Yibo Wang

AAAI Conference 2026 Conference Paper

Classifier-induced Reciprocal Points for Multi-label Open-set Recognition

• Yibo Wang
• Yong Rui
• Min-Ling Zhang

Multi-label learning is a practical machine learning paradigm dealing with instances associated with multiple labels simultaneously. Most existing multi-label learning studies are designed under the closed-world assumption, i.e. a fixed size of label space. However, it encounters significant difficulties in open-set scenarios, where test data may contain unknown labels absent from the training set to be recognized. Existing method typically tackles this challenging problem through sub-labeling approximations and prototype-based comparisons, which often overlooks the implicit information carried by unknown labels. To address this, we propose a novel framework CREM, i.e. Classifier-induced REciprocal point for Multi-label open-set recognition, which rethinks the above problem from the reciprocal point perspective. Specifically, reciprocal points are formulated by explicitly constraining the opposition feature space to a learnable bounded margin. Then reciprocal points can be induced through the classifier with the instance-wise bias eliminated. Subsequently, a unified optimization framework is introduced to jointly facilitate the classifier and reciprocal points induction. Extensive experiments demonstrate the effectiveness and superiority of the proposed CREM approach in the multi-label open-set recognition paradigm.

YNIMG Journal 2026 Journal Article

Dynamic brain network reconfiguration under stress: A multiphase fMRI study

• Xiao Luo
• Jiahao Huang
• Kun Shi
• Yiqing Ma
• Yibo Wang
• Xinli Tang
• Xiaolin Zhao
• Xi Ren

NeurIPS Conference 2025 Conference Paper

Ada-R1: Hybrid-CoT via Bi-Level Adaptive Reasoning Optimization

• Haotian Luo
• Haiying He
• Yibo Wang
• Jinluan Yang
• Rui Liu
• Naiqiang Tan
• Xiaochun Cao
• Dacheng Tao

Recently, long-thought reasoning models achieve strong performance on complex reasoning tasks, but often incur substantial inference overhead, making efficiency a critical concern. Our empirical analysis reveals that the benefit of using Long-CoT varies across problems: while some problems require elaborate reasoning, others show no improvement—or even degraded accuracy. This motivates adaptive reasoning strategies that tailor reasoning depth to the input. However, prior work primarily reduces redundancy within long reasoning paths, limiting exploration of more efficient strategies beyond the Long-CoT paradigm. To address this, we propose a novel two-stage framework for adaptive and efficient reasoning. First, we construct a hybrid reasoning model by merging long and short CoT models to enable diverse reasoning styles. Second, we apply bi-level preference training to guide the model to select suitable reasoning styles (group-level), and prefer concise and correct reasoning within each style group (instance-level). Experiments demonstrate that our method significantly reduces inference costs compared to other baseline approaches, while maintaining performance. Notably, on five mathematical datasets, the average length of reasoning is reduced by more than 50\%, highlighting the potential of adaptive strategies to optimize reasoning efficiency in large language models.

NeurIPS Conference 2025 Conference Paper

Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via Collective Monte Carlo Tree Search

• Huanjin Yao
• Jiaxing Huang
• Wenhao Wu
• Jingyi Zhang
• Yibo Wang
• Shunyu Liu
• Yingjie Wang
• YuXin Song

In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this end, we propose Collective Monte Carlo Tree Search (CoMCTS), a new learning-to-reason method for MLLMs, which introduces the concept of collective learning into ``tree search'' for effective and efficient reasoning-path searching and learning. The core idea of CoMCTS is to leverage collective knowledge from multiple models to collaboratively conjecture, search and identify effective reasoning paths toward correct answers via four iterative operations including Expansion, Simulation and Error Positioning, Backpropagation, and Selection. Using CoMCTS, we construct Mulberry-260k, a multimodal dataset with a tree of rich, explicit and well-defined reasoning nodes for each question. With Mulberry-260k, we perform collective SFT to train our model, Mulberry, a series of MLLMs with o1-like step-by-step Reasoning and Reflection capabilities. Extensive experiments demonstrate the superiority of our proposed methods on various benchmarks. Code is available at https: //github. com/HJYao00/Mulberry.

