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Chao Gao

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45

AAAI Conference 2026 Conference Paper

FreeAskWorld: An Interactive and Closed-Loop Simulator for Human-Centric Embodied AI

  • Yuhang Peng
  • Yizhou Pan
  • Xinning He
  • Jihaoyu Yang
  • Xinyu Yin
  • Han Wang
  • Xiaoji Zheng
  • Chao Gao

As embodied intelligence emerges as a core frontier in artificial intelligence research, simulation platforms must evolve beyond low-level physical interactions to capture complex, human-centered social behaviors. We introduce FreeAskWorld, an interactive simulation framework that integrates large language models (LLMs) for high-level behavior planning and semantically grounded interaction, informed by theories of intention and social cognition. Our framework supports scalable, realistic human-agent simulations and includes a modular data generation pipeline tailored for diverse embodied tasks.To validate the framework, we extend the classic Vision-and-Language Navigation (VLN) task into a semantically enriched Direction Inquiry setting, wherein agents can actively seek and interpret navigational guidance. We present and publicly release FreeAskWorld, a large-scale benchmark dataset comprising reconstructed environments, six diverse task types, 16 core object categories, 63,429 annotated sample frames, and more than 17 hours of interaction data to support training and evaluation of embodied AI systems. We benchmark VLN models, and human participants under both open-loop and closed-loop settings. Experimental results demonstrate that models fine-tuned on FreeAskWorld outperform their original counterparts, achieving enhanced semantic understanding and interaction competency. These findings underscore the efficacy of socially grounded simulation frameworks in advancing embodied AI systems toward sophisticated high-level planning and more naturalistic human-agent interaction.

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AAAI Conference 2026 Conference Paper

Modeling Trend Dynamics with Variational Neural ODEs for Information Popularity Prediction

  • Yuchen Wang
  • Dongpeng Hou
  • Weikai Jing
  • Chao Gao
  • Xianghua Li
  • Yang Liu

Predicting the future popularity of information in online social networks is a crucial yet challenging task, due to the complex spatiotemporal dynamics underlying information diffusion. Existing methods typically use structural or sequential patterns within the observation window as direct inputs for subsequent popularity prediction. However, most approaches lack the ability to explicitly model the overall trend of popularity up to the prediction time, which leads to limited predictive capability. To address these limitations, we propose VNOIP, a novel method based on variational neural Ordinary Differential Equations (ODEs) for information popularity prediction. Specifically, VNOIP introduces bidirectional jump ODEs with attention mechanisms to capture long-range dependencies and bidirectional context within cascade sequences. Furthermore, by jointly considering both cascade patterns and overall trend temporal patterns, VNOIP explicitly models the continuous-time dynamics of popularity trend trajectories with variational neural ODEs. Additionally, a knowledge distillation loss is employed to align the evolution of prior and posterior latent variables. Extensive experiments on real-world datasets demonstrate that VNOIP is highly competitive in both prediction accuracy and efficiency compared to state-of-the-art baselines.

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IJCAI Conference 2025 Conference Paper

A Generalized Diffusion Framework with Learnable Propagation Dynamics for Source Localization

  • Dongpeng Hou
  • Yuchen Wang
  • Chao Gao
  • Xianghua Li

Source localization has been widely studied in recent years due to its crucial role in controlling the spread of harmful information. Existing methods only achieve satisfactory performance within a specific propagation model, which restricts their applicability and generalizability across different scenarios. To address this, we propose a Generalized Diffusion Framework for Source Localization (GDFSL), which enhances probabilistic diffusion models to flexibly capture the underlying dynamics of various propagation scenarios. By redefining the forward diffusion process, GDFSL ensures convergence to a real distribution of infected states that accurately represents the targeted dynamics, enabling the model to learn unbiased noise in a self-supervised manner that encodes fine-grained propagation characteristics. A closed-form reverse diffusion process is then derived to trace the propagation back to the source. The process does not rely on an explicit source label term, facilitating direct inference of sources from observed data. Experimental results show that GDFSL outperforms SOTA methods in various propagation models, particularly in scenarios where historical training data is limited or unavailable. The code is available at https: //github. com/cgao-comp/GDFSL.

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IJCAI Conference 2025 Conference Paper

A Prior-based Discrete Diffusion Model for Social Graph Generation

  • Shu Yin
  • Dongpeng Hou
  • Lianwei Wu
  • Xianghua Li
  • Chao Gao

Graph generation is essential in social network analysis, particularly for modeling information flow and user interactions. However, existing probabilistic diffusion models face challenges when applied to social propagation graphs. The continuous noise does not apply to the discrete nature of graph generation tasks, and the random Gaussian initialization in the reverse process can introduce biases that deviate from real-world propagation patterns. To address these issues, this paper introduces a Prior-based Discrete Diffusion Model (PDDM) for social graph generation. PDDM redefines the forward process as a discrete process for node denoising and edge generation, and the task of the denoising module is transformed into the connection probability learning of node-level tasks. Further, PDDM employs a new starting point of the reverse process by incorporating user similarity as the probability matrix, which can better leverage the social context. These developments mitigate reverse-starting bias and enhance model robustness. Moreover, PDDM integrates lightweight deep graph networks such as GAT, demonstrating both scalability and applicability to graph generation scenarios. Comprehensive experiments on real-world social network datasets demonstrate PDDM’s superiority in terms of the MMD metric and downstream tasks. The code is available at https: //github. com/cgao-comp/PDDM.

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IJCAI Conference 2025 Conference Paper

Can Retelling Have Adequate Information for Reasoning? An Enhancement Method for Imperfect Video Understanding with Large Language Model

  • Mingxin Li
  • Wenhao Wang
  • Hongru Ji
  • Xianghua Li
  • Chao Gao

Large Language Models (LLMs) demonstrate strong capabilities in video understanding. However, it exhibits hallucinations and factual errors in video description. On the one hand, existing Multimodal Large Language Models (MLLMs) are primarily trained by combining language models and vision models, with their visual understanding capabilities depending on the performance of the backbone. Moreover, video descriptions often suffer from incomplete content and the possibility of errors. Given the proven assessment of the strong reasoning capabilities of LLMs, this paper proposes ERSR, a novel Entity and Relationship based Self-Enhanced Reasoning method for imperfect video understanding. Specifically, an entities and relationships strategy is designed to perform scene graphs based on the limited observed entity relationships, thereby enhancing video descriptions. Furthermore, by providing question feedbacks, a self-enhanced forward and feedback reasoning strategy is provided to enhance reasoning logic. Finally, the prediction question answering results are re-validated through rethinking and verifying using the LLMs. Extensive experiments show that the proposed method achieves competitive results on real-world video understanding datasets, with an overall improvement of no less than 1. 4%.

