Arrow Research search

Author name cluster

Nan Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

43 papers
2 author rows

Possible papers

43

JBHI Journal 2026 Journal Article

Direct PET-to-CT Generation for Attenuation Correction: A Slice-to-Slice Continual Transformer Segmentation-Aware Network

  • Rongjun Ge
  • Hanyuan Zheng
  • Yuxin Liu
  • Liutao Yang
  • Li Wang
  • Xu Ji
  • Jingtao Shen
  • Nan Li

Direct synthetic computed tomography (CT) generation from positron emission tomography (PET) plays a crucial role in PET attenuation correction, yet providing detailed structural information to compensate for functional imaging. Compared to the widely used PET/CT and indirect PET/MR-CT, the direct PET-to-CT translation method (denoted as PET-to-CT) offers several advantages: 1) The CT required for PET-to-CT is directly obtained from PET, thereby avoiding the intermediate errors generated in the inter-step processes of multimodal scanning in PET/CT and PET/MR-CT. 2) Furthermore, direct PET-to-CT eliminates the requirement for supplementary imaging equipment, thereby reducing complexity and scan duration in contrast to PET/CT and PET/MR-CT imaging. Thus, direct PET-to-CT is highly promising for clinical applications. However, it faces challenges, including spatial resolution mismatches between PET and CT, as well as voxel-wise semantic differences arising from functional and structural imaging. To address these challenges, this paper proposes a 2D hierarchical method called S2SCT (Slice-to-Slice Continual Transformer)-SA (Segmentation-aware) Network. It uses a slice-continual network to acquire semantic transformation knowledge from each PET slice to a CT slice, facilitating the conversion between functional and structural imaging domains. Subsequently, the segmentation-aware network is designed to futher capture spatial correlations both between slices and within slice, resulting in improved CT spatial resolution. The experiment results demonstrate that our proposed method outperforms mainstream methods in both CT generation and attenuation correction, as evidenced by both visual results and metric values.

Details DOI

AAAI Conference 2026 Conference Paper

UV-RGS: Relightable 3D Gaussian Splatting from Unposed Views Under Varied Illuminations

  • Wei Feng
  • Chi Huang
  • Qi Zhang
  • Qian Zhang
  • Nan Li

The latest advancements in scene relighting have been predominantly driven by inverse rendering with 3D Gaussian Splatting (3DGS). However, existing methods remain overly reliant on precise camera parameters under static illumination conditions, which is prohibitively expensive and even impractical in real-world scenarios. In this paper, we propose a novel learning from Unposed views under Varied illuminations Relightable 3D Gaussian Splatting (dubbed UV-RGS), to address this challenge by jointly optimizing camera poses, 3DGS representations, surface materials, and environment illuminations (i.e., unknown and varied lighting conditions in training) using only unposed views under varied lightings. Firstly, UV-RGS presents a viewpoint dividing strategy to group inputs into constituent units, enabling each unit can perform similar poses and illuminations. Next, for each unit, to get the constituent model, UV-RGS establishes an incrementally pose learning module to estimate coarse camera parameters, which also enjoy a proxy-view refinement to alleviate the sparse view learning. Additionally, for all constituent unit models, we introduce a holistic model learning strategy that integrates progressive unit aggregation component and the 3DGS coupled with camera poses joint optimization, which realizes the scene high-fidelity perception by the physical-based rendering. Extensive experiments on both real-world and synthetic challenging datasets demonstrate the effectiveness of UV-RGS, achieving the state-of-the-art performance for scene inverse rendering by learning 3DGS from only unposed views under varied illuminations.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

2D Gaussian Splatting for Outdoor Scene Decomposition and Relighting

  • Wei Feng
  • Kangrui Ye
  • Qi Zhang
  • Qian Zhang
  • Nan Li

Gaussian splatting techniques have recently revolutionized outdoor scene decomposition and relighting through multi-view images. However, achieving high rendering quality still requires a fixed lighting condition among all input views, which is costly or even impractical to capture in outdoor scenes. In this paper, we propose outdoor scene decomposition and relighting with 2D Gaussian splatting (OSDR-GS), a novel inverse rendering strategy under outdoor changing and unknown lighting conditions. Firstly, we present a lighting-based group learning framework that categorizes input images into multiple lighting groups, to learn the separate lighting from each group individually. Secondly, OSDR-GS introduces a fine-grained outdoor lighting component to represent sun-light and sky-light, respectively, which are also adjusted via the correlative exposure factors adaptively. Finally, we construct a visibility-driven shadow module to characterize the nuanced interplay of light and occlusion realistically, for eliminating the uncertainty of dark pixels on lighting-based group learning. Extensive experiments on multiple challenging outdoor datasets validate the effectiveness of OSDR-GS, which achieves the state-of-the-art performance in changing lighting scene inverse rendering.

