Lei Yu

AAAI Conference 2026 Conference Paper

Binary Message Passing for Generalizable Semi-Supervised Graph Anomaly Detection

• Jingyuan Zhang
• Xin Wang
• Lei Yu
• Li Yang
• Fengjun Zhang

Graph Neural Networks (GNNs) have achieved impressive performance in semi-supervised graph anomaly detection (GAD). While many GNN variants have been developed for this task, they largely focus on advanced message aggregation schemes, leaving the message routing aspect underexplored. We argue that the commonly used broadcast-based routing can also hinder generalization, particularly in the presence of rare and structurally challenging (vertices with a high-degree) anomalies. To address this, we propose Binary Message Passing (BMP), a novel routing paradigm that models the message flow of each vertex as a binary tree (BMP tree), where vanilla graph convolution is decoupled by its left and right subtrees. Each vertex recursively gathers information from neighbors with higher anomaly probabilities within each subtree, thereby amplifying the propagation of anomaly information across the topology. The anomaly probabilities are estimated and updated by the model itself, enabling adaptive, self-supervised routing over iterations. Furthermore, combining multiple BMP trees into a BMP forest provides multi-scale structural context, enhancing the expressiveness of final vertex embeddings. Extensive experiments show that BMP improves detection performance under limited supervision while exhibiting better generalization across structurally diverse anomalies.

AAAI Conference 2026 Conference Paper

Event-Guided Super-Resolving Blurry Image via Asymmetric Integral Driven Consistency

• Chi Zhang
• Xiang Zhang
• Lei Yu
• Gui-Song Xia
• Yuming Fang
• Wenhan Yang

Super-Resolution from a Blurry low-resolution image (SRB) constitutes a severely ill-posed inverse problem. Current learning-based SRB approaches primarily rely on synthetic, well-labeled paired datasets to regularize solution spaces, yet they exhibit limited generalizability in practical applications due to significant domain discrepancies between simulated degradations and real-world imaging conditions. To bridge this synthetic-to-real gap, we propose a novel Self-supervised Event-based SRB (SE-SRB) framework that leverages neuromorphic event streams as physical priors and adopts a lightweight neural architecture tailored for effective domain adaptation. Specifically, the proposed SE-SRB introduces a self-supervised learning paradigm based on asymmetric integral driven consistency, which enforces temporal coherence between predictions derived from RGB and asynchronous event streams at different time points. Extensive experiments validate that SE-SRB consistently outperforms state-of-the-art methods on both synthetic and real-world datasets. Built upon a lightweight parallel two-stream architecture, SE-SRB achieves high computational efficiency, featuring reduced parameter count, lower FLOPs, and real-time inference capability (40 FPS).

AAAI Conference 2026 Conference Paper

GenePheno: Interpretable Gene Knockout-Induced Phenotype Abnormality Prediction from Gene Sequences

• Jingquan Yan
• Yuwei Miao
• Lei Yu
• Yuzhi Guo
• Xue Xiao
• Lin Xu
• Junzhou Huang

Exploring how genetic sequences shape phenotypes is a fundamental challenge in biology and a key step toward scalable, hypothesis-driven experimentation. The task is complicated by the large modality gap between sequences and phenotypes, as well as the pleiotropic nature of gene–phenotype relationships. Existing sequence-based efforts focus on the degree to which variants of specific genes alter a limited set of phenotypes, while general gene knockout-induced phenotype abnormality prediction methods heavily rely on curated genetic information as inputs, which limits scalability and generalizability. As a result, the task of broadly predicting the presence of multiple phenotype abnormalities under gene knockout directly from gene sequences remains underexplored. We introduce GenePheno, the first interpretable multi-label prediction framework that predicts knockout-induced phenotypic abnormalities from gene sequences. GenePheno employs a contrastive multi-label learning objective that captures inter-phenotype correlations, complemented by an exclusive regularization that enforces biological consistency. It further incorporates a gene function bottleneck layer, offering human-interpretable concepts that reflect functional mechanisms behind phenotype formation. To support progress in this area, we curate four datasets with canonical gene sequences as input and multi-label phenotypic abnormalities induced by gene knockouts as targets. Across these datasets, GenePheno achieves state-of-the-art gene-centric Fmax and phenotype-centric AUC, and case studies demonstrate its ability to reveal gene functional mechanisms.

