Liping Jing

AAAI Conference 2026 Conference Paper

Learning Neural Operators from Partial Observations via Latent Autoregressive Modeling

• Jingren Hou
• Hong Wang
• Pengyu Xu
• Chang Gao
• Huafeng Liu
• Liping Jing

Real-world scientific applications frequently encounter incomplete observational data due to sensor limitations, geographic constraints, or measurement costs. Although neural operators significantly advanced PDE solving in terms of computational efficiency and accuracy, their underlying assumption of fully-observed spatial inputs severely restricts applicability in real-world application. We introduce the first systematic framework for learning neural operators from partial observation. We identify and formalize two fundamental obstacles: (i) the supervision gap in unobserved regions that prevents effective learning of physical correlations, and (ii) the dynamic spatial mismatch between incomplete inputs and complete solution fields. Specifically, our proposed LANO (Latent Autoregressive Neural Operator) introduces two novel components designed explicitly to address the core difficulties of partial observations: (i) a mask-to-predict training strategy that creates artificial supervision by strategically masking observed regions, and (ii) a Physics-Aware Latent Propagator that reconstructs solutions through boundary-first autoregressive generation in latent space. Additionally, we develop POBench-PDE, a dedicated and comprehensive benchmark designed specifically for evaluating neural operators under partial observation conditions across three PDE-governed tasks. LANO achieves state-of-the-art performance with relative error reductions ranging from eighteen to sixty-nine percent across all benchmarks under patch-wise missingness with missing rates below fifty percent, including real-world climate prediction. Our approach effectively addresses practical scenarios with missing rates of up to seventy-five percent, to some extent bridging the existing gap between idealized research settings and the complexities of real-world scientific computing.

AAAI Conference 2026 Conference Paper

MetaGameBO: Hierarchical Game-Theoretic Driven Robust Meta-Learning for Bayesian Optimization

• Hui Li
• Huafeng Liu
• Yiran Fu
• Shuyang Lin
• Baoxin Zhang
• Deqiang Ouyang
• Liping Jing
• Jian Yu

Meta-learning for Bayesian optimization accelerates optimization by leveraging knowledge from previous tasks, but existing methods optimize for average performance and fail on challenging outlier tasks critical in practice. These limitations become particularly severe when target tasks exhibit distribution shifts or when optimization budgets are limited in real-world applications. We introduce MetaGameBO, a hierarchical game-theoretic framework that formulates meta-learning as robust optimization through CVaR-based task selection and diversity-aware sample learning. Our approach incorporates uncertainty-aware adaptation via probabilistic embeddings and Thompson sampling for robust generalization to out-of-distribution targets. We establish theoretical guarantees including convergence to game-theoretic equilibria and improved sample complexity, and demonstrate substantial improvements with 95.7% reduction in average loss and 88.6% lower tail risk compared to state-of-the-art methods on challenging tasks and distribution shifts.

AAAI Conference 2026 Conference Paper

MotivDance: Fine-Grained Text-Guided Motivation Choreography with Music Synchronization

• Chenguang Li
• Yu-Hui Wen
• Liping Jing

Realistic choreography demands simultaneous attention to rhythm and motivation. Prevailing automated dance generation methods mainly depend on musical input, overlooking the motivations that drive meaningful dance creation. Inspired by the motivation choreography, we aim to articulate dance motivations through textual guidance. However, the absence of high-quality datasets concurrently containing music, textual descriptions, and motion data presents a challenge in achieving accurate fine-grained textual control. To address this limitation, we present MotivDance, a novel framework integrating fine-grained textual guidance with music to synthesize semantically coherent dance sequences. Our approach first synthesizes text-guided key poses as motivations. We then introduce an Adaptive Keyframe Locator that dynamically positions these motivations within the musical context through beat-aware synchronization and cross-modal latent space alignment. Finally, a Transformer-based U-Net diffusion model performs the motion in-betweening while preserving motivational integrity. Extensive qualitative and quantitative experiments demonstrate that MotivDance effectively integrates music with fine-grained text control to generate high-fidelity dance motions.

