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Yingying Li

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14 papers

2 author rows

ICLR Conference 2025 Conference Paper

MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction

Jing Yang
Minyue Jiang
Sen Yang
Xiao Tan 0001
Yingying Li
Errui Ding
Jingdong Wang 0001
Hanli Wang

The construction of vectorized high-definition map typically requires capturing both category and geometry information of map elements. Current state-of-the-art methods often adopt solely either point-level or instance-level representation, overlooking the strong intrinsic relationship between points and instances. In this work, we propose a simple yet efficient framework named MGMapNet (multi-granularity map network) to model map elements with multi-granularity representation, integrating both coarse-grained instance-level and fine-grained point-level queries. Specifically, these two granularities of queries are generated from the multi-scale bird's eye view features using a proposed multi-granularity aggregator. In this module, instance-level query aggregates features over the entire scope covered by an instance, and the point-level query aggregates features locally. Furthermore, a point-instance interaction module is designed to encourage information exchange between instance-level and point-level queries. Experimental results demonstrate that the proposed MGMapNet achieves state-of-the-art performances, surpassing MapTRv2 by 5.3 mAP on the nuScenes dataset and 4.4 mAP on the Argoverse2 dataset, respectively.

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ICLR Conference 2025 Conference Paper

Robust System Identification: Finite-sample Guarantees and Connection to Regularization

Hyuk Park 0005
Grani A. Hanasusanto
Yingying Li

We consider the problem of learning nonlinear dynamical systems from a single sample trajectory. While the least squares estimate (LSE) is commonly used for this task, it suffers from poor identification errors when the sample size is small or the model fails to capture the system's true dynamics. To overcome these limitations, we propose a robust LSE framework, which incorporates robust optimization techniques, and prove that it is equivalent to regularizing LSE using general Schatten $p$-norms. We provide non-asymptotic performance guarantees for linear systems, achieving an error rate of $\widetilde{\mathcal{O}}(1/\sqrt{T})$, and show that it avoids the curse of dimensionality, unlike state-of-the-art Wasserstein robust optimization models. Empirical results demonstrate substantial improvements in real-world system identification and online control tasks, outperforming existing methods.

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NeurIPS Conference 2024 Conference Paper

Identification of Analytic Nonlinear Dynamical Systems with Non-asymptotic Guarantees

Negin Musavi
Ziyao Guo
Geir Dullerud
Yingying Li

This paper focuses on the system identification of an important class of nonlinear systems: nonlinear systems that are linearly parameterized, which enjoy wide applications in robotics and other mechanical systems. We consider two system identification methods: least-squares estimation (LSE), which is a point estimation method; and set-membership estimation (SME), which estimates an uncertainty set that contains the true parameters. We provide non-asymptotic convergence rates for LSE and SME under i. i. d. control inputs and control policies with i. i. d. random perturbations, both of which are considered as non-active-exploration inputs. Compared with the counter-example based on piecewise-affine systems in the literature, the success of non-active exploration in our setting relies on a key assumption about the system dynamics: we require the system functions to be real-analytic. Our results, together with the piecewise-affine counter-example, reveal the importance of differentiability in nonlinear system identification through non-active exploration. Lastly, we numerically compare our theoretical bounds with the empirical performance of LSE and SME on a pendulum example and a quadrotor example.

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YNICL Journal 2023 Journal Article

Effect of corticosubcortical iron deposition on dysfunction in CADASIL is mediated by white matter microstructural damage

Xiuqin Jia
Yingying Li
Yunqing Ying
Xuejia Jia
Weijun Tang
Yueyan Bian
Jiajia Zhang
Danny J.J. Wang

Iron dysregulation may attenuate cognitive performance in patients with CADASIL. However, the underlying pathophysiological mechanisms remain incompletely understood. Whether white matter microstructural changes mediate these processes is largely unclear. In the present study, 30 cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) patients were confirmed via genetic analysis and 30 sex- and age-matched healthy controls underwent multimodal MRI examinations and neuropsychological assessments. Quantitative susceptibility mapping and peak width of skeletonized mean diffusivity (PSMD) were analyzed. Mediation effect analysis was performed to explore the interrelationship between iron deposition, white matter microstructural changes and cognitive deficits in CADASIL. Cognitive deterioration was most affected in memory and executive function, followed by attention and working memory in CADASIL. Excessive iron in the temporal-precuneus pathway and deep gray matter specific to CADASIL were identified. Mediation analysis further revealed that PSMD mediated the relationship between iron concentration and cognitive profile in CADASIL. The present findings provide a new perspective on iron deposition in the corticosubcortical circuit and its contribution to disease-related selective cognitive decline, in which iron concentration may affect cognition by white matter microstructural changes in CADASIL.