NeurIPS Conference 2025 Conference Paper

Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation

• Yibo Wang
• Tiansheng Huang
• Li Shen
• Huanjin Yao
• Haotian Luo
• Rui Liu
• Naiqiang Tan
• Jiaxing Huang

Harmful fine-tuning attack introduces significant security risks to the fine-tuning services. Main-stream defenses aim to vaccinate the model such that the later harmful fine-tuning attack is less effective. However, our evaluation results show that such defenses are fragile-- with a few fine-tuning steps, the model still can learn the harmful knowledge. To this end, we do further experiment and find that an embarrassingly simple solution-- adding purely random perturbations to the fine-tuned model, can recover the model from harmful behaviors, though it leads to a degradation in the model’s fine-tuning performance. To address the degradation of fine-tuning performance, we further propose \methodname, which optimizes an adaptive perturbation that will be applied to the model after fine-tuning. \methodname maintains model's safety alignment performance without compromising downstream fine-tuning performance. Comprehensive experiments are conducted on different harmful ratios, fine-tuning tasks and mainstream LLMs, where the average harmful scores are reduced by up-to 21. 2%, while maintaining fine-tuning performance. As a by-product, we analyze the adaptive perturbation and show that different layers in various LLMs have distinct safety coefficients. Source code available at https: //github. com/w-yibo/Panacea.

NeurIPS Conference 2025 Conference Paper

R1-ShareVL: Incentivizing Reasoning Capabilities of Multimodal Large Language Models via Share-GRPO

• Huanjin Yao
• Qixiang Yin
• Jingyi Zhang
• Min Yang
• Yibo Wang
• Wenhao Wu
• Fei Su
• Li Shen

In this work, we aim to incentivize the reasoning ability of Multimodal Large Language Models (MLLMs) via reinforcement learning (RL) and develop an effective approach that mitigates the sparse reward and advantage vanishing issues during RL. To this end, we propose Share-GRPO, a novel RL approach that tackle these issues by exploring and sharing diverse reasoning trajectories over expanded question space. Specifically, Share-GRPO first expands the question space for a given question via data transformation techniques, and then encourages MLLM to effectively explore diverse reasoning trajectories over the expanded question space and shares the discovered reasoning trajectories across the expanded questions during RL. In addition, Share-GRPO also shares reward information during advantage computation, which estimates solution advantages hierarchically across and within question variants, allowing more accurate estimation of relative advantages and improving the stability of policy training. Extensive evaluations over 6 widely-used reasoning benchmarks showcase the superior performance of our method. Code is available at https: //github. com/HJYao00/R1-ShareVL.

AAAI Conference 2025 Conference Paper

Revisiting Projection-Free Online Learning with Time-Varying Constraints

• Yibo Wang
• Yuanyu Wan
• Lijun Zhang

We investigate constrained online convex optimization, in which decisions must belong to a fixed and typically complicated domain, and are required to approximately satisfy additional time-varying constraints over the long term. In this setting, the commonly used projection operations are often computationally expensive or even intractable. To avoid the time-consuming operation, several projection-free methods have been proposed with an O(T^¾ (log T)^½) regret bound and an O(T^⅞) cumulative constraint violation (CCV) bound for general convex losses. In this paper, we improve this result and further establish novel regret and CCV bounds when loss functions are strongly convex. The primary idea is to first construct a composite surrogate loss, involving the original loss and constraint functions, by utilizing the Lyapunov-based technique. Then, we propose a parameter-free variant of the classical projection-free method, namely online Frank-Wolfe (OFW), and run this new extension over the online-generated surrogate loss. Theoretically, for general convex losses, we achieve an O(T^¾) regret bound and an O(T^¾ log T) CCV bound, both of which are order-wise tighter than existing results. For strongly convex losses, we establish new guarantees of an O(T^⅔) regret bound and an O(T^⅚) CCV bound. Moreover, we also extend our methods to a more challenging setting with bandit feedback, obtaining similar theoretical findings. Empirically, experiments on real-world datasets have demonstrated the effectiveness of our methods.

