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Yuan Qi

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31 papers
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31

AAAI Conference 2026 Conference Paper

Constraints-Guided Diffusion Reasoner for Neuro-Symbolic Learning

  • Xuan Zhang
  • Zhijian Zhou
  • Weidi Xu
  • Yanting Miao
  • Chao Qu
  • Yuan Qi

Enabling neural networks to learn complex logical constraints and fulfill symbolic reasoning is a critical challenge. Bridging this gap often requires guiding the neural network’s output distribution to move closer to the symbolic constraints. While diffusion models have shown remarkable generative capability across various domains, we employ the powerful architecture to perform neuro-symbolic learning and solve logical puzzles. Our diffusion-based pipeline adopts a two-stage training strategy: the first stage focuses on cultivating basic reasoning abilities, while the second emphasizes systematic learning of logical constraints. To impose hard constraints on neural outputs in the second stage, we formulate the diffusion reasoner as a Markov decision process and innovatively fine-tune it with an improved proximal policy optimization algorithm. We utilize a rule-based reward signal derived from the logical consistency of neural outputs and adopt a flexible strategy to optimize the diffusion reasoner's policy. We evaluate our methodology on some classical symbolic reasoning benchmarks, including Sudoku, Maze, pathfinding and preference learning. Experimental results demonstrate that our approach achieves outstanding accuracy and logical consistency among neural networks.

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AAAI Conference 2026 Conference Paper

PET2Rep: Towards Vision-Language Model-Drived Automated Radiology Report Generation for Positron Emission Tomography

  • Yichi Zhang
  • Wenbo Zhang
  • Zehui Ling
  • Gang Feng
  • Sisi Peng
  • Deshu Chen
  • Yuchen Liu
  • Hongwei Zhang

Positron emission tomography (PET) is a cornerstone of modern oncologic and neurologic imaging, distinguished by its unique ability to illuminate dynamic metabolic processes that transcend the anatomical focus of traditional imaging technologies. Radiology reports are essential for clinical decision making, yet their manual creation is labor-intensive and time-consuming. Recent advancements of vision-language models (VLMs) have shown strong potential in medical applications, presenting a promising avenue for automating report generation. However, existing applications of VLMs in the medical domain have predominantly focused on structural imaging modalities, while the unique characteristics of molecular PET imaging have largely been overlooked. To bridge the gap, we introduce PET2Rep, a large-scale comprehensive benchmark for evaluation of general and medical VLMs for radiology report generation for PET images. PET2Rep stands out as the first dedicated dataset for PET report generation with metabolic information, uniquely capturing whole-body image-report pairs that cover dozens of organs to fill the critical gap in existing benchmarks and mirror real-world clinical comprehensiveness. In addition to widely recognized natural language generation metrics, we introduce a series of clinical efficiency metrics to evaluate the quality of radiotracer uptake pattern description in key organs in generated reports. We conduct a head-to-head comparison of 30 cutting-edge general-purpose and medical-specialized VLMs. The results show that the current state-of-the-art VLMs perform poorly on PET report generation task, falling considerably short of fulfilling practical needs. Moreover, we identify several key insufficiency that need to be addressed to advance the development in medical applications. We believe PET2Rep will serve as a platform for the development and application of VLMs for PET imaging, accelerating the development of trustworthy reporting tools that can genuinely alleviate radiologist burden and enhance patient care.

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AAAI Conference 2026 Conference Paper

Structure-based RNA Design by Step-wise Optimization of Latent Diffusion Model

  • Qi Si
  • Xuyang Liu
  • Penglei Wang
  • Xin Guo
  • Yuan Qi
  • Yuan Cheng

RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.

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AAAI Conference 2025 Conference Paper

4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidance

  • Kaihui Cheng
  • Ce Liu
  • Qingkun Su
  • Jun Wang
  • Liwei Zhang
  • Yining Tang
  • Yao Yao
  • Siyu Zhu