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NeurIPS Conference 2023 Conference Paper

Online Adaptive Policy Selection in Time-Varying Systems: No-Regret via Contractive Perturbations

Yiheng Lin
James A. Preiss
Emile Anand
Yingying Li
Yisong Yue
Adam Wierman

We study online adaptive policy selection in systems with time-varying costs and dynamics. We develop the Gradient-based Adaptive Policy Selection (GAPS) algorithm together with a general analytical framework for online policy selection via online optimization. Under our proposed notion of contractive policy classes, we show that GAPS approximates the behavior of an ideal online gradient descent algorithm on the policy parameters while requiring less information and computation. When convexity holds, our algorithm is the first to achieve optimal policy regret. When convexity does not hold, we provide the first local regret bound for online policy selection. Our numerical experiments show that GAPS can adapt to changing environments more quickly than existing benchmarks.

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AAAI Conference 2023 Conference Paper

Online Hyperparameter Optimization for Class-Incremental Learning

Yaoyao Liu
Yingying Li
Bernt Schiele
Qianru Sun

Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings—where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first introduce the key hyperparameters that influence the tradeoff, e.g., knowledge distillation (KD) loss weights, learning rates, and classifier types. Then, we formulate the hyperparameter optimization process as an online Markov Decision Process (MDP) problem and propose a specific algorithm to solve it. We apply local estimated rewards and a classic bandit algorithm Exp3 to address the issues when applying online MDP methods to the CIL protocol. Our method consistently improves top-performing CIL methods in both TFH and TFS settings, e.g., boosting the average accuracy of TFH and TFS by 2.2 percentage points on ImageNet-Full, compared to the state-of-the-art. Code is provided at https://class-il.mpi-inf.mpg.de/online/

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YNICL Journal 2023 Journal Article

Sex differences in frontotemporal atrophy in CADASIL revealed by 7-Tesla MRI

Xiuqin Jia
Chen Ling
Yingying Li
Jinyuan Zhang
Zhixin Li
Xuejia Jia
Danny J.J. Wang
Zihao Zhang

Brain damage caused by small vessel disease (SVD) differs between males and females. We aimed to examine the pure sex-specific neuroanatomical mechanisms of SVD adjusted for voxel-based expected effects of age and sex on healthy brain volume. Thirty-one female and 32 male genetic SVD (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, CADASIL) patients and 55 sex- and age-matched healthy controls (HCs) underwent 7-Tesla MRI examinations. Voxel-based W-score maps were calculated from volumes and deformations of brain tissues, controlling for the expected effects of age and sex in HCs. Significant cognitive declines in working memory and executive function were identified in male CADASIL patients compared to female patients. Greater gray matter (GM) atrophy was found in the bilateral orbitofrontal cortex (OFC), left anterior cingulate cortex (ACC), left entorhinal cortex (EC), and right temporooccipital cortex in male CADASIL patients than in females. Working memory was associated with volumes in the right OFC specific to female CADASIL patients, whereas visuospatial ability was associated with the right hOcl (primary visual area, BA 17) volume specific to males. The current findings indicate that sex affects the pathogenesis of CADASIL, ranging from differences in neuroanatomy to those in behavioral performance, which may facilitate the development of more effective sex-specific therapeutic strategies for CADASIL and SVD.

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AAAI Conference 2021 Conference Paper

Online Optimal Control with Affine Constraints

Yingying Li
Subhro Das
Na Li

This paper considers online optimal control with affine constraints on the states and actions under linear dynamics with bounded random disturbances. The system dynamics and constraints are assumed to be known and time invariant but the convex stage cost functions change adversarially. To solve this problem, we propose Online Gradient Descent with Buffer Zones (OGD-BZ). Theoretically, we show that OGD- BZ with proper parameters can guarantee the system to satisfy all the constraints despite any admissible disturbances. Further, we investigate the policy regret of OGD-BZ, which compares OGD-BZ’s performance with the performance of the optimal linear policy in hindsight. We show that OGD-BZ can achieve a policy regret upper bound that is square root of the horizon length multiplied by some logarithmic terms of the horizon length under proper algorithm parameters.

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IJCAI Conference 2021 Conference Paper

Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video

Jie Wu
Wei Zhang
Guanbin Li
Wenhao Wu
Xiao Tan
Yingying Li
Errui Ding
Liang Lin

In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video, WSSTAD aims to localize a spatio-temporal tube (i. e. , a sequence of bounding boxes at consecutive times) that encloses the abnormal event, with only coarse video-level annotations as supervision during training. To address this challenging task, we propose a dual-branch network which takes as input the proposals with multi-granularities in both spatial-temporal domains. Each branch employs a relationship reasoning module to capture the correlation between tubes/videolets, which can provide rich contextual information and complex entity relationships for the concept learning of abnormal behaviors. Mutually-guided Progressive Refinement framework is set up to employ dual-path mutual guidance in a recurrent manner, iteratively sharing auxiliary supervision information across branches. It impels the learned concepts of each branch to serve as a guide for its counterpart, which progressively refines the corresponding branch and the whole framework. Furthermore, we contribute two datasets, i. e. , ST-UCF-Crime and STRA, consisting of videos containing spatio-temporal abnormal annotations to serve as the benchmarks for WSSTAD. We conduct extensive qualitative and quantitative evaluations to demonstrate the effectiveness of the proposed approach and analyze the key factors that contribute more to handle this task.