NeurIPS Conference 2025 Conference Paper

SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models

• Yibo Wang
• Guangda Huzhang
• Qingguo Chen
• Zhao Xu
• Weihua Luo
• Kaifu Zhang
• Lijun Zhang

Self-play fine-tuning has demonstrated promising abilities in adapting large language models (LLMs) to downstream tasks with limited real-world data. The basic principle is to iteratively refine the model with real samples and synthetic ones generated from itself. However, the existing methods primarily focus on the relative gaps between the rewards for two types of data, neglecting their absolute values. Through theoretical analysis, we identify that the gap-based methods suffer from unstable evolution, due to the potentially degenerated objectives. To address this limitation, we introduce a novel self-play fine-tuning method, namely \underline{S}elf-\underline{P}l\underline{A}y via Noise \underline{C}ontrastive \underline{E}stimation (SPACE), which leverages noise contrastive estimation to capture the real-world data distribution. Specifically, SPACE treats synthetic samples as auxiliary components, and discriminates them from the real ones in a binary classification manner. As a result, SPACE independently optimizes the absolute reward values for each type of data, ensuring a consistently meaningful objective and thereby avoiding the instability issue. Theoretically, we show that the optimal solution of the objective in SPACE aligns with the underlying distribution of real-world data, and SPACE guarantees a provably stable convergence to the optimal distribution. Empirically, we show that SPACE significantly improves the performance of LLMs over various tasks, and outperforms supervised fine-tuning that employs much more real-world samples. Compared to gap-based self-play fine-tuning methods, SPACE exhibits remarkable superiority and stable evolution.

AAAI Conference 2025 Conference Paper

Towards Unbiased Information Extraction and Adaptation in Cross-Domain Recommendation

• Yibo Wang
• Yingchun Jian
• Wenhao Yang
• Shiyin Lu
• Lei Shen
• Bing Wang
• Xiaoyi Zeng
• Lijun Zhang

Cross-Domain Recommendation (CDR) leverages additional knowledge from auxiliary domains to address the long-standing data sparsity issue. However, existing methods typically acquire this knowledge by minimizing the average loss over all domains, overlooking the fact that different domains possess different user-preference distributions. As a result, the acquired knowledge may contain biased information from data-rich domains, leading to performance degradation in data-scarce domains. In this paper, we propose a novel CDR method, which takes domain distinctions into consideration to extract and adapt unbiased information. Specifically, our method consists of two key components: Unbiased Information Extraction (UIE) and Unbiased Information Adaptation (UIA). In the UIE, inspired by distributionally robust optimization, we optimize the worst-case performance across all domains to extract domain-invariant information, preventing the potential bias from auxiliary domains. In the UIA, we introduce a new user-item attention module, which employs domain-specific information from historically interacted items to attend the adaptation of domain-invariant information. To verify the effectiveness of our method, we conduct extensive experiments on three real-world datasets, each of which contains three extremely sparse domains. Experimental results demonstrate the considerable superiority of our proposed method compared to baselines.

NeurIPS Conference 2025 Conference Paper

Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs

• Yibo Wang
• Hai-Long Sun
• Guangda Huzhang
• Qingguo Chen
• Zhao Xu
• Weihua Luo
• Kaifu Zhang
• Lijun Zhang

Recently, self-play fine-tuning (SPIN) has been proposed to adapt large language models to downstream applications with scarce expert-annotated data, by iteratively generating synthetic responses from the model itself. However, SPIN is designed to optimize the current reward advantages of annotated responses over synthetic responses at hand, which may gradually vanish during iterations, leading to \textit{unstable optimization}. Moreover, the utilization of reference policy induces a \textit{misalignment} issue between the reward formulation for training and the metric for generation. To address these limitations, we propose a novel \textbf{T}riplet-based \textbf{S}elf-\textbf{P}lay f\textbf{I}ne-tu\textbf{N}ing (TSPIN) method that integrates two key designs. First, beyond current advantages, TSPIN additionally incorporates historical advantages between iteratively generated responses and proto-synthetic responses produced by the initial policy. Even if the current advantages diminish, historical advantages remain effective, stabilizing the overall optimization. Second, TSPIN introduces the entropy constraint into the self-play framework, which is theoretically justified to support reference-free fine-tuning, eliminating the training-generation discrepancy. Empirical results on various tasks demonstrate not only the superior performance of TSPIN over SPIN, but also its stable evolution during iterations. Remarkably, compared to supervised fine-tuning, TSPIN achieves comparable or even better performance with only $25\\%$ samples, highlighting its effectiveness when faced with scarce annotated data.