AAAI Conference 2026 Conference Paper

PMGS: Reconstruction of Projectile Motion Across Large Spatiotemporal Spans via 3D Gaussian Splatting

• Yijun Xu
• Jingrui Zhang
• Yuhan Chen
• Dingwen Wang
• Lei Yu
• Chu He

Modeling complex rigid motion across large spatiotemporal spans remains an unresolved challenge in dynamic reconstruction. Existing paradigms are mainly confined to short-term, small-scale deformation and offer limited consideration for physical consistency. This study proposes PMGS, focusing on reconstructing Projectile Motion via 3D Gaussian Splatting. The workflow comprises two stages: 1) Target Modeling: achieving object-centralized reconstruction through dynamic scene decomposition and an improved point density control; 2) Motion Recovery: restoring full motion sequences by learning per-frame SE(3) poses. We introduce an acceleration consistency constraint to bridge Newtonian mechanics and pose estimation, and design a dynamic simulated annealing strategy that adaptively schedules learning rates based on motion states. Futhermore, we devise a Kalman fusion scheme to optimize error accumulation from multi-source observations to mitigate disturbances. Experiments show PMGS’s superior performance in reconstructing high-speed nonlinear rigid motion compared to mainstream dynamic methods.

EAAI Journal 2025 Journal Article

Cross-domain human activity recognition based on deviation-graph constrained Non-Negative Matrix Factorization

• Yue Li
• Yuxing Zhi
• Huaijun Wang
• Yufan Guo
• Kan Wang
• Lei Yu
• Rong Fei
• Junhuai Li

ICML Conference 2025 Conference Paper

Diff-MoE: Diffusion Transformer with Time-Aware and Space-Adaptive Experts

• Kun Cheng
• Xiao He 0014
• Lei Yu
• Zhijun Tu
• Mingrui Zhu
• Nannan Wang 0001
• Xinbo Gao 0001
• Jie Hu 0021

Diffusion models have transformed generative modeling but suffer from scalability limitations due to computational overhead and inflexible architectures that process all generative stages and tokens uniformly. In this work, we introduce Diff-MoE, a novel framework that combines Diffusion Transformers with Mixture-of-Experts to exploit both temporarily adaptability and spatial flexibility. Our design incorporates expert-specific timestep conditioning, allowing each expert to process different spatial tokens while adapting to the generative stage, to dynamically allocate resources based on both the temporal and spatial characteristics of the generative task. Additionally, we propose a globally-aware feature recalibration mechanism that amplifies the representational capacity of expert modules by dynamically adjusting feature contributions based on input relevance. Extensive experiments on image generation benchmarks demonstrate that Diff-MoE significantly outperforms state-of-the-art methods. Our work demonstrates the potential of integrating diffusion models with expert-based designs, offering a scalable and effective framework for advanced generative modeling.

AAAI Conference 2025 Conference Paper

Effective Diffusion Transformer Architecture for Image Super-Resolution

• Kun Cheng
• Lei Yu
• Zhijun Tu
• Xiao He
• Liyu Chen
• Yong Guo
• Mingrui Zhu
• Nannan Wang

Recent advances indicate that diffusion model holds great promise in image super-resolution. While latest methods are primarily based on latent diffusion models with convolutional neural networks, there are few attempts to explore transformers, which have demonstrated remarkable performance in image generation. In this work, we design an effective diffusion transformer for image super resolution (DiT-SR) that achieves the visual quality of prior-based methods, but through a training-from-scratch manner. In practice, DiT-SR leverages an overall U-shaped architecture, and adopts uniform isotropic design for all the transformer blocks across different stages. The former facilitates multi-scale hierarchical feature extraction, while the latter reallocate the computational resources to critical layers to further enhance performance. Moreover, we thoroughly analyze the limitation of the widely used AdaLN, and present a frequency-adaptive time-step conditioning module, enhancing the model's capacity to process distinct frequency information at different time steps. Extensive experiments demonstrate that DiT-SR outperforms the existing training-from-scratch diffusion-based SR methods significantly, and even beats some of the prior-based methods on pretrained Stable Diffusion, proving the superiority of diffusion transformer in image super resolution.