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TMLR Journal 2025 Journal Article

ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control

  • Ehsan Futuhi
  • Shayan Karimi
  • Chao Gao
  • Martin Müller

We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards. To enhance exploration, we introduce a search procedure, \emph{${\epsilon}{t}$-greedy}, which generates exploratory options for exploring less-visited states. We prove that search using $\epsilon t$-greedy has polynomial sample complexity under mild MDP assumptions. To more efficiently use the information provided by rewarded transitions, we develop a new dual experience replay buffer framework, \emph{GDRB}, and implement \emph{longest n-step returns}. The resulting algorithm, \emph{ETGL-DDPG}, integrates all three techniques: \bm{$\epsilon t$}-greedy, \textbf{G}DRB, and \textbf{L}ongest $n$-step, into DDPG. We evaluate ETGL-DDPG on standard benchmarks and demonstrate that it outperforms DDPG, as well as other state-of-the-art methods, across all tested sparse-reward continuous environments. Ablation studies further highlight how each strategy individually enhances the performance of DDPG in this setting.

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IJCAI Conference 2025 Conference Paper

Good Advisor for Source Localization: Using Large Language Model to Guide the Source Inference Process

  • Dongpeng Hou
  • Wenfei Wei
  • Chao Gao
  • Xianghua Li
  • Zhen Wang

With the rapid development of AI large model technology, large language models (LLMs) provide a new solution for source localization tasks due to the deep linguistic understanding and generation capabilities. However, it is difficult to understand complex propagation patterns and network structures when LLMs are directly applied to source localization, resulting in limited accuracy of source localization. Meanwhile, the high-dimensional embedding of the textual representation introduces significant amounts of redundant features, which also reduces its efficiency in source localization task to some extent. To solve the above problems, this paper proposes a multi-modal fusion framework for rumor source localization, namely Contrastive Rumor Source Localization via LLM (CRSLL), based on the idea of contrastive learning. Specifically, the framework constructs propagation embeddings by comprehensively capturing both propagation dynamics and user profile features, adopts a contrastive learning approach to enhance the representation ability of comment embeddings of rumor cascades by differentiating them from non-rumor cascade comments, filters out invalid features through a differentiable masking strategy, and fuses comment modality embeddings with propagation embeddings through an attention mechanism, so as to better capture the multi-modal data interactions. It is worth mentioning that the framework uses LLM as a good ``advisor'' to provide a rich deep semantic representation, which improves the accuracy of rumor source localization. The code is available at https: //github. com/cgao-comp/CRSLL.

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NeurIPS Conference 2025 Conference Paper

HawkBench: Investigating Resilience of RAG Methods on Stratified Information-Seeking Tasks

  • Hongjin Qian
  • Zheng Liu
  • Chao Gao
  • Yankai Wang
  • Defu Lian
  • Zhicheng Dou

In real-world information-seeking scenarios, users have dynamic and diverse needs, requiring RAG systems to demonstrate adaptable resilience. To comprehensively evaluate the resilience of current RAG methods, we introduce HawkBench, a human-labeled, multi-domain benchmark designed to rigorously assess RAG performance across categorized task types. By stratifying tasks based on information-seeking behaviors, HawkBench provides a systematic evaluation of how well RAG systems adapt to diverse user needs. Unlike existing benchmarks, which focus primarily on specific task types (mostly factoid queries) and rely on varying knowledge bases, HawkBench offers: (1) systematic task stratification to cover a broad range of query types, including both factoid and rationale queries, (2) integration of multi-domain corpora across all task types to mitigate corpus bias, and (3) rigorous annotation for high-quality evaluation. HawkBench includes 1, 600 high-quality test samples, evenly distributed across domains and task types. Using this benchmark, we evaluate representative RAG methods, analyzing their performance in terms of answer quality and response latency. Our findings highlight the need for dynamic task strategies that integrate decision-making, query interpretation, and global knowledge understanding to improve RAG generalizability. We believe HawkBench serves as a pivotal benchmark for advancing the resilience of RAG methods and their ability to achieve general-purpose information seeking.

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IJCAI Conference 2025 Conference Paper

Hybrid Relational Graphs with Sentiment-laden Semantic Alignment for Multimodal Emotion Recognition in Conversation

  • Hongru Ji
  • Xianghua Li
  • Mingxin Li
  • Meng Zhao
  • Chao Gao

Multimodal Emotion Recognition in Conversation (MERC) focuses on detecting the emotions expressed by speakers in each utterance. Recent research has increasingly leveraged graph-based models to capture interactive relationships in conversations, enhancing the ability to extract emotional cues. However, existing methods primarily focus on explicit utterance-level relationships, neglecting both the implicit connections within individual modality and the differences in implicit relationships across modalities. Moreover, these methods often overlook the role of sentimental features in conversation history in cross-modal semantic alignment. To address these issues, we propose a novel model that employs modality-adaptive hybrid relational graphs to enrich the dialogue graph by inferring implicit relationships between nodes within each modality. Furthermore, we introduce historical sentiment through a progressive strategy that utilizes contrastive learning to refine cross-modal semantic alignment. Experimental results demonstrate the superior performance of our approach over state-of-the-art methods on the IEMOCAP and MELD datasets. Our code is available at https: //github. com/cgao-comp/HRG-SSA.

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IJCAI Conference 2025 Conference Paper

HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion

  • Le Cheng
  • Peican Zhu
  • Yangming Guo
  • Keke Tang
  • Chao Gao
  • Zhen Wang

Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination

  • Ilias Diakonikolas
  • Chao Gao
  • Daniel Kane
  • John Lafferty
  • Ankit Pensia

We study the task of noiseless linear regression under Gaussian covariates in the presence of additive oblivious contamination. Specifically, we are given i. i. d. \ samples from a distribution $(x, y)$ on $\mathbb R^d \times \mathbb R$ with $x \sim \mathcal N(0, I_d)$ and $y = x^\top \beta + z$, where $z$ is drawn from an unknown distribution that is independent of $x$. Moreover, $z$ satisfies $\mathbb P[z = 0] = \alpha>0$. The goal is to accurately recover the regressor $\beta$ to small $\ell_2$-error. Ignoring computational considerations, this problem is known to be solvable using $O(d/\alpha)$ samples. On the other hand, the best known polynomial-time algorithms require $\Omega(d/\alpha^2)$ samples. Here we provide formal evidence that the quadratic dependence in $1/\alpha$ is inherent for efficient algorithms. Specifically, we show that any efficient Statistical Query algorithm for this task requires VSTAT complexity at least $\tilde{\Omega}(d^{1/2}/\alpha^2)$.