JMLR Journal 2025 Journal Article

Universal Online Convex Optimization Meets Second-order Bounds

• Lijun Zhang
• Yibo Wang
• Guanghui Wang
• Jinfeng Yi
• Tianbao Yang

Recently, several universal methods have been proposed for online convex optimization, and attain minimax rates for multiple types of convex functions simultaneously. However, they need to design and optimize one surrogate loss for each type of functions, making it difficult to exploit the structure of the problem and utilize existing algorithms. In this paper, we propose a simple strategy for universal online convex optimization, which avoids these limitations. The key idea is to construct a set of experts to process the original online functions, and deploy a meta-algorithm over the linearized losses to aggregate predictions from experts. Specifically, the meta-algorithm is required to yield a second-order bound with excess losses, so that it can leverage strong convexity and exponential concavity to control the meta-regret. In this way, our strategy inherits the theoretical guarantee of any expert designed for strongly convex functions and exponentially concave functions, up to a double logarithmic factor. As a result, we can plug in off-the-shelf online solvers as black-box experts to deliver problem-dependent regret bounds. For general convex functions, it maintains the minimax optimality and also achieves a small-loss bound. Furthermore, we extend our universal strategy to online composite optimization, where the loss function comprises a time-varying function and a fixed regularizer. To deal with the composite loss functions, we employ a meta-algorithm based on the optimistic online learning framework, which not only enjoys a second-order bound, but also can utilize estimations for upcoming loss functions. With suitable configurations, we show that the additional regularizer does not contribute to the meta-regret, thus ensuring the universality in the composite setting. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

NeurIPS Conference 2024 Conference Paper

Adaptive Variance Reduction for Stochastic Optimization under Weaker Assumptions

• Wei Jiang
• Sifan Yang
• Yibo Wang
• Lijun Zhang

This paper explores adaptive variance reduction methods for stochastic optimization based on the STORM technique. Existing adaptive extensions of STORM rely on strong assumptions like bounded gradients and bounded function values, or suffer an additional $\mathcal{O}(\log T)$ term in the convergence rate. To address these limitations, we introduce a novel adaptive STORM method that achieves an optimal convergence rate of $\mathcal{O}(T^{-1/3})$ for non-convex functions with our newly designed learning rate strategy. Compared with existing approaches, our method requires weaker assumptions and attains the optimal convergence rate without the additional $\mathcal{O}(\log T)$ term. We also extend the proposed technique to stochastic compositional optimization, obtaining the same optimal rate of $\mathcal{O}(T^{-1/3})$. Furthermore, we investigate the non-convex finite-sum problem and develop another innovative adaptive variance reduction method that achieves an optimal convergence rate of $\mathcal{O}(n^{1/4} T^{-1/2} )$, where $n$ represents the number of component functions. Numerical experiments across various tasks validate the effectiveness of our method.

NeurIPS Conference 2024 Conference Paper

Advancing Tool-Augmented Large Language Models: Integrating Insights from Errors in Inference Trees

• Sijia Chen
• Yibo Wang
• Yi-Feng Wu
• Qing-Guo Chen
• Zhao Xu
• Weihua Luo
• Kaifu Zhang
• Lijun Zhang

