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Yuan Jiang 0001

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7 papers

1 author row

ICML Conference 2024 Conference Paper

Tabular Insights, Visual Impacts: Transferring Expertise from Tables to Images

Jun-Peng Jiang
Han-Jia Ye
Leye Wang
Yang Yang 0074
Yuan Jiang 0001
De-Chuan Zhan

Transferring knowledge across diverse data modalities is receiving increasing attention in machine learning. This paper tackles the task of leveraging expert-derived, yet expensive, tabular data to enhance image-based predictions when tabular data is unavailable during inference. The primary challenges stem from the inherent complexity of accurately mapping diverse tabular data to visual contexts, coupled with the necessity to devise distinct strategies for numerical and categorical tabular attributes. We propose CHannel tAbulaR alignment with optiMal tranSport (Charms), which establishes an alignment between image channels and tabular attributes, enabling selective knowledge transfer that is pertinent to visual features. Specifically, Charms measures similarity distributions across modalities to effectively differentiate and transfer relevant tabular features, with a focus on morphological characteristics, enhancing the capabilities of visual classifiers. By maximizing the mutual information between image channels and tabular features, knowledge from both numerical and categorical tabular attributes are extracted. Experimental results demonstrate that Charms not only enhances the performance of image classifiers but also improves their interpretability by effectively utilizing tabular knowledge.

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ECAI Conference 2023 Conference Paper

Identifying Helpful Learnwares Without Examining the Whole Market

Yi Xie
Zhi-Hao Tan
Yuan Jiang 0001
Zhi-Hua Zhou

The learnware paradigm aims to construct a market of numerous well-performing machine learning models, which enables users to leverage these models to accomplish specific tasks without having to build models from scratch. Each learnware in the market is a model associated with a specification, representing the model’s utility and enabling it to be identified according to future users’ requirements. In the learnware paradigm, due to the vast and ever-increasing number of models in the market, a significant challenge is to identify helpful learnwares efficiently for a specific user task without leaking data privacy. However, existing identification methods require examining the whole market, which is computationally unaffordable in a large market. In this paper, we propose a new framework for identifying helpful learnwares without examining the whole market. Specifically, using the Reduced Kernel Mean Embedding (RKME) specification, we derive a novel learnware scoring criterion for assessing the helpfulness of a learnware, based on which we design an anchor-based framework to identify helpful learnwares by examining only a small portion of learnwares in the market. Theoretical analyses are provided for both the criterion and the anchor-based method. Empirical studies on market containing thousands of learnwares from real-world datasets confirm the effectiveness of our proposed approach.

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ICLR Conference 2023 Conference Paper

Seeing Differently, Acting Similarly: Heterogeneously Observable Imitation Learning

Xin-Qiang Cai
Yao-Xiang Ding 0001
Zi-Xuan Chen
Yuan Jiang 0001
Masashi Sugiyama
Zhi-Hua Zhou

In many real-world imitation learning tasks, the demonstrator and the learner have to act under different observation spaces. This situation brings significant obstacles to existing imitation learning approaches, since most of them learn policies under homogeneous observation spaces. On the other hand, previous studies under different observation spaces have strong assumptions that these two observation spaces coexist during the entire learning process. However, in reality, the observation coexistence will be limited due to the high cost of acquiring expert observations. In this work, we study this challenging problem with limited observation coexistence under heterogeneous observations: Heterogeneously Observable Imitation Learning (HOIL). We identify two underlying issues in HOIL: the dynamics mismatch and the support mismatch, and further propose the Importance Weighting with REjection (IWRE) algorithm based on importance weighting and learning with rejection to solve HOIL problems. Experimental results show that IWRE can solve various HOIL tasks, including the challenging tasks of transforming the vision-based demonstrations to random access memory (RAM)-based policies in the Atari domain, even with limited visual observations.