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AAAI Conference 2025 Conference Paper

Learning Complex Heterogeneous Multimodal Fake News via Social Latent Network Inference

  • Mingxin Li
  • Yuchen Zhang
  • Haowei Xu
  • Xianghua Li
  • Chao Gao
  • Zhen Wang

With the diversification of online social platforms, news dissemination has become increasingly complex, heterogeneous, and multimodal, making the fake news detection task more challenging and crucial. Previous works mainly focus on obtaining social relationships of news via retweets, limiting the accurate detection when real cascades are inaccessible. Given the proven assessment of the spreading influence of events, this paper proposes a method called HML (Complex Heterogeneous Multimodal Fake News Detection method via Latent Network Inference). Specifically, an improved social latent network inference strategy is designed to estimate the maximum likelihood of news influences under the same event. Meanwhile, a novel heterogeneous graph is built based on social attributes for multimodal news under different events. Further, to better aggregate the relationships among heterogeneous multimodal features, this paper proposes a self-supervised-based multimodal content learning strategy, to enhance, align, fuse and compare heterogeneous modal contents. Based above, a personalized heterogeneous graph representation learning is designed to classify fake news. Extensive experiments demonstrate that the proposed method outperforms the SOTA in real social media news datasets.

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IJCAI Conference 2025 Conference Paper

Learning Neural Jump Stochastic Differential Equations with Latent Graph for Multivariate Temporal Point Processes

  • Yuchen Wang
  • Dongpeng Hou
  • Chao Gao
  • Xianghua Li

Multivariate Temporal Point Processes (MTPPs) play an important role in diverse domains such as social networks and finance for predicting event sequence data. In recent years, MTPPs based on Ordinary Differential Equations (ODEs) and Stochastic Differential Equations (SDEs) have demonstrated their strong modeling capabilities. However, these models have yet to thoroughly consider the underlying relationships among different event types to enhance their modeling capacity. Therefore, this paper introduces a method that uses neural SDEs with a jump process guided by the latent graph. Firstly, our proposed method employs multi-dimensional SDEs to capture the dynamics of the intensity function for each event type. Subsequently, a latent graph structure is integrated into the jump process without any encoder, aiming to enhance the modeling and predictive capabilities for MTPPs. Theoretical analysis guarantees the existence and uniqueness of the solution for our proposed method. The experiments conducted on multiple real-world datasets show that our approaches demonstrate significant competitiveness when compared to state-of-the-art neural point processes. Meanwhile, the trainable parameters of the latent graph also improve the model interpretability without any prior knowledge. Our code is available at https: //github. com/cgao-comp/LNJSDE.

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ICRA Conference 2025 Conference Paper

OpenBench: A New Benchmark and Baseline for Semantic Navigation in Smart Logistics

  • Junhui Wang
  • Dongjie Huo
  • Zehui Xu
  • Yongliang Shi
  • Yimin Yan
  • Yuanxin Wang
  • Chao Gao
  • Yan Qiao 0004

The increasing demand for efficient last-mile delivery in smart logistics underscores the role of autonomous robots in enhancing operational efficiency and reducing costs. Traditional navigation methods, which depend on highprecision maps, are resource-intensive, while learning-based approaches often struggle with generalization in real-world scenarios. To address these challenges, this work proposes the Openstreetmap-enhanced oPen-air sEmantic Navigation (OPEN) system that combines foundation models with classic algorithms for scalable outdoor navigation. The system uses off-the-shelf OpenStreetMap (OSM) for flexible map representation, thereby eliminating the need for extensive pre-mapping efforts. It also employs Large Language Models (LLMs) to comprehend delivery instructions and Vision-Language Models (VLMs) for global localization, map updates, and house number recognition. To compensate the limitations of existing benchmarks that are inadequate for assessing last-mile delivery, this work introduces a new benchmark specifically designed for outdoor navigation in residential areas, reflecting the real-world challenges faced by autonomous delivery systems. Extensive experiments in simulated and real-world environments demonstrate the proposed system's efficacy in enhancing navigation efficiency and reliability. To facilitate further research, our code and benchmark are publicly available 1 1 https://ei-nav.github.io/OpenBench/.

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NeurIPS Conference 2025 Conference Paper

Re-ttention: Ultra Sparse Visual Generation via Attention Statistical Reshape

  • Ruichen Chen
  • Keith Mills
  • Liyao Jiang
  • Chao Gao
  • Di Niu

Diffusion Transformers (DiT) have become the de-facto model for generating high-quality visual content like videos and images. A huge bottleneck is the attention mechanism where complexity scales quadratically with resolution and video length. One logical way to lessen this burden is sparse attention, where only a subset of tokens or patches are included in the calculation. However, existing techniques fail to preserve visual quality at extremely high sparsity levels and might even incur non-negligible compute overheads. To address this concern, we propose Re-ttention, which implements very high sparse attention for visual generation models by leveraging the temporal redundancy of Diffusion Models to overcome the probabilistic normalization shift within the attention mechanism. Specifically, Re-ttention reshapes attention scores based on the prior softmax distribution history in order to preserve the visual quality of the full quadratic attention at very high sparsity levels. Experimental results on T2V/T2I models such as CogVideoX and the PixArt DiTs demonstrate that Re-ttention requires as few as 3. 1% of the tokens during inference, outperforming contemporary methods like FastDiTAttn, Sparse VideoGen and MInference.