Tool-augmented large language models (LLMs) leverage tools, often in the form of APIs, to improve their reasoning capabilities on complex tasks. This enables them to act as intelligent agents interacting with the real world. The recently introduced ToolLLaMA model by Qin et al. [2023] utilizes the depth-first search-based decision tree (DFSDT) mechanism for multi-step reasoning with $16000+$ real-world APIs, effectively enhancing the performance of tool-augmented LLMs compared to traditional chain reasoning mechanisms. However, their approach only employs successful paths from decision trees (also called inference trees) for supervised fine-tuning (SFT), missing out on the potential learning opportunities from failed paths. Inspired by this, we propose an inference trajectory optimization framework based on preference learning to address this limitation. We first introduce a novel method for constructing step-wise preference data from tree-like expert trajectories, which leverages the previously ignored failed explorations in the decision trees. In the subsequent training phase, we first fine-tune the LLM with successful tool-usage expert trajectories and then apply direct preference optimization (DPO) with the preference data to update the LLM's policy, resulting in our ToolPrefer-LLaMA (TP-LLaMA) model. This approach not only enhances the utilization of original expert data but also broadens the learning space of the model. Our experiments demonstrate that by obtaining insights from errors in inference trees, TP-LLaMA significantly outperforms the baselines across almost all test scenarios by a large margin and exhibits better generalization capabilities with unseen APIs. At the same time, TP-LLaMA has also demonstrated superior reasoning efficiency compared to the baselines, making it more suitable for complex tool-usage reasoning tasks.

AAAI Conference 2024 Conference Paper

Non-stationary Projection-Free Online Learning with Dynamic and Adaptive Regret Guarantees

• Yibo Wang
• Wenhao Yang
• Wei Jiang
• Shiyin Lu
• Bing Wang
• Haihong Tang
• Yuanyu Wan
• Lijun Zhang

Projection-free online learning has drawn increasing interest due to its efficiency in solving high-dimensional problems with complicated constraints. However, most existing projection-free online methods focus on minimizing the static regret, which unfortunately fails to capture the challenge of changing environments. In this paper, we investigate non-stationary projection-free online learning, and choose dynamic regret and adaptive regret to measure the performance. Specifically, we first provide a novel dynamic regret analysis for an existing projection-free method named BOGD_IP, and establish an O(T^¾ (1+P_T)) dynamic regret bound, where P_T denotes the path-length of the comparator sequence. Then, we improve the upper bound to O(T^¾ (1+P_T)^¼) by running multiple BOGD_IP algorithms with different step sizes in parallel, and tracking the best one on the fly. Our results are the first general-case dynamic regret bounds for projection-free online learning, and can recover the existing O(T^¾) static regret by setting P_T = 0. Furthermore, we propose a projection-free method to attain an O(?^¾) adaptive regret bound for any interval with length?, which nearly matches the static regret over that interval. The essential idea is to maintain a set of BOGD_IP algorithms dynamically, and combine them by a meta algorithm. Moreover, we demonstrate that it is also equipped with an O(T^¾ (1+P_T)^¼) dynamic regret bound. Finally, empirical studies verify our theoretical findings.

NeurIPS Conference 2024 Conference Paper

Online Composite Optimization Between Stochastic and Adversarial Environments

• Yibo Wang
• Sijia Chen
• Wei Jiang
• Wenhao Yang
• Yuanyu Wan
• Lijun Zhang

We study online composite optimization under the Stochastically Extended Adversarial (SEA) model. Specifically, each loss function consists of two parts: a fixed non-smooth and convex regularizer, and a time-varying function which can be chosen either stochastically, adversarially, or in a manner that interpolates between the two extremes. In this setting, we show that for smooth and convex time-varying functions, optimistic composite mirror descent (OptCMD) can obtain an $\mathcal{O}(\sqrt{\sigma_{1: T}^2} + \sqrt{\Sigma_{1: T}^2})$ regret bound, where $\sigma_{1: T}^2$ and $\Sigma_{1: T}^2$ denote the cumulative stochastic variance and the cumulative adversarial variation of time-varying functions, respectively. For smooth and strongly convex time-varying functions, we establish an $\mathcal{O}((\sigma_{\max}^2 + \Sigma_{\max}^2)\log(\sigma_{1: T}^2 + \Sigma_{1: T}^2))$ regret bound, where $\sigma_{\max}^2$ and $\Sigma_{\max}^2$ denote the maximal stochastic variance and the maximal adversarial variation, respectively. For smooth and exp-concave time-varying functions, we achieve an $\mathcal{O}(d \log (\sigma_{1: T}^2 + \Sigma_{1: T}^2))$ bound where $d$ denotes the dimensionality. Moreover, to deal with the unknown function type in practical problems, we propose a multi-level \textit{universal} algorithm that is able to achieve the desirable bounds for three types of time-varying functions simultaneously. It should be noticed that all our findings match existing bounds for the SEA model without the regularizer, which implies that there is \textit{no price} in regret bounds for the benefits gained from the regularizer.

