2022 Conference Paper

A Simple Decentralized Cross-Entropy Method

Zichen Zhang
Jun Jin
Martin Jagersand
Jun Luo
Dale Schuurmans

Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling distribution based on only the top-$k$ operation's results on samples. In this paper, we show that such a centralized approach makes CEM vulnerable to local optima, thus impairing its sample efficiency. To tackle this issue, we propose Decentralized CEM (DecentCEM), a simple but effective improvement over classical CEM, by using an ensemble of CEM instances running independently from one another, and each performing a local improvement of its own sampling distribution. We provide both theoretical and empirical analysis to demonstrate the effectiveness of this simple decentralized approach. We empirically show that, compared to the classical centralized approach using either a single or even a mixture of Gaussian distributions, our DecentCEM finds the global optimum much more consistently thus improves the sample efficiency. Furthermore, we plug in our DecentCEM in the planning problem of MBRL, and evaluate our approach in several continuous control environments, with comparison to the state-of-art CEM based MBRL approaches (PETS and POPLIN). Results show sample efficiency improvement by simply replacing the classical CEM module with our DecentCEM module, while only sacrificing a reasonable amount of computational cost. Lastly, we conduct ablation studies for more in-depth analysis. Code is available at https: //github. com/vincentzhang/decentCEM.

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