ICLR Conference 2025 Conference Paper

Emergence of a High-Dimensional Abstraction Phase in Language Transformers

• Emily Cheng
• Diego Doimo
• Corentin Kervadec
• Iuri Macocco
• Lei Yu
• Alessandro Laio
• Marco Baroni

A language model (LM) is a mapping from a linguistic context to an output token. However, much remains to be known about this mapping, including how its geometric properties relate to its function. We take a high-level geometric approach to its analysis, observing, across five pre-trained transformer-based LMs and three input datasets, a distinct phase characterized by high intrinsic dimensionality. During this phase, representations (1) correspond to the first full linguistic abstraction of the input; (2) are the first to viably transfer to downstream tasks; (3) predict each other across different LMs. Moreover, we find that an earlier onset of the phase strongly predicts better language modelling performance. In short, our results suggest that a central high-dimensionality phase underlies core linguistic processing in many common LM architectures.

NeurIPS Conference 2025 Conference Paper

Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting

• Chuandong Liu
• Huijiao Wang
• Lei Yu
• Gui-Song Xia

Recent advances in 3D Gaussian Splatting have shown remarkable potential for novel view synthesis. However, most existing large-scale scene reconstruction methods rely on the divide-and-conquer paradigm, which often leads to the loss of global scene information and requires complex parameter tuning due to scene partitioning and local optimization. To address these limitations, we propose MixGS, a novel holistic optimization framework for large-scale 3D scene reconstruction. MixGS models the entire scene holistically by integrating camera pose and Gaussian attributes into a view-aware representation, which is decoded into fine-detailed Gaussians. Furthermore, a novel mixing operation combines decoded and original Gaussians to jointly preserve global coherence and local fidelity. Extensive experiments on large-scale scenes demonstrate that MixGS achieves state-of-the-art rendering quality and competitive speed, while significantly reducing computational requirements, enabling large-scale scene reconstruction training on a single 24GB VRAM GPU.

AAAI Conference 2025 Conference Paper

HomoMatcher: Achieving Dense Feature Matching with Semi-Dense Efficiency by Homography Estimation

• Xiaolong Wang
• Lei Yu
• Yingying Zhang
• Jiangwei Lao
• Lixiang Ru
• Liheng Zhong
• Jingdong Chen
• Yu Zhang

Feature matching between image pairs is a fundamental problem in computer vision that drives many applications, such as SLAM. Recently, semi-dense matching approaches have achieved substantial performance enhancements and established a widely-accepted coarse-to-fine paradigm. However, the majority of existing methods focus on improving coarse feature representation rather than the fine-matching module. Prior fine-matching techniques, which rely on point-to-patch matching probability expectation or direct regression, often lack precision and do not guarantee the continuity of feature points across sequential images. To address this limitation, this paper concentrates on enhancing the fine-matching module in the semi-dense matching framework. We employ a lightweight and efficient homography estimation network to generate the perspective mapping between patches obtained from coarse matching. This patch-to-patch approach achieves the overall alignment of two patches, resulting in a higher sub-pixel accuracy by incorporating additional constraints. By leveraging the homography estimation between patches, we can achieve a dense matching result with low computational cost. Extensive experiments demonstrate that our method achieves higher accuracy compared to previous semi-dense matchers. Meanwhile, our dense matching results exhibit similar end-point-error accuracy compared to previous dense matchers while maintaining semi-dense efficiency.

AAAI Conference 2025 Conference Paper

MTGA: Multi-View Temporal Granularity Aligned Aggregation for Event-Based Lip-Reading

• Wenhao Zhang
• Jun Wang
• Yong Luo
• Lei Yu
• Wei Yu
• Zheng He
• Jialie Shen

Lip-reading is to utilize the visual information of the speaker’s lip movements to recognize words and sentences. Existing event-based lip-reading solutions integrate different frame rate branches to learn spatio-temporal features of varying granularities. However, aggregating events into event frames inevitably leads to the loss of fine-grained temporal information within frames. To remedy this drawback, we propose a novel framework termed Multi-view Temporal Granularity aligned Aggregation (MTGA). Specifically, we first present a novel event representation method, namely time-segmented voxel graph list, where the most significant local voxels are temporally connected into a graph list. Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated. Finally, we design a temporal aggregation module that incorporates positional encoding, which enables the capture of local absolute spatial and global temporal information. Experiments demonstrate that our method outperforms both the event-based and video-based lip-reading counterparts.