PDF Details DOI

ICRA Conference 2025 Conference Paper

A Visual Servo System for Robotic on-Orbit Servicing Based on 3D Perception of Non-Cooperative Satellite

  • Panpan Zhao
  • Li Jin
  • Yeheng Chen
  • Jiachen Li
  • Xiuqiang Song
  • Wenxuan Chen
  • Nan Li
  • Wenjuan Du

The 3D perception of satellites, including both their shape and pose, is a key foundation for robotic on-orbit servicing. However, the demanding space environment-such as intense and dim illumination-presents significant challenges. Previous non-cooperative methods focus on specific geometric features like solar panel brackets or docking rings, overlooking the satellite's overall shape and increasing the risk of collisions during grasping. Additionally, satellites are often weakly textured, limiting the accuracy of 3D perception. To address these issues, we propose, for the first time, a 3D perceptionbased visual servo system of non-cooperative satellites. This system combines reconstruction and tracking to enhance shape perception and pose estimation accuracy in orbital conditions. Specifically, we employ an alternating iterative strategy to simultaneously reconstruct and track the satellite and introduce a novel constraint to fuse different cues under extreme conditions. Further, we develop a simulation environment platform, a dualarm microgravity grasping system, and an online monitoring module to enhance system capabilities for on-orbit servicing. Synthetic and real-world datasets from the simulation environment are also created for experimental validation. Results show that each module of our system achieves state-of-the-art performance.

Details

NeurIPS Conference 2025 Conference Paper

Efficient and Generalizable Mixed-Precision Quantization via Topological Entropy

  • Nan Li
  • Yonghui Su
  • Lianbo Ma

Network quantization effectively reduces both memory footprints and inference time of deep neural networks, enabling their deployment on resource-constrained devices. To fully utilize the multiple bit-width arithmetic operations of the hardware, mixed-precision quantization (MPQ) is developed to assign different bit-widths to each layer. However, the quantization policy obtained by existing MPQ methods struggles to achieve the objectives of efficiency and generalization simultaneously. In this paper, we propose an efficient and generalizable MPQ based on topological entropy (TE) (GMPQ-TE). Specifically, TE, derived from \textit{topological data analysis}, effectively measures the quantization sensitivity of each layer by using the minibatch of data with the same label. Furthermore, we observe that TE remains consistent across various datasets and shows a strong correlation with both quantized model accuracy and bit-width. Thus, MPQ is formulated as a single-pass linear programming problem, obtaining a generalizable quantization policy in a few seconds (11s on MobileNet-V2). Extensive experiments show that the quantization policy obtained on CIFAR-10 can generalize to ImageNet and PASCAL VOC. GMPQ-TE achieves a competitive accuracy-complexity trade-off compared to state-of-the-art MPQ methods.

PDF Details

IJCAI Conference 2025 Conference Paper

Transferable Relativistic Predictor: Mitigating Cross-Task Cold-Start Issue in NAS

  • Nan Li
  • Bing Xue
  • Lianbo Ma
  • Mengjie Zhang

In neural architecture search (NAS), the relativistic predictor has recently emerged as an attractive technique to solve ranking issue for performance evaluation by predicting the relativistic ranking of architecture pair rather than the absolute performance of an architecture. However, it suffers from a significant cold-start issue, requiring a large amount of evaluated architectures to train an effective predictor on new datasets. In this paper, we propose a transferable relativistic predictor (TRP). Specifically, we construct a proxy dataset using the transferable cheaper-to-obtain performance estimation to softly label the rank between architectural pairs. The soft label with a smooth and easy-to-optimize loss function facilitates the learning of expressive and generalizable representations on the proxy dataset. Furthermore, we construct Chebyshev interpolation for correlation curve to adaptively determine the number of evaluated architectures required on each dataset. Extensive experimental results in different search spaces show the superior performance of TRP compared with state-of-the-art predictors. TRP requires only 54 and 73 evaluated architectures for a warm start on the CIFAR-10 and CIFAR-100 under the DARTS search space.