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NeurIPS Conference 2020 Conference Paper

Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms

Yingying Li
Na Li

We consider online convex optimization with time-varying stage costs and additional switching costs. Since the switching costs introduce coupling across all stages, multi-step-ahead (long-term) predictions are incorporated to improve the online performance. However, longer-term predictions tend to suffer from lower quality. Thus, a critical question is: how to reduce the impact of long-term prediction errors on the online performance? To address this question, we introduce a gradient-based online algorithm, Receding Horizon Inexact Gradient (RHIG), and analyze its performance by dynamic regrets in terms of the temporal variation of the environment and the prediction errors. RHIG only considers at most $W$-step-ahead predictions to avoid being misled by worse predictions in the longer term. The optimal choice of $W$ suggested by our regret bounds depends on the tradeoff between the variation of the environment and the prediction accuracy. Additionally, we apply RHIG to a well-established stochastic prediction error model and provide expected regret and concentration bounds under correlated prediction errors. Lastly, we numerically test the performance of RHIG on quadrotor tracking problems.

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NeurIPS Conference 2019 Conference Paper

Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis

Yingying Li
Xin Chen
Na Li

This paper studies the online optimal control problem with time-varying convex stage costs for a time-invariant linear dynamical system, where a finite lookahead window of accurate predictions of the stage costs are available at each time. We design online algorithms, Receding Horizon Gradient-based Control (RHGC), that utilize the predictions through finite steps of gradient computations. We study the algorithm performance measured by dynamic regret: the online performance minus the optimal performance in hindsight. It is shown that the dynamic regret of RHGC decays exponentially with the size of the lookahead window. In addition, we provide a fundamental limit of the dynamic regret for any online algorithms by considering linear quadratic tracking problems. The regret upper bound of one RHGC method almost reaches the fundamental limit, demonstrating the effectiveness of the algorithm. Finally, we numerically test our algorithms for both linear and nonlinear systems to show the effectiveness and generality of our RHGC.

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IROS Conference 2009 Conference Paper

Tracking point or diffusing targets using mobile sensor networks under sensing noises

Yingying Li
Yun-Hui Liu 0001

This paper presents a distributed algorithm for a mobile sensor network to track targets with unknown motion. We formulates the target tracking as a multi-objective optimization problem which integrates the tracking quality, the energy saving and the network connectivity. To cope with sensing noises, we use the determinant of the covariance matrix of target estimation as the tracking quality measure and compute its partial derivatives for the optimization process. Virtual nodes are introduced to represent obstacles in the environment. Furthermore this algorithm can be extended to solve the problem of source tracking where sensors can only detect the density of the diffusing substances emitted by the source. Therefore a whole tracking framework has been set up which can be easily extended for applications under complicated situations. Simulations demonstrate the effectiveness of the proposed algorithm in energy conservation and tracking accuracy under different situations.

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IROS Conference 2008 Conference Paper

Distributed target tracking with energy consideration using mobile sensor networks

Yingying Li
Yun-Hui Liu 0001
Hengyang Zhang
Hesheng Wang 0001
Xuanping Cai
Dongxiang Zhou

This paper presents a fully distributed algorithm for target tracking using a mobile sensor network. It tries to maintain the target being visible to the mobile network all the time while consuming as little motion energy as possible. Meanwhile the network connectivity is maintained. At every time, only the nodes around the target are activated while other nodes keep idle. Certain functions are defined to quantify the main aspects in the tracking such as the target escaping probability and the network connectivity status. They transform the tracking into a multi-objective optimization problem. To solve this global problem, a local motion strategy is proposed. Simulation results show that our algorithm yields good performance.

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ICRA Conference 2007 Conference Paper

Energy Saving Target Tracking Using Mobile Sensor Networks

Yingying Li
Yun-Hui Liu 0001

This paper addresses the problem of tracking a mobile target using a mobile sensor network while minimizing the energy consumption and maintaining the network connectivity during the tracking process. While minimizing the tracking energy consumption is proved to be NP-complete, an approximately optimal solution named breadth-first leader-follower strategy is presented. Nodes close to the target predicted position find their following nodes based on breadth-first search and lead them to cover the probable region where the target may exist next time instant. Meantime the overall network connectivity can be maintained. We have proved that the energy consumption of the nodes moving under the control of the proposed algorithm is within a scalar factor of the optimal consumption. Simulation has been conducted to demonstrate the performance of the algorithm in different situations. The results show that our algorithm can yield good performance in target tracking while consuming little energy.

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