ICML Conference 2025 Conference Paper

Learning Robust Neural Processes with Risk-Averse Stochastic Optimization

• Huafeng Liu 0001
• Yiran Fu
• Liping Jing
• Hui Li
• Shuyang Lin
• Jingyue Shi
• Deqiang Ouyang
• Jian Yu 0001

Neural processes (NPs) are a promising paradigm to enable skill transfer learning across tasks with the aid of the distribution of functions. The previous NPs employ the empirical risk minimization principle in optimization. However, the fast adaption ability to different tasks can vary widely, and the worst fast adaptation can be catastrophic in risk-sensitive tasks. To achieve robust neural processes modeling, we consider the problem of training models in a risk-averse manner, which can control the worst fast adaption cases at a certain probabilistic level. By transferring the risk minimization problem to a two-level finite sum minimax optimization problem, we can easily solve it via a double-looped stochastic mirror prox algorithm with a task-aware variance reduction mechanism via sampling samples across all tasks. The mirror prox technique ensures better handling of complex constraint sets and non-Euclidean geometries, making the optimization adaptable to various tasks. The final solution, by aggregating prox points with the adaptive learning rates, enables a stable and high-quality output. The proposed learning strategy can work with various NPs flexibly and achieves less biased approximation with a theoretical guarantee. To illustrate the superiority of the proposed model, we perform experiments on both synthetic and real-world data, and the results demonstrate that our approach not only helps to achieve more accurate performance but also improves model robustness.

NeurIPS Conference 2025 Conference Paper

Learning to Generalize: An Information Perspective on Neural Processes

• Hui Li
• Huafeng Liu
• Shuyang Lin
• Jingyue Shi
• Yiran Fu
• Liping Jing

Neural Processes (NPs) combine the adaptability of neural networks with the efficiency of meta-learning, offering a powerful framework for modeling stochastic processes. However, existing methods focus on empirical performance while lacking a rigorous theoretical understanding of generalization. To address this, we propose an information-theoretic framework to analyze the generalization bounds of NPs, introducing dynamical stability regularization to minimize sharpness and improve optimization dynamics. Additionally, we show how noise-injected parameter updates complement this regularization. The proposed approach, applicable to a wide range of NP models, is validated through experiments on classic benchmarks, including 1D regression, image completion, Bayesian optimization, and contextual bandits. The results demonstrate tighter generalization bounds and superior predictive performance, establishing a principled foundation for advancing generalizable NP models.

AAAI Conference 2025 Conference Paper

SS-GEN: A Social Story Generation Framework with Large Language Models

• Yi Feng
• Mingyang Song
• Jiaqi Wang
• Zhuang Chen
• Guanqun Bi
• Minlie Huang
• Liping Jing
• Jian Yu

Children with Autism Spectrum Disorder (ASD) often misunderstand social situations and struggle to participate in daily routines. Social Stories™ are traditionally crafted by psychology experts under strict constraints to address these challenges but are costly and limited in diversity. As Large Language Models (LLMs) advance, there's an opportunity to develop more automated, affordable, and accessible methods to generate Social Stories in real-time with broad coverage. However, adapting LLMs to meet the unique and strict constraints of Social Stories is a challenging issue. To this end, we propose SS-GEN, a Social Story GENeration framework with LLMs. Firstly, we develop a constraint-driven sophisticated strategy named StarSow to hierarchically prompt LLMs to generate Social Stories at scale, followed by rigorous human filtering to build a high-quality dataset. Additionally, we introduce quality assessment criteria to evaluate the effectiveness of these generated stories. Considering that powerful closed-source large models require very complex instructions and expensive API fees, we finally fine-tune smaller language models with our curated high-quality dataset, achieving comparable results at lower costs and with simpler instruction and deployment. This work marks a significant step in leveraging AI to personalize Social Stories cost-effectively for autistic children at scale, which we hope can encourage future research on special groups.

AAAI Conference 2023 Conference Paper

Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification

• Pengyu Xu
• Lin Xiao
• Bing Liu
• Sijin Lu
• Liping Jing
• Jian Yu

Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the performance of MLTC. To facilitate this low-resource problem, researchers introduced a simple but effective strategy, data augmentation (DA). However, most existing DA approaches struggle in multi-label settings. The main reason is that the augmented documents for one label may inevitably influence the other co-occurring labels and further exaggerate the long-tailed problem. To mitigate this issue, we propose a new pair-level augmentation framework for MLTC, called Label-Specific Feature Augmentation (LSFA), which merely augments positive feature-label pairs for the tail-labels. LSFA contains two main parts. The first is for label-specific document representation learning in the high-level latent space, the second is for augmenting tail-label features in latent space by transferring the documents second-order statistics (intra-class semantic variations) from head labels to tail labels. At last, we design a new loss function for adjusting classifiers based on augmented datasets. The whole learning procedure can be effectively trained. Comprehensive experiments on benchmark datasets have shown that the proposed LSFA outperforms the state-of-the-art counterparts.