PDF Details DOI

TIST Journal 2024 Journal Article

FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

  • Nan Li
  • Bo Kang
  • Jefrey Lijffijt
  • Tijl De Bie

Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g., by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority: a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy: a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility: the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state-of-the-art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.

Details DOI

ICRA Conference 2024 Conference Paper

Implicit Coarse-to-Fine 3D Perception for Category-level Object Pose Estimation from Monocular RGB Image

  • Jia Li
  • Li Jin
  • Xibin Song
  • Yeheng Chen
  • Nan Li
  • Xueying Qin

Category-level object pose estimation demonstrates robust generalization capabilities that benefit robotics applications. However, exclusive reliance on RGB images without leveraging any 3D information introduces ambiguity in the translation and size of objects, leading to suboptimal performance. In this paper, we propose a framework for category-level pose estimation from a single RGB image in an end-to-end manner, i. e. , Feature Auxiliary Perception Network (FAP-Net). To address inaccurate pose estimation caused by the inherent ambiguity of RGB images, we design a coarse-to-fine approach that first harnesses geometry supervision to facilitate coarse 3D feature perception and subsequently refines the features based on pose and size constraints. Experimental results on REAL275 and CAMERA25 demonstrate that FAP-Net achieves significant improvements (14. 7% on 10°10cm and 11. 4% on IoU50 on the real-scene REAL275 dataset) over the state-of-the-art and real-time inference (42 FPS).

Details

JBHI Journal 2023 Journal Article

A Self-Supervised Framework for Learning Biological Entities Representation by Fusing Class Information

  • Nan Li
  • Zhihao Yang
  • Jian Wang
  • Hongfei Lin

Ontologies are widely utilized in the biological domain for data annotation, integration, and analysis. Some representation learning methods have been proposed to learn the representation of entities to assist intelligent applications, such as knowledge discovery. However, most of them neglect the class information of entities in the ontology. In this article, we propose a unified framework, named ERCI, which jointly optimizes the knowledge graph embedding model and self-supervised learning. In this way, we can generate embeddings of bio-entities by fusing the class information. Moreover, ERCI is a pluggable framework that can be easily incorporated with any knowledge graph embedding model. We validate ERCI in two different ways. In the first way, we utilize the protein embeddings learned by the ERCI to predict protein-protein interactions on two different datasets. In the second way, we leverage the gene and disease embeddings generated by the ERCI to predict gene-disease associations. In addition, we create three datasets to simulate the long-tail scenario and evaluate ERCI on these. Experimental results show that ERCI has superior performance on all metrics compared with the state-of-the-art methods.

Details DOI

AAAI Conference 2023 Conference Paper

LADA-Trans-NER: Adaptive Efficient Transformer for Chinese Named Entity Recognition Using Lexicon-Attention and Data-Augmentation

  • Jiguo Liu
  • Chao Liu
  • Nan Li
  • Shihao Gao
  • Mingqi Liu
  • Dali Zhu

Recently, word enhancement has become very popular for Chinese Named Entity Recognition (NER), reducing segmentation errors and increasing the semantic and boundary information of Chinese words. However, these methods tend to ignore the semantic relationship before and after the sentence after integrating lexical information. Therefore, the regularity of word length information has not been fully explored in various word-character fusion methods. In this work, we propose a Lexicon-Attention and Data-Augmentation (LADA) method for Chinese NER. We discuss the challenges of using existing methods in incorporating word information for NER and show how our proposed methods could be leveraged to overcome those challenges. LADA is based on a Transformer Encoder that utilizes lexicon to construct a directed graph and fuses word information through updating the optimal edge of the graph. Specially, we introduce the advanced data augmentation method to obtain the optimal representation for the NER task. Experimental results show that the augmentation done using LADA can considerably boost the performance of our NER system and achieve significantly better results than previous state-of-the-art methods and variant models in the literature on four publicly available NER datasets, namely Resume, MSRA, Weibo, and OntoNotes v4. We also observe better generalization and application to a real-world setting from LADA on multi-source complex entities.