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IJCAI Conference 2025 Conference Paper

SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs

  • Le Cheng
  • Peican Zhu
  • Yangming Guo
  • Chao Gao
  • Zhen Wang
  • Keke Tang

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

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ICML Conference 2025 Conference Paper

Volume Optimality in Conformal Prediction with Structured Prediction Sets

  • Chao Gao
  • Liren Shan
  • Vaidehi Srinivas
  • Aravindan Vijayaraghavan

Conformal Prediction is a widely studied technique to construct prediction sets of future observations. Most conformal prediction methods focus on achieving the necessary coverage guarantees, but do not provide formal guarantees on the size (volume) of the prediction sets. We first prove the impossibility of volume optimality where any distribution-free method can only find a trivial solution. We then introduce a new notion of volume optimality by restricting the prediction sets to belong to a set family (of finite VC-dimension), specifically a union of $k$-intervals. Our main contribution is an efficient distribution-free algorithm based on dynamic programming (DP) to find a union of $k$-intervals that is guaranteed for any distribution to have near-optimal volume among all unions of $k$-intervals satisfying the desired coverage property. By adopting the framework of distributional conformal prediction (Chernozhukov et al. , 2021), the new DP based conformity score can also be applied to achieve approximate conditional coverage and conditional restricted volume optimality, as long as a reasonable estimator of the conditional CDF is available. While the theoretical results already establish volume-optimality guarantees, they are complemented by experiments that demonstrate that our method can significantly outperform existing methods in many settings.

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AAMAS Conference 2025 Conference Paper

β-DQN: Improving Deep Q-Learning By Evolving the Behavior

  • Hongming Zhang
  • Fengshuo Bai
  • Chenjun Xiao
  • Chao Gao
  • Bo Xu
  • Martin Müller

While many sophisticated exploration methods have been proposed, their lack of generality and high computational cost often lead researchers to favor simpler methods like 𝜖-greedy. Motivated by this, we introduce 𝛽-DQN, a simple and efficient exploration method that augments the standard DQN with a behavior function 𝛽. This function estimates the probability that each action has been taken at each state. By leveraging 𝛽, we generate a population of diverse policies that balance exploration between state-action coverage and overestimation bias correction. An adaptive meta-controller is designed to select an effective policy for each episode, enabling flexible and explainable exploration. 𝛽-DQN is straightforward to implement and adds minimal computational overhead to the standard DQN. Experiments on both simple and challenging exploration domains show that 𝛽-DQN outperforms existing baseline methods across a wide range of tasks, providing an effective solution for improving exploration in deep reinforcement learning.

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EAAI Journal 2024 Journal Article

Consensus Subspace Graph Regularization based on prior information for multiplex network clustering

  • Xianghua Li
  • Shu Yin
  • Xingjian Liu
  • Chao Gao
  • Zhen Wang
  • Vladimir I. Nekorkin

Multiplex network clustering can identify the common cluster structure shared by all layers, which is of great significance for downstream research such as personalized recommendations of social relationships and the mining of social media communication behaviors. Traditional multiplex network clustering methods rely only on the topological structure and are suitable for networks with clear structures. However, real-world networks often suffer from sparse connectivity and noisy edges. In addition, the current methods based on prior information only use the obtained consensus prior information as a preprocessing method for multiplex networks, and do not fully utilize and integrate the prior information in the clustering process, resulting in a low accuracy. To solve the problem of insufficient utilization of prior information, we propose a Consensus Subspace Graph Regularization (CSGR) approach for multiplex network clustering, which integrates topological information with the consensus prior information of a network. We first construct the consensus prior matrix of a network by a non-overlapping greedy search method, which represents a subset of the network that consists of nodes with high edge density. Then we construct a graph regularization term, which encodes the consensus prior information, to optimize the generation of a joint low-dimensional representation within the consensus subspace for the entire multiplex network. Finally, we employ the consensus prior matrix to denoise each network layer, thereby yielding a precise low-dimensional representation of each layer for consensus subspace fusion. In addition, comprehensive experiments have demonstrated that CSGR improves the clustering accuracy of real-world networks by an average of 1. 56%.

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AAAI Conference 2024 Conference Paper

DAG-Aware Variational Autoencoder for Social Propagation Graph Generation

  • Dongpeng Hou
  • Chao Gao
  • Xuelong Li
  • Zhen Wang

Propagation models in social networks are critical, with extensive applications across various fields and downstream tasks. However, existing propagation models are often oversimplified, scenario-specific, and lack real-world user social attributes. These limitations detaching from real-world analysis lead to inaccurate representations of the propagation process in social networks. To address these issues, we propose a User Features Attention-based DAG-Aware Variational Autoencoder (DAVA) for propagation graph generation. First, nearly 1 million pieces of user attributes data are collected. Then DAVA can integrate the analysis of propagation graph topology and corresponding user attributes as prior knowledge. By leveraging a lightweight attention-based framework and a sliding window mechanism based on BFS permutations weighted by user influence, DAVA significantly enhances the ability to generate realistic, large-scale propagation data, yielding graph scales ten times greater than those produced by existing SOTA methods. Every module of DAVA has flexibility and extension that allows for easy substitution to suit other generation tasks. Additionally, we provide a comprehensive evaluation of DAVA, one focus is the effectiveness of generated data in improving the performance of downstream tasks. During the generation process, we discover the Credibility Erosion Effect by modifying the generation rules, revealing a social phenomenon in social network propagation.

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NeurIPS Conference 2024 Conference Paper

Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration

  • Hongming Zhang
  • Chenjun Xiao
  • Chao Gao
  • Han Wang
  • Bo Xu
  • Martin Müller

Reinforcement learning (RL) algorithms are typically based on optimizing a Markov Decision Process (MDP) using the optimal Bellman equation. Recent studies have revealed that focusing the optimization of Bellman equations solely on in-sample actions tends to result in more stable optimization, especially in the presence of function approximation. Upon on these findings, in this paper, we propose an Empirical MDP Iteration (EMIT) framework. EMIT constructs a sequence of empirical MDPs using data from the growing replay memory. For each of these empirical MDPs, it learns an estimated Q-function denoted as $\widehat{Q}$. The key strength is that by restricting the Bellman update to in-sample bootstrapping, each empirical MDP converges to a unique optimal $\widehat{Q}$ function. Furthermore, gradually expanding from the empirical MDPs to the original MDP induces a monotonic policy improvement. Instead of creating entirely new algorithms, we demonstrate that EMIT can be seamlessly integrated with existing online RL algorithms, effectively acting as a regularizer for contemporary Q-learning methods. We show this by implementing EMIT for two representative RL algorithms, DQN and TD3. Experimental results on Atari and MuJoCo benchmarks show that EMIT significantly reduces estimation errors and substantially improves the performance of both algorithms.

