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Yijie Wang

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10 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

Distributionally Robust Performative Optimization

Zhuangzhuang Jia
Yijie Wang
Roy Dong
Grani A. Hanasusanto

In performative stochastic optimization, decisions can influence the distribution of random parameters, rendering the data-generating process itself decision-dependent. In practice, decision-makers rarely have access to the true distribution map and must instead rely on imperfect surrogate models, which can lead to severely suboptimal solutions under misspecification. Data scarcity or costly collection further exacerbates these challenges in real-world settings. To address these challenges, we propose a distributionally robust framework for performative optimization that explicitly accounts for ambiguity in the decision-dependent distribution. Our framework introduces three modeling paradigms that capture a broad range of applications in machine learning and decision-making under uncertainty. This latter setting has not previously been explored in the performative optimization literature. To tackle the intractability of the resulting nonconvex objectives, we develop an iterative algorithm named repeated robust risk minimization, which alternates between solving a decision-independent distributionally robust optimization problem and updating the ambiguity set based on the previous decision. This decoupling ensures computational tractability at each iteration while enhancing robustness to model uncertainty. We provide reformulations compatible with off-the-shelf solvers and establish theoretical guarantees on convergence and suboptimality. Extensive numerical experiments in strategic classification, revenue management, and portfolio optimization demonstrate significant performance gains over state-of-the-art baselines, highlighting the practical value of our approach.

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ECAI Conference 2025 Conference Paper

DRLServe: Adaptive Prefill Chunking with Deep Reinforcement Learning for LLM Inference

Chongxiang Sun
Han Bao
Yijie Wang

In traditional LLM inference, the prefill phase necessitates exclusive access to the GPU, forcing mutually exclusive execution between the prefill and decode phases. This makes it challenging to balance Time to First Token (TTFT) and Time Between Tokens (TBT). Existing chunked-prefills techniques decompose prefill tasks into non-exclusive GPU chunks for batch execution, enabling parallel execution of prefill and decode, improving TTFT-TBT trade-offs. However, current chunked-prefills techniques rely on artificial experience to set static chunk sizes, making it difficult to optimize TTFT-TBT trade-offs under complex workloads. Consequently, it is necessary to dynamically adjust the prefill chunk size without interrupting the LLM inference. However, it is difficult because LLM inference scenarios are real-time and concurrent, and the state information is high-dimensional. To address this, we propose DRLServe - an adaptive chunked prefill inference technology based on deep reinforcement learning, which significantly enhances TTFT-TBT trade-offs by dynamically adjusting the chunk size based on real-time load and system resource utilisation. Specifically, we propose a real-time adaptive prefill chunking inference framework (RAPC), which decouples inference and chunk size adjustment, allowing it to complete chunk size adjustment without interrupting the existing inference. RAPC implements a resource and task-aware dual-driven chunk size decision mechanism, which can obtain resource status and inference task characteristics in real-time and dynamically adjust the chunk size. Then, we present a reinforcement learning algorithm for real-time prefill chunk partitioning (TAPPO). For the first time, we modelled LLM inference as a Markov Decision Process (MDP). We used reinforcement learning to learn the chunk partitioning strategy, aiming to achieve adaptive decision-making of prefill chunk size under constraints of inference latency and training stability, to optimise the TTFT-TBT trade-offs. Experiments show that compared with the latest inference technology, DRLServe can shorten TTFT by 50. 9%, TBT mean by 64. 6%, TBT variance by 99. 0%.

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UAI Conference 2024 Conference Paper

Bias-aware Boolean Matrix Factorization Using Disentangled Representation Learning

Xiao Wang 0099
Jia Wang
Tong Zhao 0002
Yijie Wang
Nan Zhang
Yong Zang
Sha Cao
Chi Zhang 0021