PDF Details DOI

ICLR Conference 2022 Conference Paper

Neural Program Synthesis with Query

  • Di Huang
  • Rui Zhang 0040
  • Xing Hu 0001
  • Xishan Zhang
  • Pengwei Jin
  • Nan Li
  • Zidong Du
  • Qi Guo 0001

Aiming to find a program satisfying the user intent given input-output examples, program synthesis has attracted increasing interest in the area of machine learning. Despite the promising performance of existing methods, most of their success comes from the privileged information of well-designed input-output examples. However, providing such input-output examples is unrealistic because it requires the users to have the ability to describe the underlying program with a few input-output examples under the training distribution. In this work, we propose a query-based framework that trains a query neural network to generate informative input-output examples automatically and interactively from a large query space. The quality of the query depends on the amount of the mutual information between the query and the corresponding program, which can guide the optimization of the query framework. To estimate the mutual information more accurately, we introduce the functional space (F-space) which models the relevance between the input-output examples and the programs in a differentiable way. We evaluate the effectiveness and generalization of the proposed query-based framework on the Karel task and the list processing task. Experimental results show that the query-based framework can generate informative input-output examples which achieve and even outperform well-designed input-output examples.

Details

IJCAI Conference 2021 Conference Paper

IIAS: An Intelligent Insurance Assessment System through Online Real-time Conversation Analysis

  • Mengdi Zhou
  • Shuang Peng
  • Minghui Yang
  • Nan Li
  • Hongbin Wang
  • Li Qiao
  • Haitao Mi
  • Zujie Wen

With the development of Chinese medical insurance industry, the amount of claim cases is growing rapidly. Ultimately, more claims necessarily indicate that the insurance company has to spend much time assessing claims and decides how much compensation the claimant should receive, which is a highly professional process that involves many complex operations. Therefore, the insurance assessor's role is essential. However, for the junior assessor often lacking in practical experience, it is not easy to quickly handle such an online procedure. In order to alleviate assessors' cognitive workload, we propose an Intelligent Insurance Assessment System (IIAS) that helps effectively collect claimant information through online real-time conversation analysis. With the assistance of IIAS, the average time cost of the insurance assessment procedure is reduced from 55 minutes to 35 minutes.

PDF Details DOI

IJCAI Conference 2019 Conference Paper

A Practical Semi-Parametric Contextual Bandit

  • Yi Peng
  • Miao Xie
  • Jiahao Liu
  • Xuying Meng
  • Nan Li
  • Cheng Yang
  • Tao Yao
  • Rong Jin

Classic multi-armed bandit algorithms are inefficient for a large number of arms. On the other hand, contextual bandit algorithms are more efficient, but they suffer from a large regret due to the bias of reward estimation with finite dimensional features. Although recent studies proposed semi-parametric bandits to overcome these defects, they assume arms' features are constant over time. However, this assumption rarely holds in practice, since real-world problems often involve underlying processes that are dynamically evolving over time especially for the special promotions like Singles' Day sales. In this paper, we formulate a novel Semi-Parametric Contextual Bandit Problem to relax this assumption. For this problem, a novel Two-Steps Upper-Confidence Bound framework, called Semi-Parametric UCB (SPUCB), is presented. It can be flexibly applied to linear parametric function problem with a satisfied gap-free bound on the n-step regret. Moreover, to make our method more practical in online system, an optimization is proposed for dealing with high dimensional features of a linear function. Extensive experiments on synthetic data as well as a real dataset from one of the largest e-commercial platforms demonstrate the superior performance of our algorithm.

PDF Details

IJCAI Conference 2019 Conference Paper

Hybrid Item-Item Recommendation via Semi-Parametric Embedding

  • Peng Hu
  • Rong Du
  • Yao Hu
  • Nan Li

Nowadays, item-item recommendation plays an important role in modern recommender systems. Traditionally, this is either solved by behavior-based collaborative filtering or content-based meth- ods. However, both kinds of methods often suffer from cold-start problems, or poor performance due to few behavior supervision; and hybrid methods which can leverage the strength of both kinds of methods are needed. In this paper, we propose a semi-parametric embedding framework for this problem. Specifically, the embedding of an item is composed of two parts, i. e. , the parametric part from content information and the non-parametric part designed to encode behavior information; meanwhile, a deep learning algorithm is proposed to learn two parts simultaneously. Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of the proposed method.