TMLR Journal 2024 Journal Article

Uncertainty in Graph Neural Networks: A Survey

• Fangxin Wang
• Yuqing Liu
• Kay Liu
• Yibo Wang
• Sourav Medya
• Philip S. Yu

Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources such as inherent randomness in data and model training errors can lead to unstable and erroneous predictions. Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty with an emphasis on its integration in graph learning. We compare and summarize existing graph uncertainty theory and methods, alongside the corresponding downstream tasks. Thereby, we bridge the gap between theory and practice, meanwhile connecting different GNN communities. Moreover, our work provides valuable insights into promising directions in this field.

NeurIPS Conference 2024 Conference Paper

Universal Online Convex Optimization with $1$ Projection per Round

• Wenhao Yang
• Yibo Wang
• Peng Zhao
• Lijun Zhang

To address the uncertainty in function types, recent progress in online convex optimization (OCO) has spurred the development of universal algorithms that simultaneously attain minimax rates for multiple types of convex functions. However, for a $T$-round online problem, state-of-the-art methods typically conduct $O(\log T)$ projections onto the domain in each round, a process potentially time-consuming with complicated feasible sets. In this paper, inspired by the black-box reduction of Cutkosky and Orabona [2018], we employ a surrogate loss defined over simpler domains to develop universal OCO algorithms that only require $1$ projection. Embracing the framework of prediction with expert advice, we maintain a set of experts for each type of functions and aggregate their predictions via a meta-algorithm. The crux of our approach lies in a uniquely designed expert-loss for strongly convex functions, stemming from an innovative decomposition of the regret into the meta-regret and the expert-regret. Our analysis sheds new light on the surrogate loss, facilitating a rigorous examination of the discrepancy between the regret of the original loss and that of the surrogate loss, and carefully controlling meta-regret under the strong convexity condition. With only $1$ projection per round, we establish optimal regret bounds for general convex, exponentially concave, and strongly convex functions simultaneously. Furthermore, we enhance the expert-loss to exploit the smoothness property, and demonstrate that our algorithm can attain small-loss regret for multiple types of convex and smooth functions.

AAAI Conference 2023 Conference Paper

Distributed Projection-Free Online Learning for Smooth and Convex Losses

• Yibo Wang
• Yuanyu Wan
• Shimao Zhang
• Lijun Zhang

We investigate the problem of distributed online convex optimization with complicated constraints, in which the projection operation could be the computational bottleneck. To avoid projections, distributed online projection-free methods have been proposed and attain an O(T^{3/4}) regret bound for general convex losses. However, they cannot utilize the smoothness condition, which has been exploited in the centralized setting to improve the regret. In this paper, we propose a new distributed online projection-free method with a tighter regret bound of O(T^{2/3}) for smooth and convex losses. Specifically, we first provide a distributed extension of Follow-the-Perturbed-Leader so that the smoothness can be utilized in the distributed setting. Then, we reduce the computational cost via sampling and blocking techniques. In this way, our method only needs to solve one linear optimization per round on average. Finally, we conduct experiments on benchmark datasets to verify the effectiveness of our proposed method.