Protein structure prediction is pivotal for understanding the structure-function relationship of proteins, advancing biological research, and facilitating pharmaceutical development and experimental design. While deep learning methods and the expanded availability of experimental 3D protein structures have accelerated structure prediction, the dynamic nature of protein structures has received limited attention. This study introduces an innovative 4D diffusion model incorporating molecular dynamics (MD) simulation data to learn dynamic protein structures. Our approach is distinguished by the following components: (1) a unified diffusion model capable of generating dynamic protein structures, including both the backbone and side chains, utilizing atomic grouping and side-chain dihedral angle predictions; (2) a reference network that enhances structural consistency by integrating the latent embeddings of the initial 3D protein structures; and (3) a motion alignment module aimed at improving temporal structural coherence across multiple time steps. To our knowledge, this is the first diffusion-based model aimed at predicting protein trajectories across multiple time steps simultaneously. Validation on benchmark datasets demonstrates that our model exhibits high accuracy in predicting dynamic 3D structures of proteins containing up to 256 amino acids over 32 time steps, effectively capturing both local flexibility in stable states and significant conformational changes.

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NeurIPS Conference 2025 Conference Paper

ChromFound: Towards A Universal Foundation Model for Single-Cell Chromatin Accessibiltiy Data

  • Yifeng Jiao
  • Yuchen Liu
  • Yu Zhang
  • Xin Guo
  • Yushuai Wu
  • Chen Jiang
  • Jiyang Li
  • Hongwei Zhang

The advent of single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) offers an innovative perspective for deciphering regulatory mechanisms by assembling a vast repository of single-cell chromatin accessibility data. While foundation models have achieved significant success in single-cell transcriptomics, there is currently no foundation model for scATAC-seq that supports zero-shot high-quality cell identification and comprehensive multi-omics analysis simultaneously. Key challenges lie in the high dimensionality and sparsity of scATAC-seq data, as well as the lack of a standardized schema for representing open chromatin regions (OCRs). Here, we present ChromFound, a foundation model tailored for scATAC-seq. ChromFound utilizes a hybrid architecture and genome-aware tokenization to effectively capture genome-wide long contexts and regulatory signals from dynamic chromatin landscapes. Pretrained on 1. 97 million cells from 30 tissues and 6 disease conditions, ChromFound demonstrates broad applicability across 6 diverse tasks. Notably, it achieves robust zero-shot performance in generating universal cell representations and exhibits excellent transferability in cell type annotation and cross-omics prediction. By uncovering enhancer-gene links undetected by existing computational methods, ChromFound offers a promising framework for understanding disease risk variants in the noncoding genome. The implementation of ChromFound is available via https: //github. com/JohnsonKlose/ChromFound.

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IJCAI Conference 2025 Conference Paper

Tensor Network: from the Perspective of AI4Science and Science4AI

  • Junchi Yan
  • Yehui Tang
  • Xinyu Ye
  • Hao Xiong
  • Xiaoqiu Zhong
  • Yuhan Wang
  • Yuan Qi

Tensor network has been a promising numerical tool for computational problems across science and AI. For their emerging and fast development especially in the intersection between AI and science, this paper tries to present a compact review, regarding both their applications and its own recent technical development including open-source tools. Specifically, we make the observations that tensor network plays a functional role in matrix compression and representation, information fusion, as well as quantum-inspired algorithms, which can be generally regarded as Science4AI in our survey. On the other hand, there is an emerging line of research in tensor network in AI4Science especially like learning quantum many-body physics by using e. g. neural network quantum state. Importantly, we unify tensorization methodologies across classical and modern architectures, and particularly show how tensorization bridges low-order parameter spaces to high-dimensional representations without exponential parameter growth, and further point out their potential use in scientific computing. We conclude the paper with outlook for future trends.

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AAAI Conference 2021 Conference Paper

Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection

  • Dongbo Xi
  • Bowen Song
  • Fuzhen Zhuang
  • Yongchun Zhu
  • Shuai Chen
  • Tianyi Zhang
  • Yuan Qi
  • Qing He

With the explosive growth of e-payment industry, online transaction fraud has become one of the biggest challenges for the business. The historical behavior information of users provides rich information for digging into the users’ fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit user’s behavioral information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields within one event have proven to be strong indicators of fraudulent behaviors. In this paper, we propose the Dual Importanceaware Factorization Machines (DIFM), which exploits the inter- and intra-event information among users’ behavior sequence from dual perspectives, i. e. , field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in Alipay’s risk management system, which provides real-time fraud detection service for ecommerce platforms. Experimental results on industrial data under various scenarios in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.