NeurIPS Conference 2025 Conference Paper

Restricted Global-Aware Graph Filters Bridging GNNs and Transformer for Node Classification

• Jingyuan Zhang
• Xin Wang
• Lei Yu
• Zhirong Huang
• Li Yang
• Fengjun Zhang

Transformers have been widely regarded as a promising direction for breaking through the performance bottlenecks of Graph Neural Networks (GNNs), primarily due to their global receptive fields. However, a recent empirical study suggests that tuned classical GNNs can match or even outperform state-of-the-art Graph Transformers (GTs) on standard node classification benchmarks. Motivated by this fact, we deconstruct several representative GTs to examine how global attention components influence node representations. We find that the global attention module does not provide significant performance gains and may even exacerbate test error oscillations. Consequently, we consider that the Transformer is barely able to learn connectivity patterns that meaningfully complement the original graph topology. Interestingly, we further observe that mitigating such oscillations enables the Transformer to improve generalization in GNNs. In a nutshell, we reinterpret the Transformer through the lens of graph spectrum and reformulate it as a global-aware graph filter with band-pass characteristics and linear complexity. This unique perspective introduces multi-channel filtering constraints that effectively suppress test error oscillations. Extensive experiments (17 homophilous, heterophilous graphs) provide comprehensive empirical evidence for our perspective. This work clarifies the role of Transformers in GNNs and suggests that advancing modern GNN research may still require a return to the graph itself.

ICLR Conference 2025 Conference Paper

Robust LLM safeguarding via refusal feature adversarial training

• Lei Yu
• Virginie Do
• Karen Hambardzumyan
• Nicola Cancedda

Large language models (LLMs) are vulnerable to adversarial attacks that can elicit harmful responses. Defending against such attacks remains challenging due to the opacity of jailbreaking mechanisms and the high computational cost of training LLMs robustly. We demonstrate that adversarial attacks share a universal mechanism for circumventing LLM safeguards that works by ablating a dimension in the residual stream embedding space called the refusal feature. We further show that the operation of refusal feature ablation (RFA) approximates the worst-case perturbation of offsetting model safety. Based on these findings, we propose Refusal Feature Adversarial Training (ReFAT), a novel algorithm that efficiently performs LLM adversarial training by simulating the effect of input-level attacks via RFA. Experiment results show that ReFAT significantly improves the robustness of three popular LLMs against a wide range of adversarial attacks, with considerably less computational overhead compared to existing adversarial training methods.

IJCAI Conference 2024 Conference Paper

Imperio: Language-Guided Backdoor Attacks for Arbitrary Model Control

• Ka-Ho Chow
• Wenqi Wei
• Lei Yu

Natural language processing (NLP) has received unprecedented attention. While advancements in NLP models have led to extensive research into their backdoor vulnerabilities, the potential for these advancements to introduce new backdoor threats remains unexplored. This paper proposes Imperio, which harnesses the language understanding capabilities of NLP models to enrich backdoor attacks. Imperio provides a new model control experience. Demonstrated through controlling image classifiers, it empowers the adversary to manipulate the victim model with arbitrary output through language-guided instructions. This is achieved using a language model to fuel a conditional trigger generator, with optimizations designed to extend its language understanding capabilities to backdoor instruction interpretation and execution. Our experiments across three datasets, five attacks, and nine defenses confirm Imperio's effectiveness. It can produce contextually adaptive triggers from text descriptions and control the victim model with desired outputs, even in scenarios not encountered during training. The attack reaches a high success rate without compromising the accuracy of clean inputs and exhibits resilience against representative defenses. Supplementary materials are available at https: //khchow. com/Imperio.