NeurIPS Conference 2023 Conference Paper

Neural Processes with Stability

• Huafeng Liu
• Liping Jing
• Jian Yu

Unlike traditional statistical models depending on hand-specified priors, neural processes (NPs) have recently emerged as a class of powerful neural statistical models that combine the strengths of neural networks and stochastic processes. NPs can define a flexible class of stochastic processes well suited for highly non-trivial functions by encoding contextual knowledge into the function space. However, noisy context points introduce challenges to the algorithmic stability that small changes in training data may significantly change the models and yield lower generalization performance. In this paper, we provide theoretical guidelines for deriving stable solutions with high generalization by introducing the notion of algorithmic stability into NPs, which can be flexible to work with various NPs and achieves less biased approximation with theoretical guarantees. To illustrate the superiority of the proposed model, we perform experiments on both synthetic and real-world data, and the results demonstrate that our approach not only helps to achieve more accurate performance but also improves model robustness.

NeurIPS Conference 2023 Conference Paper

Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation

• Yilin Lyu
• Liyuan Wang
• Xingxing Zhang
• Zicheng Sun
• Hang Su
• Jun Zhu
• Liping Jing

Continual learning entails learning a sequence of tasks and balancing their knowledge appropriately. With limited access to old training samples, much of the current work in deep neural networks has focused on overcoming catastrophic forgetting of old tasks in gradient-based optimization. However, the normalization layers provide an exception, as they are updated interdependently by the gradient and statistics of currently observed training samples, which require specialized strategies to mitigate recency bias. In this work, we focus on the most popular Batch Normalization (BN) and provide an in-depth theoretical analysis of its sub-optimality in continual learning. Our analysis demonstrates the dilemma between balance and adaptation of BN statistics for incremental tasks, which potentially affects training stability and generalization. Targeting on these particular challenges, we propose Adaptive Balance of BN (AdaB$^2$N), which incorporates appropriately a Bayesian-based strategy to adapt task-wise contributions and a modified momentum to balance BN statistics, corresponding to the training and testing stages. By implementing BN in a continual learning fashion, our approach achieves significant performance gains across a wide range of benchmarks, particularly for the challenging yet realistic online scenarios (e. g. , up to 7. 68\%, 6. 86\% and 4. 26\% on Split CIFAR-10, Split CIFAR-100 and Split Mini-ImageNet, respectively). Our code is available at https: //github. com/lvyilin/AdaB2N.

IJCAI Conference 2023 Conference Paper

Recognizable Information Bottleneck

• Yilin Lyu
• Xin Liu
• Mingyang Song
• Xinyue Wang
• Yaxin Peng
• Tieyong Zeng
• Liping Jing

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalization in real-world scenarios due to the vacuous generalization bound. The recent PAC-Bayes IB uses information complexity instead of information compression to establish a connection with the mutual information generalization bound. However, it requires the computation of expensive second-order curvature, which hinders its practical application. In this paper, we establish the connection between the recognizability of representations and the recent functional conditional mutual information (f-CMI) generalization bound, which is significantly easier to estimate. On this basis we propose a Recognizable Information Bottleneck (RIB) which regularizes the recognizability of representations through a recognizability critic optimized by density ratio matching under the Bregman divergence. Extensive experiments on several commonly used datasets demonstrate the effectiveness of the proposed method in regularizing the model and estimating the generalization gap.

NeurIPS Conference 2022 Conference Paper

Amortized Mixing Coupling Processes for Clustering

• Huafeng Liu
• Liping Jing

Considering the ever-increasing scale of data, which may contain tens of thousands of data points or complicated latent structures, the issue of scalability and algorithmic efficiency becomes of vital importance for clustering. In this paper, we propose cluster-wise amortized mixing coupling processes (AMCP), which is able to achieve efficient amortized clustering in a well-defined non-parametric Bayesian posterior. Specifically, AMCP learns clusters sequentially with the aid of the proposed intra-cluster mixing (IntraCM) and inter-cluster coupling (InterCC) strategies, which investigate the relationship between data points and reference distribution in a linear optimal transport mixing view, and coupling the unassigned set and assigned set to generate new cluster. IntraCM and InterCC avoid pairwise calculation of distances between clusters and reduce the computational complexity from quadratic to linear in the current number of clusters. Furthermore, cluster-wise sequential process is able to improve the quick adaptation ability for the next cluster generation. In this case, AMCP simultaneously learns what makes a cluster, how to group data points into clusters, and how to adaptively control the number of clusters. To illustrate the superiority of the proposed method, we perform experiments on both synthetic data and real-world data in terms of clustering performance and computational efficiency. The source code is available at https: //github. com/HuafengHK/AMCP.