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NeurIPS Conference 2020 Conference Paper

Bandit Samplers for Training Graph Neural Networks

  • Ziqi Liu
  • Zhengwei Wu
  • Zhiqiang Zhang
  • Jun Zhou
  • Shuang Yang
  • Le Song
  • Yuan Qi

Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT). The fundamental reason is that the embeddings of the neighbors or learned weights involved in the optimal sampling distribution are \emph{changing} during the training and \emph{not known a priori}, but only \emph{partially observed} when sampled, thus making the derivation of an optimal variance reduced samplers non-trivial. In this paper, we formulate the optimization of the sampling variance as an adversary bandit problem, where the rewards are related to the node embeddings and learned weights, and can vary constantly. Thus a good sampler needs to acquire variance information about more neighbors (exploration) while at the same time optimizing the immediate sampling variance (exploit). We theoretically show that our algorithm asymptotically approaches the optimal variance within a factor of 3. We show the efficiency and effectiveness of our approach on multiple datasets.

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AAAI Conference 2020 Conference Paper

Cost-Effective Incentive Allocation via Structured Counterfactual Inference

  • Romain Lopez
  • Chenchen Li
  • Xiang Yan
  • Junwu Xiong
  • Michael Jordan
  • Yuan Qi
  • Le Song

We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback. In contrast to traditional policy optimization frameworks, we take into account the additional reward structure and budget constraints common in this setting, and develop a new two-step method for solving this constrained counterfactual policy optimization problem. Our method first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints. We also establish theoretical error bounds for our estimation procedure and we empirically show that the approach leads to significant improvement on both synthetic and real datasets.

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IJCAI Conference 2020 Conference Paper

Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining

  • Shuo Yang
  • Zhiqiang Zhang
  • Jun Zhou
  • Yang Wang
  • Wang Sun
  • Xingyu Zhong
  • Yanming Fang
  • Quan Yu

Small and Medium-sized Enterprises (SMEs) are playing a vital role in the modern economy. Recent years, financial risk analysis for SMEs attracts lots of attentions from financial institutions. However, the financial risk analysis for SMEs usually suffers data deficiency problem, especially for the mobile financial institutions which seldom collect credit-related data directly from SMEs. Fortunately, although credit-related information of SMEs is hard to be acquired sufficiently, the interactive relationships between SMEs, which may contain valuable information of financial risk, is usually available for the mobile financial institutions. Finding out credit-related relationship of SME from massive interactions helps comprehensively model the SMEs thus improve the performance of financial risk analysis. In this paper, tackling the data deficiency problem of financial risk analysis for SMEs, we propose an innovative financial risk analysis framework with graph-based supply chain mining. Specifically, to capture the credit-related topology structural and temporal variation information of SMEs, we design and employ a novel spatial-temporal aware graph neural network, to mine supply chain relationship on a SME graph, and then analysis the credit risk based on the mined supply chain graph. Experimental results on real-world financial datasets prove the effectiveness of our proposal for financial risk analysis for SMEs.

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AAAI Conference 2020 Conference Paper

Long Short-Term Sample Distillation

  • Liang Jiang
  • Zujie Wen
  • Zhongping Liang
  • Yafang Wang
  • Gerard de Melo
  • Zhe Li
  • Liangzhuang Ma
  • Jiaxing Zhang

In the past decade, there has been substantial progress at training increasingly deep neural networks. Recent advances within the teacher–student training paradigm have established that information about past training updates show promise as a source of guidance during subsequent training steps. Based on this notion, in this paper, we propose Long Short- Term Sample Distillation, a novel training policy that simultaneously leverages multiple phases of the previous training process to guide the later training updates to a neural network, while efficiently proceeding in just one single generation pass. With Long Short-Term Sample Distillation, the supervision signal for each sample is decomposed into two parts: a long-term signal and a short-term one. The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher– student differences, while the short-term one yields more upto-date cues with the goal of enabling higher-quality updates. Moreover, the teachers for each sample are unique, such that, overall, the model learns from a very diverse set of teachers. Comprehensive experimental results across a range of vision and NLP tasks demonstrate the effectiveness of this new training method.