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ICML Conference 2020 Conference Paper

Learning with Feature and Distribution Evolvable Streams

Zhenyu Zhang
Peng Zhao 0006
Yuan Jiang 0001
Zhi-Hua Zhou

In many real-world applications, data are collected in the form of a stream, whose feature space can evolve over time. For instance, in the environmental monitoring task, features can be dynamically vanished or augmented due to the existence of expired old sensors and deployed new sensors. Furthermore, besides the evolvable feature space, the data distribution is usually changing in the streaming scenario. When both feature space and data distribution are evolvable, it is quite challenging to design algorithms with guarantees, particularly theoretical understandings of generalization ability. To address this difficulty, we propose a novel discrepancy measure for data with evolving feature space and data distribution, named the \emph{evolving discrepancy}. Based on that, we present the generalization error analysis, and the theory motivates the design of a learning algorithm which is further implemented by deep neural networks. Empirical studies on synthetic data verify the rationale of our proposed discrepancy measure, and extensive experiments on real-world tasks validate the effectiveness of our algorithm.

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ECAI Conference 2020 Conference Paper

Multi-Label Learning with Deep Forest

Liang Yang
Xi-Zhu Wu
Yuan Jiang 0001
Zhi-Hua Zhou

In multi-label learning, each instance is associated with multiple labels, and the crucial task is how to leverage label correlations in building models. The deep forest is a recent deep learning framework based on decision tree ensembles, which has a cascade structure that can do representation learning like deep neural models and does not rely on backpropagation. Though deep forests have been found useful in classification tasks, the potential of applying it into multi-label learning has not been studied. We consider that the layer-by-layer processing structure of the deep forest is appropriate for solving multi-label problems. Therefore we design the Multi-Label Deep Forest (MLDF) method, including two mechanisms: measure-aware feature reuse and measure-aware layer growth. The measure-aware feature reuse mechanism enables MLDF to reuse better representation in the previous layer. The measure-aware layer growth mechanism ensures MLDF gradually increase the model complexity guided by performance measure. MLDF handles two challenging problems at the same time: one is restricting the model complexity to ease the overfitting issue; another is optimizing the performance measure on user’s demand since there are many different measures in the multi-label evaluation. Experiments demonstrate that our proposal not only beats the compared methods over six measures on benchmark datasets but also enjoys label correlation discovery and other desired properties in multi-label learning.

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ICML Conference 2020 Conference Paper

Safe Deep Semi-Supervised Learning for Unseen-Class Unlabeled Data

Lan-Zhe Guo
Zhenyu Zhang
Yuan Jiang 0001
Yufeng Li 0008
Zhi-Hua Zhou

Deep semi-supervised learning (SSL) has been recently shown very effectively. However, its performance is seriously decreased when the class distribution is mismatched, among which a common situation is that unlabeled data contains some classes not seen in the labeled data. Efforts on this issue remain to be limited. This paper proposes a simple and effective safe deep SSL method to alleviate the harm caused by it. In theory, the result learned from the new method is never worse than learning from merely labeled data, and it is theoretically guaranteed that its generalization approaches the optimal in the order $O(\sqrt{d\ln(n)/n})$, even faster than the convergence rate in supervised learning associated with massive parameters. In the experiment of benchmark data, unlike the existing deep SSL methods which are no longer as good as supervised learning in 40% of unseen-class unlabeled data, the new method can still achieve performance gain in more than 60% of unseen-class unlabeled data. Moreover, the proposal is suitable for many deep SSL algorithms and can be easily extended to handle other cases of class distribution mismatch.

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ICML Conference 2018 Conference Paper

Rectify Heterogeneous Models with Semantic Mapping

Han-Jia Ye
De-Chuan Zhan
Yuan Jiang 0001
Zhi-Hua Zhou

On the way to the robust learner for real-world applications, there are still great challenges, including considering unknown environments with limited data. Learnware (Zhou; 2016) describes a novel perspective, and claims that learning models should have reusable and evolvable properties. We propose to Encode Meta InformaTion of features (EMIT), as the model specification for characterizing the changes, which grants the model evolvability to bridge heterogeneous feature spaces. Then, pre-trained models from related tasks can be Reused by our REctiFy via heterOgeneous pRedictor Mapping (REFORM}) framework. In summary, the pre-trained model is adapted to a new environment with different features, through model refining on only a small amount of training data in the current task. Experimental results over both synthetic and real-world tasks with diverse feature configurations validate the effectiveness and practical utility of the proposed framework.

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