AAMAS Conference 2024 Conference Paper

MA-MIX: Value Function Decomposition for Cooperative Multiagent Reinforcement Learning Based on Multi-Head Attention Mechanism

• Yu Niu
• Hengxu Zhao
• Lei Yu

Multi-Agent Deep Reinforcement Learning (MADRL) is a research field that combines deep learning and multi-agent reinforcement learning. In complex tasks, a single agent often finds it difficult to complete the task independently, thus requiring cooperation and communication between agents. However, communication between agents remains a key issue in multi-agent cooperative reinforcement learning. To address this issue, we propose a new method called Multi-Head Attention Mixing Network (MA-MIX), which aims to solve key challenges in multi-agent systems. MA-MIX is based on the multi-head attention mechanism and innovatively applied to agent networks, effectively solving the problem of information exchange and cooperation in multi-agent systems. We compared MA-MIX with traditional QMIX algorithms and other baseline algorithms. The experimental results show that MA-MIX has superior performance under the StarCraft Multi-Agent Challenge (SMAC) environment.

EAAI Journal 2024 Journal Article

YOLO_SRv2: An evolved version of YOLO_SR

• Yong Lv
• Yu Zhou
• Qing Chen
• Wenzheng Chi
• Lining Sun
• Lei Yu

TMLR Journal 2023 Journal Article

Beyond Intuition: Rethinking Token Attributions inside Transformers

• Jiamin Chen
• Xuhong Li
• Lei Yu
• Dejing Dou
• Haoyi Xiong

The multi-head attention mechanism, or rather the Transformer-based models have always been under the spotlight, not only in the domain of text processing, but also for computer vision. Several works have recently been proposed around exploring the token attributions along the intrinsic decision process. However, the ambiguity of the expression formulation can lead to an accumulation of error, which makes the interpretation less trustworthy and less applicable to different variants. In this work, we propose a novel method to approximate token contributions inside Transformers. We start from the partial derivative to each token, divide the interpretation process into attention perception and reasoning feedback with the chain rule and explore each part individually with explicit mathematical derivations. In attention perception, we propose the head-wise and token-wise approximations in order to learn how the tokens interact to form the pooled vector. As for reasoning feedback, we adopt a noise-decreasing strategy by applying the integrated gradients to the last attention map. Our method is further validated qualitatively and quantitatively through the faithfulness evaluations across different settings: single modality (BERT and ViT) and bi-modality (CLIP), different model sizes (ViT-L) and different pooling strategies (ViT-MAE) to demonstrate the broad applicability and clear improvements over existing methods.

AAAI Conference 2023 Conference Paper

High-Level Semantic Feature Matters Few-Shot Unsupervised Domain Adaptation

• Lei Yu
• Wanqi Yang
• Shengqi Huang
• Lei Wang
• Ming Yang

In few-shot unsupervised domain adaptation (FS-UDA), most existing methods followed the few-shot learning (FSL) methods to leverage the low-level local features (learned from conventional convolutional models, e.g., ResNet) for classification. However, the goal of FS-UDA and FSL are relevant yet distinct, since FS-UDA aims to classify the samples in target domain rather than source domain. We found that the local features are insufficient to FS-UDA, which could introduce noise or bias against classification, and not be used to effectively align the domains. To address the above issues, we aim to refine the local features to be more discriminative and relevant to classification. Thus, we propose a novel task-specific semantic feature learning method (TSECS) for FS-UDA. TSECS learns high-level semantic features for image-to-class similarity measurement. Based on the high-level features, we design a cross-domain self-training strategy to leverage the few labeled samples in source domain to build the classifier in target domain. In addition, we minimize the KL divergence of the high-level feature distributions between source and target domains to shorten the distance of the samples between the two domains. Extensive experiments on DomainNet show that the proposed method significantly outperforms SOTA methods in FS-UDA by a large margin (i.e., ~10%).

AAAI Conference 2023 Conference Paper

Learning from Training Dynamics: Identifying Mislabeled Data beyond Manually Designed Features

• Qingrui Jia
• Xuhong Li
• Lei Yu
• Jiang Bian
• Penghao Zhao
• Shupeng Li
• Haoyi Xiong
• Dejing Dou