NeurIPS Conference 2022 Conference Paper

Divert More Attention to Vision-Language Tracking

• Mingzhe Guo
• Zhipeng Zhang
• Heng Fan
• Liping Jing

Relying on Transformer for complex visual feature learning, object tracking has witnessed the new standard for state-of-the-arts (SOTAs). However, this advancement accompanies by larger training data and longer training period, making tracking increasingly expensive. In this paper, we demonstrate that the Transformer-reliance is not necessary and the pure ConvNets are still competitive and even better yet more economical and friendly in achieving SOTA tracking. Our solution is to unleash the power of multimodal vision-language (VL) tracking, simply using ConvNets. The essence lies in learning novel unified-adaptive VL representations with our modality mixer (ModaMixer) and asymmetrical ConvNet search. We show that our unified-adaptive VL representation, learned purely with the ConvNets, is a simple yet strong alternative to Transformer visual features, by unbelievably improving a CNN-based Siamese tracker by 14. 5% in SUC on challenging LaSOT (50. 7%$\rightarrow$65. 2%), even outperforming several Transformer-based SOTA trackers. Besides empirical results, we theoretically analyze our approach to evidence its effectiveness. By revealing the potential of VL representation, we expect the community to divert more attention to VL tracking and hope to open more possibilities for future tracking beyond Transformer. Code and models are released at https: //github. com/JudasDie/SOTS.

IJCAI Conference 2022 Conference Paper

Learning Target-aware Representation for Visual Tracking via Informative Interactions

• Mingzhe Guo
• Zhipeng Zhang
• Heng Fan
• Liping Jing
• Yilin Lyu
• Bing Li
• Weiming Hu

We introduce a novel backbone architecture to improve target-perception ability of feature representation for tracking. Having observed de facto frameworks perform feature matching simply using the backbone outputs for target localization, there is no direct feedback from the matching module to the backbone network, especially the shallow layers. Concretely, only the matching module can directly access the target information, while the representation learning of candidate frame is blind to the reference target. Therefore, the accumulated target-irrelevant interference in shallow stages may degrade the feature quality of deeper layers. In this paper, we approach the problem by conducting multiple branch-wise interactions inside the Siamese-like backbone networks (InBN). The core of InBN is a general interaction modeler (GIM) that injects the target information to different stages of the backbone network, leading to better target-perception of candidate feature representation with negligible computation cost. The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced on multiple benchmarks. In particular, the CNN version improves the baseline with 3. 2/6. 9 absolute gains of SUC on LaSOT/TNL2K. The Transformer version obtains SUC of 65. 7/52. 0 on LaSOT/TNL2K, which are on par with recent SOTAs.

AAAI Conference 2021 Conference Paper

Does Head Label Help for Long-Tailed Multi-Label Text Classification

• Lin Xiao
• Xiangliang Zhang
• Liping Jing
• Chi Huang
• Mingyang Song

Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i. e. , a few labels are associated with a large number of documents (a. k. a. head labels), while a large fraction of labels are associated with a small number of documents (a. k. a. tail labels). To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. The meta-knowledge is the mapping from fewshot network parameters to many-shot network parameters, which aims to promote the generalizability of tail classifiers. Extensive experimental results on three benchmark datasets demonstrate that HTTN consistently outperforms the stateof-the-art methods. The code and hyper-parameter settings are released for reproducibility1.

JMLR Journal 2021 Journal Article

Interpretable Deep Generative Recommendation Models

• Huafeng Liu
• Liping Jing
• Jingxuan Wen
• Pengyu Xu
• Jiaqi Wang
• Jian Yu
• Michael K. Ng

User preference modeling in recommendation system aims to improve customer experience through discovering users’ intrinsic preference based on prior user behavior data. This is a challenging issue because user preferences usually have complicated structure, such as inter-user preference similarity and intra-user preference diversity. Among them, inter-user similarity indicates different users may share similar preference, while intra-user diversity indicates one user may have several preferences. In literatures, deep generative models have been successfully applied in recommendation systems due to its flexibility on statistical distributions and strong ability for non-linear representation learning. However, they suffer from the simple generative process when handling complex user preferences. Meanwhile, the latent representations learned by deep generative models are usually entangled, and may range from observed-level ones that dominate the complex correlations between users, to latent-level ones that characterize a user’s preference, which makes the deep model hard to explain and unfriendly for recommendation. Thus, in this paper, we propose an Interpretable Deep Generative Recommendation Model (InDGRM) to characterize inter-user preference similarity and intra-user preference diversity, which will simultaneously disentangle the learned representation from observed-level and latent-level. In InDGRM, the observed-level disentanglement on users is achieved by modeling the user-cluster structure (i.e., inter-user preference similarity) in a rich multimodal space, so that users with similar preferences are assigned into the same cluster. The observed-level disentanglement on items is achieved by modeling the intra-user preference diversity in a prototype learning strategy, where different user intentions are captured by item groups (one group refers to one intention). To promote disentangled latent representations, InDGRM adopts structure and sparsity-inducing penalty and integrates them into the generative procedure, which has ability to enforce each latent factor focus on a limited subset of items (e.g., one item group) and benefit latent-level disentanglement. Meanwhile, it can be efficiently inferred by minimizing its penalized upper bound with the aid of local variational optimization technique. Theoretically, we analyze the generalization error bound of InDGRM to guarantee its performance. A series of experimental results on four widely-used benchmark datasets demonstrates the superiority of InDGRM on recommendation performance and interpretability. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