PDF Details

IROS Conference 2019 Conference Paper

Mapping for Planetary Rovers from Terramechanics Perspective *

  • Ruyi Zhou
  • Liang Ding 0001
  • Haibo Gao
  • Wenhao Feng
  • Zongquan Deng
  • Nan Li

In an autonomous scientific exploration system, the terrain map generated from mapping process integrates sensing information from multiple aspects and lays the base for decision making processes. With the increasing challenges in planetary exploration, equipping planetary rovers with the principles of terramechanics is becoming more and more common, especially on rough or intricate terrain. However, it is difficult for conventional maps with elevation information only to reflect terrain mechanical properties, which play important roles in terramechanics-based simulation or motion control. This study extracts the dominant parameters in terrain bearing and shearing models, and presents a multi-layered grid map with fundamental geometric and mechanical elements. A corresponding mapping scheme based on dense visual input is designed to reconstruct elevation in the map and predict terrain mechanical parameters of the entire visual field. Experiments are conducted to verify the practicability of the approach proposed in a Mars emulation yard with a rover prototype.

Details

AAAI Conference 2019 Conference Paper

Robust Online Matching with User Arrival Distribution Drift

  • Yu-Hang Zhou
  • Chen Liang
  • Nan Li
  • Cheng Yang
  • Shenghuo Zhu
  • Rong Jin

Recently, online matching problems have attracted much attention due to its emerging applications in internet advertising. Most existing online matching methods have adopted either adversarial or stochastic user arrival assumption, while on both of them significant limitation exists. The adversarial model does not exploit existing knowledge of the user sequence, and thus can be pessimistic in practice. On other hands, the stochastic model assumes that users are drawn from a stationary distribution, which may not be true in real applications. In this paper, we consider a novel user arrival model where users are drawn from drifting distribution, which is a hybrid case between the adversarial and stochastic model, and propose a new approach RDLA to deal with such assumption. Instead of maximizing empirical total revenues on the revealed users, RDLA leverages distributionally robust optimization techniques to learn dual variables via a worst-case consideration over an ambiguity set on the underlying user distribution. Experiments on a real-world dataset exhibit the superiority of our approach.

PDF Details

AAAI Conference 2019 Conference Paper

Semi-Parametric Sampling for Stochastic Bandits with Many Arms

  • Mingdong Ou
  • Nan Li
  • Cheng Yang
  • Shenghuo Zhu
  • Rong Jin

We consider the stochastic bandit problem with a large candidate arm set. In this setting, classic multi-armed bandit algorithms, which assume independence among arms and adopt non-parametric reward model, are inefficient, due to the large number of arms. By exploiting arm correlations based on a parametric reward model with arm features, contextual bandit algorithms are more efficient, but they can also suffer from large regret in practical applications, due to the reward estimation bias from mis-specified model assumption or incomplete features. In this paper, we propose a novel Bayesian framework, called Semi-Parametric Sampling (SPS), for this problem, which employs semi-parametric function as the reward model. Specifically, the parametric part of SPS, which models expected reward as a parametric function of arm feature, can efficiently eliminate poor arms from candidate set. The non-parametric part of SPS, which adopts nonparametric reward model, revises the parametric estimation to avoid estimation bias, especially on the remained candidate arms. We give an implementation of SPS, Linear SPS (LSPS), which utilizes linear function as the parametric part. In semi-parametric environment, theoretical analysis shows that LSPS achieves better regret bound (i. e. Õ( √ N 1−α dα √ T) with α ∈ [0, 1]) than existing approaches. Also, experiments demonstrate the superiority of the proposed approach.

PDF Details

IJCAI Conference 2018 Conference Paper

Multinomial Logit Bandit with Linear Utility Functions

  • Mingdong Ou
  • Nan Li
  • Shenghuo Zhu
  • Rong Jin

Multinomial logit bandit is a sequential subset selection problem which arises in many applications. In each round, the player selects a K-cardinality subset from N candidate items, and receives a reward which is governed by a multinomial logit (MNL) choice model considering both item utility and substitution property among items. The player's objective is to dynamically learn the parameters of MNL model and maximize cumulative reward over a finite horizon T. This problem faces the exploration-exploitation dilemma, and the involved combinatorial nature makes it non-trivial. In recent years, there have developed some algorithms by exploiting specific characteristics of the MNL model, but all of them estimate the parameters of MNL model separately and incur a regret bound which is not preferred for large candidate set size N. In this paper, we consider the linear utility MNL choice model whose item utilities are represented as linear functions of d-dimension item features, and propose an algorithm, titled LUMB, to exploit the underlying structure. It is proven that the proposed algorithm achieves regret which is free of candidate set size. Experiments show the superiority of the proposed algorithm.