ICRA Conference 2023 Conference Paper

SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing

• Jianyuan Ruan
• Bo Li
• Yibo Wang
• Yuxiang Sun 0002

Most current LiDAR simultaneous localization and mapping (SLAM) systems build maps in point clouds, which are sparse when zoomed in, even though they seem dense to human eyes. Dense maps are essential for robotic applications, such as map-based navigation. Due to the low memory cost, mesh has become an attractive dense model for mapping in recent years. However, existing methods usually produce mesh maps by using an offline post-processing step to generate mesh maps. This two-step pipeline does not allow these methods to use the built mesh maps online and to enable localization and meshing to benefit each other. To solve this problem, we propose the first CPU-only real-time LiDAR SLAM system that can simultaneously build a mesh map and perform localization against the mesh map. A novel and direct meshing strategy with Gaussian process reconstruction realizes the fast building, registration, and updating of mesh maps. We perform experiments on several public datasets. The results show that our SLAM system can run at around 40Hz. The localization and meshing accuracy also outperforms the state-of-the-art methods, including the TSDF map and Poisson reconstruction. Our code and video demos are available at: https://github.com/lab-sun/SLAMesh.

JBHI Journal 2023 Journal Article

Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks

• Lan Shi
• Marianthie Wank
• Yan Chen
• Yibo Wang
• Yachuan Liu
• Emily C. Hector
• Peter X.K. Song

Objective: We propose a new analytic framework, “Artificial Synthetic Imaging Data (ASID) Workflow, ” for sleep classification from a wearable device comprising: 1) the creation of ASID from data collected by a non-invasive wearable device that permits real-time multi-modal physiological monitoring on heart rate (HR), 3-axis accelerometer, electrodermal activity, and skin temperature, denoted as “Temporal E4 Data” (TED) and 2) the use of an image classification supervised learning algorithm, convolutional neural network (CNN), to classify periods of sleep. Methods: We investigate ASID Workflow under 6 settings (3 data resolutions × 2 HR scenarios). Competing machine/deep learning classification algorithms, including logistic regression, support vector machine, random forest, k-nearest neighbors, and Long Short-Term Memory, are applied to TED as comparisons, termed “Competing Workflow. ” Results: The ASID Workflow achieves excellent performance with mean weighted accuracy across settings of 94. 7%, and is superior to the Competing Workflow with high and low resolution data regardless of the inclusion of HR modality. This superiority is maximized for low resolution data without HR. Additionally, CNN has a relatively low subject-wise test computational cost compared with competing algorithms. Conclusion: We demonstrate the utility of creating ASID from multi-modal physiological data and applying a preexisting image classification algorithm to achieve better classification accuracy. We shed light on the influence of data resolution and HR modality on the Workflow's performance. Significance: Applying CNN to ASID allows us to capture both temporal and spatial dependency among physiological variables and modalities by using 2D images' topological structure that competing algorithms fail to utilize.

NeurIPS Conference 2022 Conference Paper

Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization

• Wei Jiang
• Gang Li
• Yibo Wang
• Lijun Zhang
• Tianbao Yang

Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively studied to improve the convergence rates of stochastic non-convex optimization, which usually maintain and update a sequence of estimators for a single function across iterations. What if we need to track multiple functional mappings across iterations but only with access to stochastic samples of $\mathcal{O}(1)$ functional mappings at each iteration? There is an important application in solving an emerging family of coupled compositional optimization problems in the form of $\sum_{i=1}^m f_i(g_i(\mathbf{w}))$, where $g_i$ is accessible through a stochastic oracle. The key issue is to track and estimate a sequence of $\mathbf g(\mathbf{w})=(g_1(\mathbf{w}), \ldots, g_m(\mathbf{w}))$ across iterations, where $\mathbf g(\mathbf{w})$ has $m$ blocks and it is only allowed to probe $\mathcal{O}(1)$ blocks to attain their stochastic values and Jacobians. To improve the complexity for solving these problems, we propose a novel stochastic method named Multi-block-Single-probe Variance Reduced (MSVR) estimator to track the sequence of $\mathbf g(\mathbf{w})$. It is inspired by STORM but introduces a customized error correction term to alleviate the noise not only in stochastic samples for the selected blocks but also in those blocks that are not sampled. With the help of the MSVR estimator, we develop several algorithms for solving the aforementioned compositional problems with improved complexities across a spectrum of settings with non-convex/convex/strongly convex/Polyak-Lojasiewicz (PL) objectives. Our results improve upon prior ones in several aspects, including the order of sample complexities and dependence on the strong convexity parameter. Empirical studies on multi-task deep AUC maximization demonstrate the better performance of using the new estimator.