ICLR Conference 2021 Conference Paper

Probing BERT in Hyperbolic Spaces

• Boli Chen
• Yao Fu
• Guangwei Xu
• Pengjun Xie
• Chuanqi Tan
• Mosha Chen
• Liping Jing

Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embeddings lay in certain metric spaces, typically the Euclidean space. This work considers a family of geometrically special spaces, the hyperbolic spaces, that exhibit better inductive biases for hierarchical structures and may better reveal linguistic hierarchies encoded in contextualized representations. We introduce a $\textit{Poincaré probe}$, a structural probe projecting these embeddings into a Poincaré subspace with explicitly defined hierarchies. We focus on two probing objectives: (a) dependency trees where the hierarchy is defined as head-dependent structures; (b) lexical sentiments where the hierarchy is defined as the polarity of words (positivity and negativity). We argue that a key desideratum of a probe is its sensitivity to the existence of linguistic structures. We apply our probes on BERT, a typical contextualized embedding model. In a syntactic subspace, our probe better recovers tree structures than Euclidean probes, revealing the possibility that the geometry of BERT syntax may not necessarily be Euclidean. In a sentiment subspace, we reveal two possible meta-embeddings for positive and negative sentiments and show how lexically-controlled contextualization would change the geometric localization of embeddings. We demonstrate the findings with our Poincaré probe via extensive experiments and visualization. Our results can be reproduced at https://github.com/FranxYao/PoincareProbe

AAAI Conference 2020 Conference Paper

Hyperbolic Interaction Model for Hierarchical Multi-Label Classification

• Boli Chen
• Xin Huang
• Lin Xiao
• Zixin Cai
• Liping Jing

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.

AIJ Journal 2019 Journal Article

Clustering ensemble based on sample's stability

• Feijiang Li
• Yuhua Qian
• Jieting Wang
• Chuangyin Dang
• Liping Jing

AAAI Conference 2018 Short Paper

Discriminative Semi-Supervised Feature Selection via Rescaled Least Squares Regression-Supplement

• Guowen Yuan
• Xiaojun Chen
• Chen Wang
• Feiping Nie
• Liping Jing

In this paper, we propose a Discriminative Semi-Supervised Feature Selection (DSSFS) method. In this method, a dragging technique is introduced to the Rescaled Linear Square Regression in order to enlarge the distances between different classes. An iterative method is proposed to simultaneously learn the regression coefficients, -draggings matrix and predicting the unknown class labels. Experimental results show the superiority of DSSFS.

IJCAI Conference 2015 Conference Paper

Sparse Probabilistic Matrix Factorization by Laplace Distribution for Collaborative Filtering

• Liping Jing
• Peng Wang
• Liu Yang

In recommendation systems, probabilistic matrix factorization (PMF) is a state-of-the-art collaborative filtering method by determining the latent features to represent users and items. However, two major issues limiting the usefulness of PMF are the sparsity problem and long-tail distribution. Sparsity refers to the situation that the observed rating data are sparse, which results in that only part of latent features are informative for describing each item/user. Long tail distribution implies that a large fraction of items have few ratings. In this work, we propose a sparse probabilistic matrix factorization method (SPMF) by utilizing a Laplacian distribution to model the item/user factor vector. Laplacian distribution has ability to generate sparse coding, which is beneficial for SPMF to distinguish the relevant and irrelevant latent features with respect to each item/user. Meanwhile, the tails in Laplacian distribution are comparatively heavy, which is rewarding for SPMF to recommend the tail items. Furthermore, a distributed Gibbs sampling algorithm is developed to efficiently train the proposed sparse probabilistic model. A series of experiments on Netflix and Movielens datasets have been conducted to demonstrate that SPMF outperforms the existing PMF and its extended version Bayesian PMF (BPMF), especially for the recommendation of tail items.