PDF Details

AAAI Conference 2018 Conference Paper

Tau-FPL: Tolerance-Constrained Learning in Linear Time

  • Ao Zhang
  • Nan Li
  • Jian Pu
  • Jun Wang
  • Junchi Yan
  • Hongyuan Zha

In many real-world applications, learning a classifier with false-positive rate under a specified tolerance is appealing. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classi- fiers, which are of limitation in methodology since they do not directly incorporate the false-positive rate tolerance. In this paper, we propose a novel scoring-thresholding approach, τ- False Positive Learning (τ-FPL) to address this problem. We show that the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed τ-FPL over the existing approaches.

PDF Details

IJCAI Conference 2017 Conference Paper

Affinity Learning for Mixed Data Clustering

  • Nan Li
  • Longin Jan Latecki

In this paper, we propose a novel affinity learning based framework for mixed data clustering, which includes: how to process data with mixed-type attributes, how to learn affinities between data points, and how to exploit the learned affinities for clustering. In the proposed framework, each original data attribute is represented with several abstract objects defined according to the specific data type and values. Each attribute value is transformed into the initial affinities between the data point and the abstract objects of attribute. We refine these affinities and infer the unknown affinities between data points by taking into account the interconnections among the attribute values of all data points. The inferred affinities between data points can be exploited for clustering. Alternatively, the refined affinities between data points and the abstract objects of attributes can be transformed into new data features for clustering. Experimental results on many real world data sets demonstrate that the proposed framework is effective for mixed data clustering.

PDF Details

IJCAI Conference 2017 Conference Paper

Open Category Classification by Adversarial Sample Generation

  • Yang Yu
  • Wei-Yang Qu
  • Nan Li
  • Zimin Guo

In real-world classification tasks, it is difficult to collect training samples from all possible categories of the environment. Therefore, when an instance of an unseen class appears in the prediction stage, a robust classifier should be able to tell that it is from an unseen class, instead of classifying it to be any known category. In this paper, adopting the idea of adversarial learning, we propose the ASG framework for open-category classification. ASG generates positive and negative samples of seen categories in the unsupervised manner via an adversarial learning strategy. With the generated samples, ASG then learns to tell seen from unseen in the supervised manner. Experiments performed on several datasets show the effectiveness of ASG.

PDF Details

IJCAI Conference 2017 Conference Paper

Restart and Random Walk in Local Search for Maximum Vertex Weight Cliques with Evaluations in Clustering Aggregation

  • Yi Fan
  • Nan Li
  • Chengqian Li
  • Zongjie Ma
  • Longin Jan Latecki
  • Kaile Su

The Maximum Vertex Weight Clique (MVWC) problem is NP-hard and also important in real-world applications. In this paper we propose to use the restart and the random walk strategies to improve local search for MVWC. If a solution is revisited in some particular situation, the search will restart. In addition, when the local search has no other options except dropping vertices, it will use random walk. Experimental results show that our solver outperforms state-of-the-art solvers in DIMACS and finds a new best-known solution. Also it is the unique solver which is comparable with state-of-the-art methods on both BHOSLIB and large crafted graphs. Furthermore we evaluated our solver in clustering aggregation. Experimental results on a number of real data sets demonstrate that our solver outperforms the state-of-the-art for solving the derived MVWC problem and helps improve the final clustering results.

PDF Details

TIST Journal 2014 Journal Article

Learning Probabilistic Hierarchical Task Networks as Probabilistic Context-Free Grammars to Capture User Preferences

  • Nan Li
  • William Cushing
  • Subbarao Kambhampati
  • Sungwook Yoon

We introduce an algorithm to automatically learn probabilistic hierarchical task networks (pHTNs) that capture a user's preferences on plans by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are twofold. First, in contrast with prior work, which employs HTNs to represent domain physics or search control knowledge, we use HTNs to model user preferences. Second, while most prior work on HTN learning requires additional information (e.g., annotated traces or tasks) to assist the learning process, our system only takes plan traces as input. Initially, we will assume that users carry out preferred plans more frequently, and thus the observed distribution of plans is an accurate representation of user preference. We then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. Taking the prevalent perspective of viewing HTNs as grammars over primitive actions, we adapt an expectation-maximization (EM) technique from the discipline of probabilistic grammar induction to acquire probabilistic context-free grammars (pCFG) that capture the distribution on plans. To account for the difference between the distributions of possible and preferred plans, we subsequently modify this core EM technique by rescaling its input. We empirically demonstrate that the proposed approaches are able to learn HTNs representing user preferences better than the inside-outside algorithm. Furthermore, when feasibility constraints are obfuscated, the algorithm with rescaled input performs better than the algorithm with the original input.