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AAAI Conference 2024 Conference Paper

GAMC: An Unsupervised Method for Fake News Detection Using Graph Autoencoder with Masking

  • Shu Yin
  • Peican Zhu
  • Lianwei Wu
  • Chao Gao
  • Zhen Wang

With the rise of social media, the spread of fake news has become a significant concern, potentially misleading public perceptions and impacting social stability. Although deep learning methods like CNNs, RNNs, and Transformer-based models like BERT have enhanced fake news detection. However, they primarily focus on content and do not consider social context during news propagation. Graph-based techniques have incorporated the social context but are limited by the need for large labeled datasets. To address these challenges, this paper introduces GAMC, an unsupervised fake news detection technique using the Graph Autoencoder with Masking and Contrastive learning. By leveraging both the context and content of news propagation as self-supervised signals, our method reduces the dependency on labeled datasets. Specifically, GAMC begins by applying data augmentation to the original news propagation graphs. Subsequently, these augmented graphs are encoded using a graph encoder and subsequently reconstructed via a graph decoder. Finally, a composite loss function that encompasses both reconstruction error and contrastive loss is designed. Firstly, it ensures the model can effectively capture the latent features, based on minimizing the discrepancy between reconstructed and original graph representations. Secondly, it aligns the representations of augmented graphs that originate from the same source. Experiments on the real-world dataset validate the effectiveness of our method.

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AAAI Conference 2024 Conference Paper

GIN-SD: Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion

  • Le Cheng
  • Peican Zhu
  • Keke Tang
  • Chao Gao
  • Zhen Wang

Source detection in graphs has demonstrated robust efficacy in the domain of rumor source identification. Although recent solutions have enhanced performance by leveraging deep neural networks, they often require complete user data. In this paper, we address a more challenging task, rumor source detection with incomplete user data, and propose a novel framework, i.e., Source Detection in Graphs with Incomplete Nodes via Positional Encoding and Attentive Fusion (GIN-SD), to tackle this challenge. Specifically, our approach utilizes a positional embedding module to distinguish nodes that are incomplete and employs a self-attention mechanism to focus on nodes with greater information transmission capacity. To mitigate the prediction bias caused by the significant disparity between the numbers of source and non-source nodes, we also introduce a class-balancing mechanism. Extensive experiments validate the effectiveness of GIN-SD and its superiority to state-of-the-art methods.

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IJCAI Conference 2024 Conference Paper

Joint Source Localization in Different Platforms via Implicit Propagation Characteristics of Similar Topics

  • Zhen Wang
  • Dongpeng Hou
  • Shu Yin
  • Chao Gao
  • Xianghua Li

Different social media are widely used in our daily lives. Inspired by the fact that similar topics have similar propagation characteristics, we mine the implicit knowledge of cascades with similar topics from different platforms to enhance the localization performance for scenarios where limited propagation data leads to the weak learning ability of existing localization models. In this work, we first construct a multiple platform propagation cascade dataset, aligning similar topics from both Twitter and Weibo, and enriching it with user profiles. Leveraging this dataset, we propose a Dual-channel Source Localization Framework (DSLF) for the joint cascades with similar topics. Specifically, a self-loop attention based graph convolutional network is designed to adaptively adjust the neighborhood aggregation scheme of different users with heterogeneous features in the message-passing process. Additionally, a dual-structure based Kullback-Leibler (KL) regularization module is proposed to constrain the latent distribution space of the source probabilities of similar characteristic-level users for a similar topic, enhancing the robustness of the model. Extensive experiments across Twitter and Weibo platforms demonstrate the superiority of the proposed DSLF over the SOTA methods. The code is available at https: //github. com/cgao-comp/DSLF.

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AAAI Conference 2024 Conference Paper

Monte Carlo Tree Search in the Presence of Transition Uncertainty

  • Farnaz Kohankhaki
  • Kiarash Aghakasiri
  • Hongming Zhang
  • Ting-Han Wei
  • Chao Gao
  • Martin Müller

Monte Carlo Tree Search (MCTS) is an immensely popular search-based framework used for decision making. It is traditionally applied to domains where a perfect simulation model of the environment is available. We study and improve MCTS in the context where the environment model is given but imperfect. We show that the discrepancy between the model and the actual environment can lead to significant performance degradation with standard MCTS. We therefore develop Uncertainty Adapted MCTS (UA-MCTS), a more robust algorithm within the MCTS framework. We estimate the transition uncertainty in the given model, and direct the search towards more certain transitions in the state space. We modify all four MCTS phases to improve the search behavior by considering these estimates. We prove, in the corrupted bandit case, that adding uncertainty information to adapt UCB leads to tighter regret bound than standard UCB. Empirically, we evaluate UA-MCTS and its individual components on the deterministic domains from the MinAtar test suite. Our results demonstrate that UA-MCTS strongly improves MCTS in the presence of model transition errors.

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TMLR Journal 2024 Journal Article

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

  • Zeyu Han
  • Chao Gao
  • Jinyang Liu
  • Jeff Zhang
  • Sai Qian Zhang

Large models represent a groundbreaking advancement in multiple application fields, enabling remarkable achievements across various tasks. However, their unprecedented scale comes with significant computational costs. These models, often consisting of billions of parameters, require vast amounts of computational resources for execution. Especially, the expansive scale and computational demands pose considerable challenges when customizing them for particular downstream tasks, particularly over the hardware platforms constrained by computational capabilities. Parameter Efficient Fine-Tuning (PEFT) provides a practical solution by efficiently adjusting the large models over the various downstream tasks. In particular, PEFT refers to the process of adjusting the parameters of a pre-trained large model to adapt it to a specific task or domain while minimizing the number of additional parameters introduced or computational resources required. This approach is particularly important when dealing with large-scale language models with high parameter counts, as fine-tuning these models from scratch can be computationally expensive and resource-intensive, posing considerable challenges in the supporting system platform design. In this survey, we present comprehensive studies of various PEFT algorithms, examining their performance and computational overhead. Moreover, we provide an overview of applications developed using different PEFT algorithms and discuss common techniques employed to mitigate PEFT computation costs. In addition to providing an extensive survey from an algorithmic standpoint, we also examine various real-world system designs to investigate the implementation costs associated with different PEFT approaches. This survey serves as a valuable resource for researchers aiming to understand both the PEFT algorithm and its system implementation, offering detailed insights into recent advancements and practical applications.