While mislabeled or ambiguously-labeled samples in the training set could negatively affect the performance of deep models, diagnosing the dataset and identifying mislabeled samples helps to improve the generalization power. Training dynamics, i.e., the traces left by iterations of optimization algorithms, have recently been proved to be effective to localize mislabeled samples with hand-crafted features. In this paper, beyond manually designed features, we introduce a novel learning-based solution, leveraging a noise detector, instanced by an LSTM network, which learns to predict whether a sample was mislabeled using the raw training dynamics as input. Specifically, the proposed method trains the noise detector in a supervised manner using the dataset with synthesized label noises and can adapt to various datasets (either naturally or synthesized label-noised) without retraining. We conduct extensive experiments to evaluate the proposed method. We train the noise detector based on the synthesized label-noised CIFAR dataset and test such noise detector on Tiny ImageNet, CUB-200, Caltech-256, WebVision and Clothing1M. Results show that the proposed method precisely detects mislabeled samples on various datasets without further adaptation, and outperforms state-of-the-art methods. Besides, more experiments demonstrate that the mislabel identification can guide a label correction, namely data debugging, providing orthogonal improvements of algorithm-centric state-of-the-art techniques from the data aspect.

ICLR Conference 2021 Conference Paper

IsarStep: a Benchmark for High-level Mathematical Reasoning

• Wenda Li 0001
• Lei Yu
• Yuhuai Wu
• Lawrence C. Paulson

A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models. In this paper, we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models. We build a non-synthetic dataset from the largest repository of proofs written by human experts in a theorem prover. The dataset has a broad coverage of undergraduate and research-level mathematical and computer science theorems. In our defined task, a model is required to fill in a missing intermediate proposition given surrounding proofs. This task provides a starting point for the long-term goal of having machines generate human-readable proofs automatically. Our experiments and analysis reveal that while the task is challenging, neural models can capture non-trivial mathematical reasoning. We further design a hierarchical transformer that outperforms the transformer baseline.

EAAI Journal 2020 Journal Article

A highly robust automatic 3D reconstruction system based on integrated optimization by point line features

• Junyi Hou
• Lei Yu
• Shumin Fei

AAAI Conference 2019 Conference Paper

Diverse Exploration via Conjugate Policies for Policy Gradient Methods

• Andrew Cohen
• Xingye Qiao
• Lei Yu
• Elliot Way
• Xiangrong Tong

We address the challenge of effective exploration while maintaining good performance in policy gradient methods. As a solution, we propose diverse exploration (DE) via conjugate policies. DE learns and deploys a set of conjugate policies which can be conveniently generated as a byproduct of conjugate gradient descent. We provide both theoretical and empirical results showing the effectiveness of DE at achieving exploration, improving policy performance, and the advantage of DE over exploration by random policy perturbations.

IJCAI Conference 2019 Conference Paper

Parametric Manifold Learning of Gaussian Mixture Models

• Ziquan Liu
• Lei Yu
• Janet H. Hsiao
• Antoni B. Chan

The Gaussian Mixture Model (GMM) is among the most widely used parametric probability distributions for representing data. However, it is complicated to analyze the relationship among GMMs since they lie on a high-dimensional manifold. Previous works either perform clustering of GMMs, which learns a limited discrete latent representation, or kernel-based embedding of GMMs, which is not interpretable due to difficulty in computing the inverse mapping. In this paper, we propose Parametric Manifold Learning of GMMs (PML-GMM), which learns a parametric mapping from a low-dimensional latent space to a high-dimensional GMM manifold. Similar to PCA, the proposed mapping is parameterized by the principal axes for the component weights, means, and covariances, which are optimized to minimize the reconstruction loss measured using Kullback-Leibler divergence (KLD). As the KLD between two GMMs is intractable, we approximate the objective function by a variational upper bound, which is optimized by an EM-style algorithm. Moreover, We derive an efficient solver by alternating optimization of subproblems and exploit Monte Carlo sampling to escape from local minima. We demonstrate the effectiveness of PML-GMM through experiments on synthetic, eye-fixation, flow cytometry, and social check-in data.

AAAI Conference 2018 Conference Paper

Diverse Exploration for Fast and Safe Policy Improvement

• Andrew Cohen
• Lei Yu
• Robert Wright

We study an important yet under-addressed problem of quickly and safely improving policies in online reinforcement learning domains. As its solution, we propose a novel exploration strategy - diverse exploration (DE), which learns and deploys a diverse set of safe policies to explore the environment. We provide DE theory explaining why diversity in behavior policies enables effective exploration without sacri- ficing exploitation. Our empirical study shows that an online policy improvement algorithm framework implementing the DE strategy can achieve both fast policy improvement and safe online performance.