Details DOI

NeurIPS Conference 2014 Conference Paper

Top Rank Optimization in Linear Time

  • Nan Li
  • Rong Jin
  • Zhi-Hua Zhou

Bipartite ranking aims to learn a real-valued ranking function that orders positive instances before negative instances. Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list. Most existing approaches are either to optimize task specific metrics or to extend the rank loss by emphasizing more on the error associated with the top ranked instances, leading to a high computational cost that is super-linear in the number of training instances. We propose a highly efficient approach, titled TopPush, for optimizing accuracy at the top that has computational complexity linear in the number of training instances. We present a novel analysis that bounds the generalization error for the top ranked instances for the proposed approach. Empirical study shows that the proposed approach is highly competitive to the state-of-the-art approaches and is 10-100 times faster.

PDF Details

ICRA Conference 2012 Conference Paper

An online stair-climbing control method for a transformable tracked robot

  • Nan Li
  • Shugen Ma
  • Bin Li 0001
  • Minghui Wang
  • Yuechao Wang

Stair-climbing is a necessary capacity for mobile robots. This paper presents an online control method for the stair-climbing of a transformable tracked robot, Amoeba-II, and this robot is also an isomerism-modules robot with different mechanism modules. Based on the reasonable compartmentalization and kinematics analysis of the stair-climbing process, the coordination of the rotations of modules can reduce the slippage between tracks and terrain. To ensure that the robot can climb stairs with enough capability and stability, the stair-climbing criterion for the robot has been established based on the force analysis of each stage of the stair-climbing procedure. Meanwhile, the interference-avoiding criterion has been set up to avoid the interference between the non-tracked module of the robot and the stair. The experiment for the stair-climbing of the robot has been implemented to certify the validity of the online stair-climbing control method for a transformable tracked robot.

Details

IROS Conference 2012 Conference Paper

An optimization design method for the mechanism parameters of an amphibious transformable robot

  • Nan Li
  • Shugen Ma
  • Minghui Wang
  • Bin Li 0001
  • Yuechao Wang

This paper presents an optimal design method for a new robot called amphibious transformable robot which can not only perform reconfiguration but also implement tasks in amphibious environment. To satisfy a range of performance requirements for the robot in aquatic and terrestrial environments, the multi-objective optimization method is adopted to design the robot which can achieve the optimal comprehensive performance in the amphibious environment. Based on the kinematics and dynamic analysis of the robot, the multi-objective optimization problem of the mechanism parameters design is established on the mapping relationships between the performance indexes, and then Multi-Objective Genetic Algorithm is proposed to get Pareto solution. Based on combination weighting method of multi-attribute decision-making, the result can be extracted and used to direct the mechanism design of the amphibious transformable robot, Amoeba-II. The experiment for the maneuverability of Amoeba-II in the amphibious environment is performed to verify the validity and applicability of the mechanism-parameters design method of amphibious transformable robot based on Multi-Objective Genetic Algorithm.

Details

NeurIPS Conference 2012 Conference Paper

Clustering Aggregation as Maximum-Weight Independent Set

  • Nan Li
  • Longin Latecki

We formulate clustering aggregation as a special instance of Maximum-Weight Independent Set (MWIS) problem. For a given dataset, an attributed graph is constructed from the union of the input clusterings generated by different underlying clustering algorithms with different parameters. The vertices, which represent the distinct clusters, are weighted by an internal index measuring both cohesion and separation. The edges connect the vertices whose corresponding clusters overlap. Intuitively, an optimal aggregated clustering can be obtained by selecting an optimal subset of non-overlapping clusters partitioning the dataset together. We formalize this intuition as the MWIS problem on the attributed graph, i. e. , finding the heaviest subset of mutually non-adjacent vertices. This MWIS problem exhibits a special structure. Since the clusters of each input clustering form a partition of the dataset, the vertices corresponding to each clustering form a maximal independent set (MIS) in the attributed graph. We propose a variant of simulated annealing method that takes advantage of this special structure. Our algorithm starts from each MIS, which is close to a distinct local optimum of the MWIS problem, and utilizes a local search heuristic to explore its neighborhood in order to find the MWIS. Extensive experiments on many challenging datasets show that: 1. our approach to clustering aggregation automatically decides the optimal number of clusters; 2. it does not require any parameter tuning for the underlying clustering algorithms; 3. it can combine the advantages of different underlying clustering algorithms to achieve superior performance; 4. it is robust against moderate or even bad input clusterings.