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NeurIPS Conference 2024 Conference Paper

ResAD: A Simple Framework for Class Generalizable Anomaly Detection

  • Xincheng Yao
  • Zixin CHEN
  • Chao Gao
  • Guangtao Zhai
  • Chongyang Zhang

This paper explores the problem of class-generalizable anomaly detection, where the objective is to train one unified AD model that can generalize to detect anomalies in diverse classes from different domains without any retraining or fine-tuning on the target data. Because normal feature representations vary significantly across classes, this will cause the widely studied one-for-one AD models to be poorly classgeneralizable (i. e. , performance drops dramatically when used for new classes). In this work, we propose a simple but effective framework (called ResAD) that can be directly applied to detect anomalies in new classes. Our main insight is to learn the residual feature distribution rather than the initial feature distribution. In this way, we can significantly reduce feature variations. Even in new classes, the distribution of normal residual features would not remarkably shift from the learned distribution. Therefore, the learned model can be directly adapted to new classes. ResAD consists of three components: (1) a Feature Converter that converts initial features into residual features; (2) a simple and shallow Feature Constraintor that constrains normal residual features into a spatial hypersphere for further reducing feature variations and maintaining consistency in feature scales among different classes; (3) a Feature Distribution Estimator that estimates the normal residual feature distribution, anomalies can be recognized as out-of-distribution. Despite the simplicity, ResAD can achieve remarkable anomaly detection results when directly used in new classes. The code is available at https: //github. com/xcyao00/ResAD.

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IJCAI Conference 2023 Conference Paper

Sequential Attention Source Identification Based on Feature Representation

  • Dongpeng Hou
  • Zhen Wang
  • Chao Gao
  • Xuelong Li

Snapshot observation based source localization has been widely studied due to its accessibility and low cost. However, the interaction of users in existing methods does not be addressed in time-varying infection scenarios. So these methods have a decreased accuracy in heterogeneous interaction scenarios. To solve this critical issue, this paper proposes a sequence-to-sequence based localization framework called Temporal-sequence based Graph Attention Source Identification (TGASI) based on an inductive learning idea. More specifically, the encoder focuses on generating multiple features by estimating the influence probability between two users, and the decoder distinguishes the importance of prediction sources in different timestamps by a designed temporal attention mechanism. It's worth mentioning that the inductive learning idea ensures that TGASI can detect the sources in new scenarios without knowing other prior knowledge, which proves the scalability of TGASI. Comprehensive experiments with the SOTA methods demonstrate the higher detection performance and scalability in different scenarios of TGASI.

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SoCS Conference 2022 Conference Paper

A Memory-Bounded Best-First Beam Search and Its Application to Scheduling Halide Programs

  • Chao Gao
  • Jingwei Chen
  • Tong Mo
  • Tanvir Sajed
  • Shangling Jui
  • Min Qin
  • Laiyuan Gong
  • Wei Lu 0023

Beam search is a popular algorithm for solving real-world problems --- especially where search space is an enormously large tree but real-time solutions are most preferred. We present a memory-bounded best-first beam search (MB2FBS), which can be viewed as an improved and generalized version of standard beam search in trees. The algorithm takes three parameters --- in contrast to the singular parameter beam size in standard beam search. We discuss how to recover standard beam search and how to realize other search behavior by setting these three parameters correspondingly. In particular, we show that the principal version of MB2FBS can be thought as an algorithm whose search expense is similar or upper bounded by beam search of certain beam size; however it often finds better solutions as it decides the number of nodes to be searched each depth dynamically with respect cost landscape. We apply our algorithm for tensor program auto-scheduling in Halide, an important industrial problem that uses tree search for optimizing tensor program executions. We show that the principal variants of MB2FBS deliver better empirical results than the highly optimized beam search counterpart. Most importantly, it finds superior schedules while no more computation cost is used for search, which is highly desirable for real-time program compilation and optimization.

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AAAI Conference 2022 Conference Paper

Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability

  • Yafei Wang
  • Bo Pan
  • Wei Tu
  • Peng Liu
  • Bei Jiang
  • Chao Gao
  • Wei Lu
  • Shangling Jui

Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with independent training samples. However, these guarantees are not known to hold generally with dependent samples, such as in online learning with time series data or distributed computing with Markovian training samples. In this paper, we show that SAA remains tractable when the distribution of unknown parameters is only observable through dependent instances and still enjoys asymptotic consistency and finite sample guarantees. Specifically, we provide a rigorous probability error analysis to derive 1 - beta confidence bounds for the out-of-sample performance of SAA estimators and show that these estimators are asymptotically consistent. We then, using monotone operator theory, study the performance of a class of stochastic first-order algorithms trained on a dependent source of data. We show that approximation error for these algorithms is bounded and concentrates around zero, and establish deviation bounds for iterates when the underlying stochastic process is phi-mixing. The algorithms presented can be used to handle numerically inconvenient loss functions such as the sum of a smooth and non-smooth function or of non-smooth functions with constraints. To illustrate the usefulness of our results, we present several stochastic versions of popular algorithms such as stochastic proximal gradient descent (S-PGD), stochastic relaxed Peaceman– Rachford splitting algorithms (S-rPRS), and numerical experiment.

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JBHI Journal 2021 Journal Article

Medication Combination Prediction Using Temporal Attention Mechanism and Simple Graph Convolution

  • Haiqiang Wang
  • Yinying Wu
  • Chao Gao
  • Yue Deng
  • Fan Zhang
  • Jiajin Huang
  • Jiming Liu

Medication combination prediction can be applied to the clinical treatment for critical patients with multi-morbidity. The suitable medication combination can help cure patients and keep the treatment medication safe. However, the complexity and uncertainty of clinical circumstances limit the predictive accuracy of medication combination. Thus, this paper proposes a new medication combination prediction model based on the temporal attention mechanism (TAM) and the simple graph convolution (SGC), named as TAMSGC. More specifically, the TAM can capture the temporal sequence information in the medical records, and the SGC is implemented to acquire the medication knowledge from the complicated medication combination. Experiments in a real dataset show that TAMSGC surpasses the baseline models on the predictive accuracy of medication combination.