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TIST Journal 2020 Journal Article

Practical Privacy Preserving POI Recommendation

  • Chaochao Chen
  • Jun Zhou
  • Bingzhe Wu
  • Wenjing Fang
  • Li Wang
  • Yuan Qi
  • Xiaolin Zheng

Point-of-Interest (POI) recommendation has been extensively studied and successfully applied in industry recently. However, most existing approaches build centralized models on the basis of collecting users’ data. Both private data and models are held by the recommender, which causes serious privacy concerns. In this article, we propose a novel Privacy preserving POI Recommendation (PriRec) framework. First, to protect data privacy, users’ private data (features and actions) are kept on their own side, e.g., Cellphone or Pad. Meanwhile, the public data that need to be accessed by all the users are kept by the recommender to reduce the storage costs of users’ devices. Those public data include: (1) static data only related to the status of POI, such as POI categories, and (2) dynamic data dependent on user-POI actions such as visited counts. The dynamic data could be sensitive, and we develop local differential privacy techniques to release such data to the public with privacy guarantees. Second, PriRec follows the representations of Factorization Machine (FM) that consists of a linear model and the feature interaction model. To protect the model privacy, the linear models are saved on the users’ side, and we propose a secure decentralized gradient descent protocol for users to learn it collaboratively. The feature interaction model is kept by the recommender since there is no privacy risk, and we adopt a secure aggregation strategy in a federated learning paradigm to learn it. To this end, PriRec keeps users’ private raw data and models in users’ own hands, and protects user privacy to a large extent. We apply PriRec in real-world datasets, and comprehensive experiments demonstrate that, compared with FM, PriRec achieves comparable or even better recommendation accuracy.

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AAAI Conference 2019 Conference Paper

Cash-Out User Detection Based on Attributed Heterogeneous Information Network with a Hierarchical Attention Mechanism

  • Binbin Hu
  • Zhiqiang Zhang
  • Chuan Shi
  • Jun Zhou
  • Xiaolong Li
  • Yuan Qi

As one of the major frauds in financial services, cash-out fraud is that users pursue cash gains with illegal or insincere means. Conventional solutions for the cash-out user detection are to perform subtle feature engineering for each user and then apply a classifier, such as GDBT and Neural Network. However, users in financial services have rich interaction relations, which are seldom fully exploited by conventional solutions. In this paper, with the real datasets in Ant Credit Pay of Ant Financial Services Group, we first study the cashout user detection problem and propose a novel hierarchical attention mechanism based cash-out user detection model, called HACUD. Specifically, we model different types of objects and their rich attributes and interaction relations in the scenario of credit payment service with an Attributed Heterogeneous Information Network (AHIN). The HACUD model enhances feature representation of objects through meta-path based neighbors exploiting different aspects of structure information in AHIN. Furthermore, a hierarchical attention mechanism is elaborately designed to model user’s preferences towards attributes and meta-paths. Experimental results on two real datasets show that the HACUD outperforms the state-of-the-art methods.

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AAAI Conference 2019 Conference Paper

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

  • Ziqi Liu
  • Chaochao Chen
  • Longfei Li
  • Jun Zhou
  • Xiaolong Li
  • Le Song
  • Yuan Qi

In this paper, we propose a new online feature selection algorithm for streaming data. We aim to focus on the following two problems which remain unaddressed in literature. First, most existing online feature selection algorithms merely utilize the first-order information of the data streams, regardless of the fact that second-order information explores the correlations between features and significantly improves the performance. Second, most online feature selection algorithms are based on the balanced data presumption, which is not true in many real-world applications. For example, in fraud detection, the number of positive examples are much less than negative examples because most cases are not fraud. The balanced assumption will make the selected features biased towards the majority class and fail to detect the fraud cases. We propose an Adaptive Sparse Confidence-Weighted (ASCW) algorithm to solve the aforementioned two problems. We first introduce an `0-norm constraint into the second-order confidence-weighted (CW) learning for feature selection. Then the original loss is substituted with a cost-sensitive loss function to address the imbalanced data issue. Furthermore, our algorithm maintains multiple sparse CW learner with the corresponding cost vector to dynamically select an optimal cost. We theoretically enhance the theory of sparse CW learning and analyze the performance behavior in F-measure. Empirical studies show the superior performance over the stateof-the-art online learning methods in the online-batch setting.