Boolean matrix factorization (BMF) has been widely utilized in fields such as recommendation systems, graph learning, text mining, and -omics data analysis. Traditional BMF methods decompose a binary matrix into the Boolean product of two lower-rank Boolean matrices plus homoscedastic random errors. However, real-world binary data typically involves biases arising from heterogeneous row- and column-wise signal distributions. Such biases can lead to suboptimal fitting and unexplainable predictions if not accounted for. In this study, we reconceptualize the binary data generation as the Boolean sum of three components: a binary pattern matrix, a background bias matrix influenced by heterogeneous row or column distributions, and random flipping errors. We introduce a novel Disentangled Representation Learning for Binary matrices (DRLB) method, which employs a dual auto-encoder network to reveal the true patterns. DRLB can be seamlessly integrated with existing BMF techniques to facilitate bias-aware BMF. Our experiments with both synthetic and real-world datasets show that DRLB significantly enhances the precision of traditional BMF methods while offering high scalability. Moreover, the bias matrix detected by DRLB accurately reflects the inherent biases in synthetic data, and the patterns identified in the bias-corrected real-world data exhibit enhanced interpretability.

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AAAI Conference 2024 Conference Paper

Data Distribution Distilled Generative Model for Generalized Zero-Shot Recognition

Yijie Wang
Mingjian Hong
Luwen Huangfu
Sheng Huang

In the realm of Zero-Shot Learning (ZSL), we address biases in Generalized Zero-Shot Learning (GZSL) models, which favor seen data. To counter this, we introduce an end-to-end generative GZSL framework called D3GZSL. This framework respects seen and synthesized unseen data as in-distribution and out-of-distribution data, respectively, for a more balanced model. D3GZSL comprises two core modules: in-distribution dual space distillation (ID2SD) and out-of-distribution batch distillation (O2DBD). ID2SD aligns teacher-student outcomes in embedding and label spaces, enhancing learning coherence. O2DBD introduces low-dimensional out-of-distribution representations per batch sample, capturing shared structures between seen and un seen categories. Our approach demonstrates its effectiveness across established GZSL benchmarks, seamlessly integrating into mainstream generative frameworks. Extensive experiments consistently showcase that D3GZSL elevates the performance of existing generative GZSL methods, under scoring its potential to refine zero-shot learning practices. The code is available at: https://github.com/PJBQ/D3GZSL.git

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JBHI Journal 2023 Journal Article

CDNet: Contrastive Disentangled Network for Fine-Grained Image Categorization of Ocular B-Scan Ultrasound

Ruilong Dan
Yunxiang Li
Yijie Wang
Xiaodiao Chen
Gangyong Jia
Shuai Wang
Ruiquan Ge
Guiping Qian

Precise and rapid categorization of images in the B-scan ultrasound modality is vital for diagnosing ocular diseases. Nevertheless, distinguishing various diseases in ultrasound still challenges experienced ophthalmologists. Thus a novel contrastive disentangled network (CDNet) is developed in this work, aiming to tackle the fine-grained image categorization (FGIC) challenges of ocular abnormalities in ultrasound images, including intraocular tumor (IOT), retinal detachment (RD), posterior scleral staphyloma (PSS), and vitreous hemorrhage (VH). Three essential components of CDNet are the weakly-supervised lesion localization module (WSLL), contrastive multi-zoom (CMZ) strategy, and hyperspherical contrastive disentangled loss (HCD-Loss), respectively. These components facilitate feature disentanglement for fine-grained recognition in both the input and output aspects. The proposed CDNet is validated on our ZJU Ocular Ultrasound Dataset (ZJUOUSD), consisting of 5213 samples. Furthermore, the generalization ability of CDNet is validated on two public and widely-used chest X-ray FGIC benchmarks. Quantitative and qualitative results demonstrate the efficacy of our proposed CDNet, which achieves state-of-the-art performance in the FGIC task.

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ICLR Conference 2023 Conference Paper

Learning Sparse Group Models Through Boolean Relaxation

Yijie Wang
Yuan Zhou 0007
Xiaoqing Huang
Kun Huang
Jie Zhang
Jianzhu Ma

We introduce an efficient algorithmic framework for learning sparse group models formulated as the natural convex relaxation of a cardinality-constrained program with Boolean variables. We provide theoretical techniques to characterize the equivalent condition when the relaxation achieves the exact integral optimal solution, as well as a rounding algorithm to produce a feasible integral solution once the optimal relaxation solution is fractional. We demonstrate the power of our equivalent condition by applying it to two ensembles of random problem instances that are challenging and popularly used in literature and prove that our method achieves exactness with overwhelming probability and nearly optimal sample complexity. Empirically, we use synthetic datasets to demonstrate that our proposed method significantly outperforms the state-of-the-art group sparse learning models in terms of individual and group support recovery when the number of samples is small. Furthermore, we show the out-performance of our method in cancer drug response prediction.