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JMLR Journal 2021 Journal Article

Mixing Time of Metropolis-Hastings for Bayesian Community Detection

  • Bumeng Zhuo
  • Chao Gao

We study the computational complexity of a Metropolis-Hastings algorithm for Bayesian community detection. We first establish a posterior strong consistency result for a natural prior distribution on stochastic block models under the optimal signal-to-noise ratio condition in the literature. We then give a set of conditions that guarantee rapidly mixing of a simple Metropolis-Hastings algorithm. The mixing time analysis is based on a careful study of posterior ratios and a canonical path argument to control the spectral gap of the Markov chain. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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ICML Conference 2021 Conference Paper

SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II

  • Xiangjun Wang
  • Junxiao Song
  • Penghui Qi
  • Peng Peng
  • Zhenkun Tang
  • Wei Zhang
  • Weimin Li
  • Xiongjun Pi

AlphaStar, the AI that reaches GrandMaster level in StarCraft II, is a remarkable milestone demonstrating what deep reinforcement learning can achieve in complex Real-Time Strategy (RTS) games. However, the complexities of the game, algorithms and systems, and especially the tremendous amount of computation needed are big obstacles for the community to conduct further research in this direction. We propose a deep reinforcement learning agent, StarCraft Commander (SCC). With order of magnitude less computation, it demonstrates top human performance defeating GrandMaster players in test matches and top professional players in a live event. Moreover, it shows strong robustness to various human strategies and discovers novel strategies unseen from human plays. In this paper, we’ll share the key insights and optimizations on efficient imitation learning and reinforcement learning for StarCraft II full game.

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JMLR Journal 2020 Journal Article

Bayesian Model Selection with Graph Structured Sparsity

  • Youngseok Kim
  • Chao Gao

We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of effective resistance, we derive an EM-type algorithm with closed-form iterations to efficiently explore possible candidates for Bayesian model selection. The deterministic nature of the proposed algorithm makes it more scalable to large-scale and high-dimensional data sets compared with existing stochastic search algorithms. When applied to sparse linear regression, our framework recovers the EMVS algorithm by Ročková and George (2014) as a special case. We also discuss extensions of our framework using tools from graph algebra to incorporate complex Bayesian models such as biclustering and submatrix localization. Extensive simulation studies and real data applications are conducted to demonstrate the superior performance of our methods over its frequentist competitors such as $\ell_0$ or $\ell_1$ penalization. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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JMLR Journal 2020 Journal Article

Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective

  • Chao Gao
  • Yuan Yao
  • Weizhi Zhu

Robust covariance matrix estimation is a fundamental task in statistics. The recent discovery on the connection between robust estimation and generative adversarial nets (GANs) suggests that it is possible to compute depth-like robust estimators using similar techniques that optimize GANs. In this paper, we introduce a general learning via classification framework based on the notion of proper scoring rules. This framework allows us to understand both matrix depth function, a technique of rate-optimal robust estimation, and various GANs through the lens of variational approximations of $f$-divergences induced by proper scoring rules. We then propose a new class of robust covariance matrix estimators in this framework by carefully constructing discriminators with appropriate neural network structures. These estimators are proved to achieve the minimax rate of covariance matrix estimation under Huber's contamination model. The results are also extended to robust scatter estimation for elliptical distributions. Our numerical results demonstrate the good performance of the proposed procedures under various settings against competitors in the literature. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2020. ( edit, beta )

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RLDM Conference 2019 Conference Abstract

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

  • Chao Gao
  • Pablo Hernandez-Leal
  • Bilal Kartal

The Pommerman Team Environment is a recently proposed benchmark which involves a multi- agent domain with challenges such as partial observability, decentralized execution (without communica- tion), and very sparse and delayed rewards. The inaugural Pommerman Team Competition held at NeurIPS 2018 hosted 25 participants who submitted a team of 2 agents. Our submission nn team skynet955 skynet955 won 2nd place of the learning agents category. Our team is composed of 2 neural networks trained with state of the art deep reinforcement learning algorithms and makes use of concepts like reward shaping, curricu- lum learning, and an automatic reasoning module for action pruning. Here, we describe these elements and additionally we present a collection of open-sourced agents that can be used for training and testing in the Pommerman environment. Relevant code available at: https: //github. com/BorealisAI/ pommerman-baseline

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JMLR Journal 2019 Journal Article

Stochastic Canonical Correlation Analysis

  • Chao Gao
  • Dan Garber
  • Nathan Srebro
  • Jialei Wang
  • Weiran Wang

We study the sample complexity of canonical correlation analysis (CCA), i.e., the number of samples needed to estimate the population canonical correlation and directions up to arbitrarily small error. With mild assumptions on the data distribution, we show that in order to achieve $\epsilon$-suboptimality in a properly defined measure of alignment between the estimated canonical directions and the population solution, we can solve the empirical objective exactly with $N(\epsilon, \Delta, \gamma)$ samples, where $\Delta$ is the singular value gap of the whitened cross-covariance matrix and $1/\gamma$ is an upper bound of the condition number of auto-covariance matrices. Moreover, we can achieve the same learning accuracy by drawing the same level of samples and solving the empirical objective approximately with a stochastic optimization algorithm; this algorithm is based on the shift-and-invert power iterations and only needs to process the dataset for $\mathcal{O} \left(\log \frac{1}{\epsilon} \right)$ passes. Finally, we show that, given an estimate of the canonical correlation, the streaming version of the shift-and-invert power iterations achieves the same learning accuracy with the same level of sample complexity, by processing the data only once. [abs] [ pdf ][ bib ] &copy JMLR 2019. ( edit, beta )

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JMLR Journal 2018 Journal Article

Goodness-of-Fit Tests for Random Partitions via Symmetric Polynomials

  • Chao Gao

We consider goodness-of-fit tests with i.i.d. samples generated from a categorical distribution $(p_1,...,p_k)$. For a given $(q_1,...,q_k)$, we test the null hypothesis whether $p_j=q_{\pi(j)}$ for some label permutation $\pi$. The uncertainty of label permutation implies that the null hypothesis is composite instead of being singular. In this paper, we construct a testing procedure using statistics that are defined as indefinite integrals of some symmetric polynomials. This method is aimed directly at the invariance of the problem, and avoids the need of matching the unknown labels. The asymptotic distribution of the testing statistic is shown to be chi-squared, and its power is proved to be nearly optimal under a local alternative hypothesis. Various degenerate structures of the null hypothesis are carefully analyzed in the paper. A two-sample version of the test is also studied. [abs] [ pdf ][ bib ] &copy JMLR 2018. ( edit, beta )

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IJCAI Conference 2018 Conference Paper

Three-Head Neural Network Architecture for Monte Carlo Tree Search

  • Chao Gao
  • Martin Müller
  • Ryan Hayward

AlphaGo Zero pioneered the concept of two-head neural networks in Monte Carlo Tree Search (MCTS), where the policy output is used for prior action probability and the state-value estimate is used for leaf node evaluation. We propose a three-head neural net architecture with policy, state- and action-value outputs, which could lead to more efficient MCTS since neural leaf estimate can still be back-propagated in tree with delayed node expansion and evaluation. To effectively train the newly introduced action-value head on the same game dataset as for two-head nets, we exploit the optimal relations between parent and children nodes for data augmentation and regularization. In our experiments for the game of Hex, the action-value head learning achieves similar error as the state-value prediction of a two-head architecture. The resulting neural net models are then combined with the same Policy Value MCTS (PV-MCTS) implementation. We show that, due to more efficient use of neural net evaluations, PV-MCTS with three-head neural nets consistently performs better than the two-head ones, significantly outplaying the state-of-the-art player MoHex-CNN.