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AAAI Conference 2019 Conference Paper

Latent Dirichlet Allocation for Internet Price War

  • Chenchen Li
  • Xiang Yan
  • Xiaotie Deng
  • Yuan Qi
  • Wei Chu
  • Le Song
  • Junlong Qiao
  • Jianshan He

Current Internet market makers are facing an intense competitive environment, where personalized price reductions or discounted coupons are provided by their peers to attract more customers. Much investment is spent to catch up with each other’s competitors but participants in such a price cut war are often incapable of winning due to their lack of information about others’ strategies or customers’ preference. We formalize the problem as a stochastic game with imperfect and incomplete information and develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents preferences of customers and strategies of competitors. Tests on simulated experiments and an open dataset for real data show that, by subsuming all available market information of the market maker’s competitors, our model exhibits a significant improvement for understanding the market environment and finding the best response strategies in the Internet price war. Our work marks the first successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.

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NeurIPS Conference 2019 Conference Paper

Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning

  • Chao Qu
  • Shie Mannor
  • Huan Xu
  • Yuan Qi
  • Le Song
  • Junwu Xiong

We consider the networked multi-agent reinforcement learning (MARL) problem in a fully decentralized setting, where agents learn to coordinate to achieve joint success. This problem is widely encountered in many areas including traffic control, distributed control, and smart grids. We assume each agent is located at a node of a communication network and can exchange information only with its neighbors. Using softmax temporal consistency, we derive a primal-dual decentralized optimization method and obtain a principled and data-efficient iterative algorithm named {\em value propagation}. We prove a non-asymptotic convergence rate of $\mathcal{O}(1/T)$ with nonlinear function approximation. To the best of our knowledge, it is the first MARL algorithm with a convergence guarantee in the control, off-policy, non-linear function approximation, fully decentralized setting.

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JAIR Journal 2016 Journal Article

Association Discovery and Diagnosis of Alzheimer’s Disease with Bayesian Multiview Learning

  • Zenglin Xu
  • Shandian Zhe
  • Yuan Qi
  • Peng Yu

The analysis and diagnosis of Alzheimer’s disease (AD) can be based on genetic variations, e.g., single nucleotide polymorphisms (SNPs) and phenotypic traits, e.g., Magnetic Resonance Imaging (MRI) features. We consider two important and related tasks: i) to select genetic and phenotypical markers for AD diagnosis and ii) to identify associations between genetic and phenotypical data. While previous studies treat these two tasks separately, they are tightly coupled because underlying associations between genetic variations and phenotypical features contain the biological basis for a disease. Here we present a new sparse Bayesian approach for joint association study and disease diagnosis. In this approach, common latent features are extracted from different data sources based on sparse projection matrices and used to predict multiple disease severity levels; in return, the disease status can guide the discovery of relationships between data sources. The sparse projection matrices not only reveal interactions between data sources but also select groups of biomarkers related to the disease. Moreover, to take advantage of the linkage disequilibrium (LD) measuring the non-random association of alleles, we incorporate a graph Laplacian type of prior in the model. To learn the model from data, we develop an efficient variational inference algorithm. Analysis on an imaging genetics dataset for the study of Alzheimer’s Disease (AD) indicates that our model identifies biologically meaningful associations between genetic variations and MRI features, and achieves significantly higher accuracy for predicting ordinal AD stages than the competing methods.

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AAAI Conference 2016 Conference Paper

DinTucker: Scaling Up Gaussian Process Models on Large Multidimensional Arrays

  • Shandian Zhe
  • Yuan Qi
  • Youngja Park
  • Zenglin Xu
  • Ian Molloy
  • Suresh Chari

Tensor decomposition methods are effective tools for modelling multidimensional array data (i. e. , tensors). Among them, nonparametric Bayesian models, such as Infinite Tucker Decomposition (InfTucker), are more powerful than multilinear factorization approaches, including Tucker and PARAFAC, and usually achieve better predictive performance. However, they are difficult to handle massive data due to a prohibitively high training cost. To address this limitation, we propose Distributed infinite Tucker (DINTUCKER), a new hierarchical Bayesian model that enables local learning of InfTucker on subarrays and global information integration from local results. We further develop a distributed stochastic gradient descent algorithm, coupled with variational inference for model estimation. In addition, the connection between DINTUCKER and InfTucker is revealed in terms of model evidence. Experiments demonstrate that DINTUCKER maintains the predictive accuracy of InfTucker and is scalable on massive data: On multidimensional arrays with billions of elements from two real-world applications, DINTUCKER achieves significantly higher prediction accuracy with less training time, compared with the state-of-the-art large-scale tensor decomposition method, GigaTensor.