AAAI Conference 2017 Conference Paper

A Hybrid Collaborative Filtering Model with Deep Structure for Recommender Systems

• Xin Dong
• Lei Yu
• Zhonghuo Wu
• Yuxia Sun
• Lingfeng Yuan
• Fangxi Zhang

Collaborative filtering(CF) is a widely used approach in recommender systems to solve many real-world problems. Traditional CF-based methods employ the user-item matrix which encodes the individual preferences of users for items for learning to make recommendation. In real applications, the rating matrix is usually very sparse, causing CF-based methods to degrade significantly in recommendation performance. In this case, some improved CF methods utilize the increasing amount of side information to address the data sparsity problem as well as the cold start problem. However, the learned latent factors may not be effective due to the sparse nature of the user-item matrix and the side information. To address this problem, we utilize advances of learning effective representations in deep learning, and propose a hybrid model which jointly performs deep users and items’ latent factors learning from side information and collaborative filtering from the rating matrix. Extensive experimental results on three real-world datasets show that our hybrid model outperforms other methods in effectively utilizing side information and achieves performance improvement.

YNIMG Journal 2016 Journal Article

Financial literacy is associated with white matter integrity in old age

• S. Duke Han
• Patricia A. Boyle
• Konstantinos Arfanakis
• Debra Fleischman
• Lei Yu
• Bryan D. James
• David A. Bennett

AAAI Conference 2015 Conference Paper

Improving Approximate Value Iteration with Complex Returns by Bounding

• Robert Wright
• Xingye Qiao
• Steven Loscalzo
• Lei Yu

Approximate value iteration (AVI) is a widely used technique in reinforcement learning. Most AVI methods do not take full advantage of the sequential relationship between samples within a trajectory in deriving value estimates, due to the challenges in dealing with the inherent bias and variance in the n-step returns. We propose a bounding method which uses a negatively biased but relatively low variance estimator generated from a complex return to provide a lower bound on the observed value of a traditional one-step return estimator. In addition, we develop a new Bounded FQI algorithm, which efficiently incorporates the bounding method into an AVI framework. Experiments show that our method produces more accurate value estimates than existing approaches, resulting in improved policies.

JAAMAS Journal 2014 Journal Article

Predictive feature selection for genetic policy search

• Steven Loscalzo
• Robert Wright
• Lei Yu

Abstract Automatic learning of control policies is becoming increasingly important to allow autonomous agents to operate alongside, or in place of, humans in dangerous and fast-paced situations. Reinforcement learning (RL), including genetic policy search algorithms, comprise a promising technology area capable of learning such control policies. Unfortunately, RL techniques can take prohibitively long to learn a sufficiently good control policy in environments described by many sensors (features). We argue that in many cases only a subset of available features are needed to learn the task at hand, since others may represent irrelevant or redundant information. In this work, we propose a predictive feature selection framework that analyzes data obtained during execution of a genetic policy search algorithm to identify relevant features on-line. This serves to constrain the policy search space and reduces the time needed to locate a sufficiently good policy by embedding feature selection into the process of learning a control policy. We explore this framework through an instantiation called predictive feature selection embedded in neuroevolution of augmenting topology (NEAT), or PFS-NEAT. In an empirical study, we demonstrate that PFS-NEAT is capable of enabling NEAT to successfully find good control policies in two benchmark environments, and show that it can outperform three competing feature selection algorithms, FS-NEAT, FD-NEAT, and SAFS-NEAT, in several variants of these environments.

AIJ Journal 2004 Journal Article

A selective sampling approach to active feature selection

• Huan Liu
• Hiroshi Motoda
• Lei Yu

JMLR Journal 2004 Journal Article

Efficient Feature Selection via Analysis of Relevance and Redundancy

• Lei Yu
• Huan Liu

Feature selection is applied to reduce the number of features in many applications where data has hundreds or thousands of features. Existing feature selection methods mainly focus on finding relevant features. In this paper, we show that feature relevance alone is insufficient for efficient feature selection of high-dimensional data. We define feature redundancy and propose to perform explicit redundancy analysis in feature selection. A new framework is introduced that decouples relevance analysis and redundancy analysis. We develop a correlation-based method for relevance and redundancy analysis, and conduct an empirical study of its efficiency and effectiveness comparing with representative methods. [abs] [ pdf ]