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JMLR Journal 2017 Journal Article

Achieving Optimal Misclassification Proportion in Stochastic Block Models

  • Chao Gao
  • Zongming Ma
  • Anderson Y. Zhang
  • Harrison H. Zhou

Community detection is a fundamental statistical problem in network data analysis. In this paper, we present a polynomial time two-stage method that provably achieves optimal statistical performance in misclassification proportion for stochastic block model under weak regularity conditions. Our two-stage procedure consists of a refinement stage motivated by penalized local maximum likelihood estimation. This stage can take a wide range of weakly consistent community detection procedures as its initializer, to which it applies and outputs a community assignment that achieves optimal misclassification proportion with high probability. The theoretical property is confirmed by simulated examples. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

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IJCAI Conference 2017 Conference Paper

Focused Depth-first Proof Number Search using Convolutional Neural Networks for the Game of Hex

  • Chao Gao
  • Martin Müller
  • Ryan Hayward

Proof Number search (PNS) is an effective algorithm for searching theoretical values on games with non-uniform branching factors. Focused depth-first proof number search (FDFPN) with dynamic widening was proposed for Hex where the branching factor is nearly uniform. However, FDFPN is fragile to its heuristic move ordering function. The recent advances of Convolutional Neural Networks (CNNs) have led to considerable progress in game playing. We investigate how to incorporate the strength of CNNs into solving, with application to the game of Hex. We describe FDFPN-CNN, a new focused DFPN search that uses convolutional neural networks. FDFPN-CNN integrates two CNNs trained from games played by expert players. The value approximation CNN provides reliable information for defining the widening size by estimating the value of the node to expand, while the policy CNN selects promising children nodes to the search. On 8x8 Hex, experimental results show FDFPN-CNN performs notably better than FDFPN, suggesting a promising direction for better solving Hex positions where learning from strong players is possible.

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ICRA Conference 2017 Conference Paper

MSGD: Scalable back-end for indoor magnetic field-based GraphSLAM

  • Chao Gao
  • Robert Harle

Simultaneous Localisation and Mapping (SLAM) systems that recover the trajectory of a robot or mobile device are characterised by a front-end and back-end. The front-end uses sensor observations to identify loop closures; the back-end optimises the estimated trajectory to be consistent with these closures. The GraphSLAM framework formulates the back-end problem as a graph-based optimisation on a pose graph. This paper describes a back-end system optimised for very dense sequence-based loop closures. This arises when the frontend generates magnetic loop closures, among other things. Magnetic measurements are fast varying, which is good for localisation, but the requirement for high sampling rates (50 Hz+) produces many more loop closures than conventional systems. To date, however, there is no study optimising GraphSLAM back-end for sequence-based magnetic loop closures. Hence we introduce a novel variant of the Stochastic Gradient Descent-based SLAM algorithm called MSGD (Magnetic-SGD). We use high-accuracy groundtruth system and extensive real datasets to evaluate MSGD against state-of-the-art back-end algorithms. We demonstrate MSGD is at least as good as the best competitor algorithm in terms of quality, while being faster and more scalable.

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ICML Conference 2016 Conference Paper

Exact Exponent in Optimal Rates for Crowdsourcing

  • Chao Gao
  • Yu Lu
  • Dengyong Zhou

Crowdsourcing has become a popular tool for labeling large datasets. This paper studies the optimal error rate for aggregating crowdsourced labels provided by a collection of amateur workers. Under the Dawid-Skene probabilistic model, we establish matching upper and lower bounds with an exact exponent mI(\pi), where m is the number of workers and I(\pi) is the average Chernoff information that characterizes the workers’ collective ability. Such an exact characterization of the error exponent allows us to state a precise sample size requirement m \ge \frac1I(\pi)\log\frac1ε in order to achieve an εmisclassification error. In addition, our results imply optimality of various forms of EM algorithms given accurate initializers of the model parameters.

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JMLR Journal 2016 Journal Article

Optimal Estimation and Completion of Matrices with Biclustering Structures

  • Chao Gao
  • Yu Lu
  • Zongming Ma
  • Harrison H. Zhou

Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (Hartigan, 1972) where one seeks to cluster cases and variables simultaneously. Such structures are also prevalent in block modeling of networks. In this paper, we develop a theory for the estimation and completion of matrices with biclustering structures, where the data is a partially observed and noise contaminated matrix with a certain underlying biclustering structure. In particular, we show that a constrained least squares estimator achieves minimax rate-optimal performance in several of the most important scenarios. To this end, we derive unified high probability upper bounds for all sub-Gaussian data and also provide matching minimax lower bounds in both Gaussian and binary cases. Due to the close connection of graphon to stochastic block models, an immediate consequence of our general results is a minimax rate- optimal estimator for sparse graphons. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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ICRA Conference 2010 Conference Paper

On-line calibration of multiple LIDARs on a mobile vehicle platform

  • Chao Gao
  • John R. Spletzer

In this paper, we examine the problem of extrinsic calibration of multiple LIDARs on a mobile vehicle platform. To achieve fully automated and on-line calibration, the original non-linear calibration model is reformulated as a second-order cone program (SOCP). This provides an advantage over more standard linearized approaches in that a priori information such as a default LIDAR calibration, calibration tolerances, etc. , can be readily modeled. Furthermore, in contrast to general non-linear methods, the SOCP relaxation is convex, returns a global minimum, and can be solved very quickly using modern interior point methods (IPM). This enables the calibration to be estimated on-line for multiple LIDARs simultaneously. Experimental results are provided where the approach is used to successfully calibrate a pair of Sick LMS291-S14 LIDARs mounted on a mobile vehicle platform. These showed the SOCP formulation yielded a more accurate reconstruction and was 1-2 orders of magnitude faster than the traditional non-linear least-squares approach.

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