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NeurIPS Conference 2016 Conference Paper

Distributed Flexible Nonlinear Tensor Factorization

  • Shandian Zhe
  • Kai Zhang
  • Pengyuan Wang
  • Kuang-chih Lee
  • Zenglin Xu
  • Yuan Qi
  • Zoubin Ghahramani

Tensor factorization is a powerful tool to analyse multi-way data. Recently proposed nonlinear factorization methods, although capable of capturing complex relationships, are computationally quite expensive and may suffer a severe learning bias in case of extreme data sparsity. Therefore, we propose a distributed, flexible nonlinear tensor factorization model, which avoids the expensive computations and structural restrictions of the Kronecker-product in the existing TGP formulations, allowing an arbitrary subset of tensor entries to be selected for training. Meanwhile, we derive a tractable and tight variational evidence lower bound (ELBO) that enables highly decoupled, parallel computations and high-quality inference. Based on the new bound, we develop a distributed, key-value-free inference algorithm in the MapReduce framework, which can fully exploit the memory cache mechanism in fast MapReduce systems such as Spark. Experiments demonstrate the advantages of our method over several state-of-the-art approaches, in terms of both predictive performance and computational efficiency.

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IJCAI Conference 2016 Conference Paper

Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models

  • Syed Abbas Z. Naqvi
  • Shandian Zhe
  • Yuan Qi
  • Yifan Yang
  • Jieping Ye

We consider the application of Bayesian spike-and-slab models in high-dimensional feature selection problems. To do so, we propose a simple yet effective fast approximate Bayesian inference algorithm based on Laplace's method. We exploit two efficient optimization methods, GIST and L-BFGS, to obtain the mode of the posterior distribution. Then we propose an ensemble Nystrom based approach to calculate the diagonal of the inverse Hessian over the mode to obtain the approximate posterior marginals in O(knp) time, k ≪ p. Furthermore, we provide the theoretical analysis about the estimation consistency and approximation error bounds. With the posterior marginals of the model weights, we use quadrature integration to estimate the marginal posteriors of selection probabilities and indicator variables for all features, which quantify the selection uncertainty. Our method not only maintains the benefits of the Bayesian treatment (e. g. , uncertainty quantification) but also possesses the computational efficiency, and oracle properties of the frequentist methods. Simulation shows that our method estimates better or comparable selection probabilities and indicator variables than alternative approximate inference methods such as VB and EP, but with less running time. Extensive experiments on large real datasets demonstrate that our method often improves prediction accuracy over Bayesian automatic relevance determination, EP, and frequentist L1 type methods.

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AAAI Conference 2015 Conference Paper

Bayesian Maximum Margin Principal Component Analysis

  • Changying Du
  • Shandian Zhe
  • Fuzhen Zhuang
  • Yuan Qi
  • Qing He
  • Zhongzhi Shi

Supervised dimensionality reduction has shown great advantages in finding predictive subspaces. Previous methods rarely consider the popular maximum margin principle and are prone to overfitting to usually small training data, especially for those under the maximum likelihood framework. In this paper, we present a posterior-regularized Bayesian approach to combine Principal Component Analysis (PCA) with the maxmargin learning. Based on the data augmentation idea for max-margin learning and the probabilistic interpretation of PCA, our method can automatically infer the weight and penalty parameter of max-margin learning machine, while finding the most appropriate PCA subspace simultaneously under the Bayesian framework. We develop a fast mean-field variational inference algorithm to approximate the posterior. Experimental results on various classification tasks show that our method outperforms a number of competitors.

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IJCAI Conference 2015 Conference Paper

EigenGP: Gaussian Process Models with Adaptive Eigenfunctions

  • Hao Peng
  • Yuan Qi

Gaussian processes (GPs) provide a nonparametric representation of functions. However, classical GP inference suffers from high computational cost for big data. In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary elements—eigenfunctions of a GP prior—and prior precisions in a sparse finite model. It is well known that, among all orthogonal basis functions, eigenfunctions can provide the most compact representation. Unlike other sparse Bayesian finite models where the basis function has a fixed form, our eigenfunctions live in a reproducing kernel Hilbert space as a finite linear combination of kernel functions. We learn the dictionary elements— eigenfunctions—and the prior precisions over these elements as well as all the other hyperparameters from data by maximizing the model marginal likelihood. We explore computational linear algebra to simplify the gradient computation significantly. Our experimental results demonstrate improved predictive performance of EigenGP over alternative sparse GP methods as well as relevance vector machines.