PDF Details

AAAI Conference 2010 Conference Paper

Integrating Transfer Learning in Synthetic Student

  • Nan Li
  • William Cohen
  • Ken Koedinger

Building an intelligent agent, which simulates human-level learning appropriate for learning math, science, or a second language, could potentially benefit both education in understanding human learning, and artificial intelligence in creating human-level intelligence. Recently, we have proposed an efficient approach to acquiring procedural knowledge using transfer learning. However, it operated as a separate module. In this paper, we describe how to integrate this module into a machine-learning agent, SimStudent, that learns procedural knowledge from examples and through problem solving. We illustrate this method in the domain of algebra, after which we consider directions for future research in this area.

PDF Details

IROS Conference 2010 Conference Paper

Progress in the biomechatronic design and control of a hand prosthesis

  • Xinqing Wang
  • Yiwei Liu 0001
  • Dapeng Yang 0001
  • Nan Li
  • Li Jiang 0001
  • Hong Liu 0002

A five-fingered, multi-sensory biomechatronic hand with sEMG interface is presented. The cambered palm is specially designed to enhance the stability while grasping. The location of the thumb is designed by maximizing interaction area between the thumb and other fingers. The opposite thumb could grasp along a cone surface, while maintaining its function. By taken the advantage of coupling linkage mechanism, each finger with three phalanges could fulfill flexion-extension movement independently. Besides, each finger is equipped with torque and position sensors. Thus, the cosmetics and dexterity are improved remarkably compared to conventional prosthesis. The hardware architecture is divided into control system and EMG signal processing system. Moreover, a novel two-stage decision strategy combing the position-based impedance control scheme is implemented to realize the real-time sEMG control of the hand. According to the grasp experiment results, the hand can accomplish several grasp modes stably; the success rate of 10 modes is up to 90%.

Details

IJCAI Conference 2009 Conference Paper

  • Nan Li
  • Subbarao Kambhampati
  • Sungwook Yoon

While much work on learning in planning focused on learning domain physics (i. e. , action models), and search control knowledge, little attention has been paid towards learning user preferences on desirable plans. Hierarchical task networks (HTN) are known to provide an effective way to encode user prescriptions about what constitute good plans. However, manual construction of these methods is complex and error prone. In this paper, we propose a novel approach to learning probabilistic hierarchical task networks that capture user preferences by examining user-produced plans given no prior information about the methods (in contrast, most prior work on learning within the HTN framework focused on learning “method preconditions”—i. e. , domain physics—assuming that the structure of the methods is given as input). We will show that this problem has close parallels to the problem of probabilistic grammar induction, and describe how grammar induction methods can be adapted to learn task networks. We will empirically demonstrate the effectiveness of our approach by showing that task networks we learn are able to generate plans with a distribution close to the distribution of the userpreferred plans.

PDF Details

ICAPS Conference 2009 Conference Paper

Learning User Plan Preferences Obfuscated by Feasibility Constraints

  • Nan Li
  • William Cushing
  • Subbarao Kambhampati
  • Sung Wook Yoon

It has long been recognized that users can have complex preferences on plans. Non-intrusive learning of such preferences by observing the plans executed by the user is an attractive idea. Unfortunately, the executed plans are often not a true representation of user preferences, as they result from the interaction between user preferences and feasibility constraints. In the travel planning scenario, a user whose true preference is to travel by a plane may well be frequently observed traveling by car because of feasibility constraints (perhaps the user is a poor graduate student). In this work, we describe a novel method for learning true user preferences obfuscated by such feasibility constraints. Our base learner induces probabilistic hierarchical task networks (pHTNs) from sets of training plans. Our approach is to rescale the input so that it represents the user's preference distribution on plans rather than the observed distribution on plans.

Details