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NeurIPS Conference 2021 Conference Paper

Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics

Man Shun Ang
Jianzhu Ma
Nianjun Liu
Kun Huang
Yijie Wang

We consider the problem of projecting a vector onto the so-called k-capped simplex, which is a hyper-cube cut by a hyperplane. For an n-dimensional input vector with bounded elements, we found that a simple algorithm based on Newton's method is able to solve the projection problem to high precision with a complexity roughly about O(n), which has a much lower computational cost compared with the existing sorting-based methods proposed in the literature. We provide a theory for partial explanation and justification of the method. We demonstrate that the proposed algorithm can produce a solution of the projection problem with high precision on large scale datasets, and the algorithm is able to significantly outperform the state-of-the-art methods in terms of runtime (about 6-8 times faster than a commercial software with respect to CPU time for input vector with 1 million variables or more). We further illustrate the effectiveness of the proposed algorithm on solving sparse regression in a bioinformatics problem. Empirical results on the GWAS dataset (with 1, 500, 000 single-nucleotide polymorphisms) show that, when using the proposed method to accelerate the Projected Quasi-Newton (PQN) method, the accelerated PQN algorithm is able to handle huge-scale regression problem and it is more efficient (about 3-6 times faster) than the current state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Neighborhood Consensus Networks for Unsupervised Multi-view Outlier Detection

Li Cheng
Yijie Wang
Xinwang Liu

Multi-view outlier detection recently attracted rapidly growing attention with the development of multi-view learning. Although promising performance demonstrated, we observe that identifying outliers in multi-view data is still a challenging task due to the complicated characteristics of multi-view data. Specifically, an effective multi-view outlier detection method should be able to handle (1) different types of outliers; (2) two or more views; (3) samples without clusters; (4) high dimensional data. Unfortunately, little is known about how these four issues can be handled simultaneously. In this paper, we propose an unsupervised multi-view outlier detection method to address these issues. Our method is based on the proposed novel neighborhood consensus networks termed NC-Nets, which automatically encodes intrinsic information into a comprehensive latent space for each view (for issue (4)) and uniforms the neighborhood structures among different views (for issue (2)). Accordingly, we propose an outlier score measurement which consists of two parts: the withinview reconstruction score and the cross-view neighborhood consensus score. The measurement is designed based on the characteristics of the different outlier types (for issue (1)) and no cluster assumption is needed (for issue (3)). Experimental results show that our method significantly outperforms state-of-the-art methods. On average, our method achieves 11. 2% ∼ 96. 2% improvement in term of AUC and 33. 5% ∼ 352. 7% improvement in term of F1-Score.

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AAAI Conference 2020 Conference Paper

Outlier Detection Ensemble with Embedded Feature Selection

Li Cheng
Yijie Wang
Xinwang Liu
Bin Li

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Speciﬁcally, for each random sub-sampling based learning component, ODEFS uniﬁes feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.

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AAAI Conference 2019 Conference Paper

Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical Data

Hongzuo Xu
Yongjun Wang
Zhiyue Wu
Yijie Wang

Non-IID categorical data is ubiquitous and common in realworld applications. Learning various kinds of couplings has been proved to be a reliable measure when detecting outliers in such non-IID data. However, it is a critical yet challenging problem to model, represent, and utilise high-order complex value couplings. Existing outlier detection methods normally only focus on pairwise primary value couplings and fail to uncover real relations that hide in complex couplings, resulting in suboptimal and unstable performance. This paper introduces a novel unsupervised embedding-based complex value coupling learning framework EMAC and its instance SCAN to address these issues. SCAN first models primary value couplings. Then, coupling bias is defined to capture complex value couplings with different granularities and highlight the essence of outliers. An embedding method is performed on the value network constructed via biased value couplings, which further learns high-order complex value couplings and embeds these couplings into a value representation matrix. Bidirectional selective value coupling learning is proposed to show how to estimate value and object outlierness through value couplings. Substantial experiments show that SCAN (i) significantly outperforms five state-of-the-art outlier detection methods on thirteen real-world datasets; and (ii) has much better resilience to noise than its competitors.

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