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AAAI Conference 2015 Conference Paper

Sparse Bayesian Multiview Learning for Simultaneous Association Discovery and Diagnosis of Alzheimer’s Disease

  • Shandian Zhe
  • Zenglin Xu
  • Yuan Qi
  • Peng Yu

In the analysis and diagnosis of many diseases, such as the Alzheimer’s disease (AD), two important and related tasks are usually required: i) selecting genetic and phenotypical markers for diagnosis, and ii) identifying associations between genetic and phenotypical features. While previous studies treat these two tasks separately, they are tightly coupled due to the same underlying biological basis. To harness their potential benefits for each other, we propose a new sparse Bayesian approach to jointly carry out the two important and related tasks. In our approach, we extract common latent features from different data sources by sparse projection matrices and then use the latent features to predict disease severity levels; in return, the disease status can guide the learning of sparse projection matrices, which not only reveal interactions between data sources but also select groups of related biomarkers. In order to boost the learning of sparse projection matrices, we further incorporate graph Laplacian priors encoding the valuable linkage disequilibrium (LD) information. To efficiently estimate the model, we develop a variational inference algorithm. Analysis on an imaging genetics dataset for AD study shows that our model discovers biologically meaningful associations between single nucleotide polymorphisms (SNPs) and magnetic resonance imaging (MRI) features, and achieves significantly higher accuracy for predicting ordinal AD stages than competitive methods.

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ICML Conference 2013 Conference Paper

Message passing with l1 penalized KL minimization

  • Yuan Qi
  • Yandong Guo

Bayesian inference is often hampered by large computational expense. As a generalization of belief propagation (BP), expectation propagation (EP) approximates exact Bayesian computation with efficient message passing updates. However, when an approximation family used by EP is far from exact posterior distributions, message passing may lead to poor approximation quality and suffer from divergence. To address this issue, we propose an approximate inference method, relaxed expectation propagation(REP), based on a new divergence with a l1 penalty. Minimizing this penalized divergence adaptively relaxes EP’s moment matching requirement for message passing. We apply REP to Gaussian process classification and experimental results demonstrate significant improvement of REP over EP and alpha-divergence based power EP – in terms of algorithmic stability, estimation accuracy, and predictive performance. Furthermore, we develop relaxed belief propagation(RBP), a special case of REP, to conduct inference on discrete Markov random fields (MRFs). Our results show improved estimation accuracy of RBP over BP and fractional BP when interactions between MRF nodes are strong.

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NeurIPS Conference 2011 Conference Paper

EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning

  • Feng Yan
  • Yuan Qi

For many real-world applications, we often need to select correlated variables---such as genetic variations and imaging features associated with Alzheimer's disease---in a high dimensional space. The correlation between variables presents a challenge to classical variable selection methods. To address this challenge, the elastic net has been developed and successfully applied to many applications. Despite its great success, the elastic net does not exploit the correlation information embedded in the data to select correlated variables. To overcome this limitation, we present a novel hybrid model, EigenNet, that uses the eigenstructures of data to guide variable selection. Specifically, it integrates a sparse conditional classification model with a generative model capturing variable correlations in a principled Bayesian framework. We develop an efficient active-set algorithm to estimate the model via evidence maximization. Experiments on synthetic data and imaging genetics data demonstrated the superior predictive performance of the EigenNet over the lasso, the elastic net, and the automatic relevance determination.

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AAAI Conference 2011 Conference Paper

Sparse Matrix-Variate t Process Blockmodels

  • Zenglin Xu
  • Feng Yan
  • Yuan Qi

We consider the problem of modeling network interactions and identifying latent groups of network nodes. This problem is challenging due to the facts i) that the network nodes are interdependent instead of independent, ii) that the network data are very noisy (e. g. , missing edges), and iii) that the network interactions are often sparse. To address these challenges, we propose a Sparse Matrix-variate t process Blockmodel (SMTB). In particular, we generalize a matrix-variate t distribution to a t process on matrices with nonlinear covariance functions. Due to this generalization, our model can estimate latent memberships for individual network nodes. This separates our model from previous t distribution based relational models. Also, we introduce sparse prior distributions on the latent membership parameters to select group assignments for individual nodes. To learn the model efficiently from data, we develop a variational method. When compared with several state-of-the-art models, including the predictive matrixvariate t models and mixed membership stochastic blockmodels, our model achieved improved prediction accuracy on real world network datasets.

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NeurIPS Conference 2011 Conference Paper

t-divergence Based Approximate Inference

  • Nan Ding
  • Yuan Qi
  • S. V. N. Vishwanathan

Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions. We extend the idea of approximate inference to the t-exponential family by defining a new t-divergence. This divergence measure is obtained via convex duality between the log-partition function of the t-exponential family and a new t-entropy. We illustrate our approach on the Bayes Point Machine with a Student's t-prior.

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NeurIPS Conference 2009 Conference Paper

Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units

  • Feng Yan
  • Ningyi Xu
  • Yuan Qi

The recent emergence of Graphics Processing Units (GPUs) as general-purpose parallel computing devices provides us with new opportunities to develop scalable learning methods for massive data. In this work, we consider the problem of parallelizing two inference methods on GPUs for latent Dirichlet Allocation (LDA) models, collapsed Gibbs sampling (CGS) and collapsed variational Bayesian (CVB). To address limited memory constraints on GPUs, we propose a novel data partitioning scheme that effectively reduces the memory cost. Furthermore, the partitioning scheme balances the computational cost on each multiprocessor and enables us to easily avoid memory access conflicts. We also use data streaming to handle extremely large datasets. Extensive experiments showed that our parallel inference methods consistently produced LDA models with the same predictive power as sequential training methods did but with 26x speedup for CGS and 196x speedup for CVB on a GPU with 30 multiprocessors; actually the speedup is almost linearly scalable with the number of multiprocessors available. The proposed partitioning scheme and data streaming can be easily ported to many other models in machine learning.

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NeurIPS Conference 2006 Conference Paper

Parameter Expanded Variational Bayesian Methods

  • Tommi Jaakkola
  • Yuan Qi

Bayesian inference has become increasingly important in statistical machine learning. Exact Bayesian calculations are often not feasible in practice, however. A number of approximate Bayesian methods have been proposed to make such calculations practical, among them the variational Bayesian (VB) approach. The VB approach, while useful, can nevertheless suffer from slow convergence to the approximate solution. To address this problem, we propose Parameter-eXpanded Variational Bayesian (PX-VB) methods to speed up VB. The new algorithm is inspired by parameter-expanded expectation maximization (PX-EM) and parameterexpanded data augmentation (PX-DA). Similar to PX-EM and -DA, PX-VB expands a model with auxiliary variables to reduce the coupling between variables in the original model. We analyze the convergence rates of VB and PX-VB and demonstrate the superior convergence rates of PX-VB in variational probit regression and automatic relevance determination.

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NeurIPS Conference 2005 Conference Paper

Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification

  • Ashish Kapoor
  • Hyungil Ahn
  • Yuan Qi
  • Rosalind Picard

There have been many graph-based approaches for semi-supervised clas- sification. One problem is that of hyperparameter learning: performance depends greatly on the hyperparameters of the similarity graph, trans- formation of the graph Laplacian and the noise model. We present a Bayesian framework for learning hyperparameters for graph-based semi- supervised classification. Given some labeled data, which can contain inaccurate labels, we pose the semi-supervised classification as an in- ference problem over the unknown labels. Expectation Propagation is used for approximate inference and the mean of the posterior is used for classification. The hyperparameters are learned using EM for evidence maximization. We also show that the posterior mean can be written in terms of the kernel matrix, providing a Bayesian classifier to classify new points. Tests on synthetic and real datasets show cases where there are significant improvements in performance over the existing approaches.

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NeurIPS Conference 2003 Conference Paper

Tree-structured Approximations by Expectation Propagation

  • Yuan Qi
  • Tom Minka

Approximation structure plays an important role in inference on loopy graphs. As a tractable structure, tree approximations have been utilized in the variational method of Ghahramani & Jordan (1997) and the se- quential projection method of Frey et al. (2000). However, belief propa- gation represents each factor of the graph with a product of single-node messages. In this paper, belief propagation is extended to represent fac- tors with tree approximations, by way of the expectation propagation framework. That is, each factor sends a “message” to all pairs of nodes in a tree structure. The result is more accurate inferences and more fre- quent convergence than ordinary belief propagation, at a lower cost than variational trees or double-loop algorithms.

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