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Siwei Wang

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47 papers
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47

AAAI Conference 2026 Conference Paper

Collaborative Transformers with Multi-Level Forensic Attention for Image Manipulation Localization

  • Jiwei Zhang
  • Wenbo Feng
  • Siwei Wang
  • Feifei Kou
  • Haoyang Yu
  • Shaozhang Niu

The proliferation of the tampered images on social media can pose serious societal risks, influencing public opinion and causing panic. Image Manipulation Localization technique has advanced to address this, but some methods focus on microscopic traces, overlooking macroscopic semantics that deceive viewers. To address this problem, we propose a novel Image Manipulation Localization framework called Collaborative Transformers (Co-Transformers), designed to fully explore and utilize the collaborative information between macroscopic semantics and microscopic traces. This framework is based on two Vision Transformer variants. The first variant captures the semantic logic of the image. The second variant delves into microscopic tampering traces. By dynamically fusing these two complementary features, the framework enables interaction between macroscopic semantic inconsistencies and microscopic abnormal traces, effectively coordinating their relationship in the latent space. Furthermore, we introduce a new Multi-Level Forensic Attention (MLF-Attention) mechanism to enhance the model's ability to extract various tampered traces, this mechanism can be integrated into our framework. Compared with existing methods, our proposed framework achieves state-of-the-art results in localization accuracy and shows good robustness against various attacks.

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AAAI Conference 2026 Conference Paper

DMCAR: Disentangled Mixture-of-Experts with Context-Aware Routing for Multi-View Clustering

  • Baili Xiao
  • Ke Liang
  • Jiaqi Jin
  • Jun Wang
  • Yinbo Xu
  • Siwei Wang
  • En Zhu

Multi-View Clustering (MVC) aims to enhance clustering performance by integrating multi-source complementary information. However, existing deep MVC methods face inherent challenges in balancing the learning of shared consensus representations with the preservation of view-specific information: independent encoders hinder effective cross-view collaboration, while a single shared encoder tends to sacrifice representation diversity. Although the recently introduced Mixture-of-Experts (MoE) model offers a novel approach to facilitating view collaboration, its flattened expert pool design often leads to entanglement between shared and specific information, and its routing mechanism limits collaboration potential by neglecting cross-view context. To address these challenges, this paper proposes a novel deep multi-view clustering framework—Decoupled Mixture-of-Experts with Context-Aware Routing for Multi-View Clustering (DMCAR-MVC). At its core is an innovative Decoupled MoE (D-MoE) architecture. We establish a public expert pool to learn cross-view shared representations while equipping each view with an independent private expert pool to capture its unique information, thereby structurally enforcing the decoupling of shared and specific representations. Building on this, we further design a Context-Aware Hierarchical Routing (CAHR) mechanism. When routing for the public expert pool, this mechanism introduces a global context vector to guide expert selection, enabling more efficient and globally informed cross-view collaboration. Finally, to optimize the model, we adopt a multi-level contrastive learning paradigm: on one hand, a cross-view alignment loss ensures semantic consistency in shared representations; on the other, an orthogonality constraint is imposed to further enhance separability between shared and specific representations. Extensive experiments on multiple benchmark datasets demonstrate that DMCAR-MVC significantly outperforms state-of-the-art methods across key clustering metrics. Additionally, comprehensive ablation studies thoroughly validate the effectiveness and necessity of each proposed component.

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AAAI Conference 2026 Conference Paper

Graph Masked Autoencoder for Multi-view Remote Sensing Data Clustering

  • Renxiang Guan
  • Junhong Li
  • Siwei Wang
  • Tianrui Liu
  • Dayu Hu
  • Miaomiao Li
  • Xinwang Liu

Multi-view graph clustering (MVGC) for remote sensing data has gained increasing attention due to its ability to integrate complementary information across modalities while capturing spatial dependencies in heterogeneous data. Although current methods based on graph contrastive learning achieve strong performance, they often misidentify intra-cluster samples as negatives, leading to class conflicts and reduced clustering accuracy. Graph masked autoencoders have recently shown promising potential in learning robust representations through masked reconstruction, but their application to remote sensing data remains underexplored. This challenge is especially notable in the multi-view remote sensing setting, where high heterogeneity and complex spatial structures increase the difficulty of effective representation learning. To address these issues, we propose Clustering-Guided graph Mask AutoEncoder (CG-MAE), the first framework to extend graph masked autoencoders to multi-view remote sensing clustering. We introduce a clustering-guided masking strategy that selectively masks nodes near cluster centers and intra-cluster edges, which are crucial for capturing key structural information. By reconstructing these masked components, the model is encouraged to focus on learning features that are highly relevant to clustering. To further improve training stability and efficiency, we design an easy-to-hard node masking strategy that enables the model to gradually learn from increasingly challenging patterns. Additionally, we propose a dual self-adaptive learning mechanism that encourages the model to align more closely with the underlying semantic distributions. Extensive experiments on four widely used multi-view remote sensing datasets demonstrate that CG-MAE consistently outperforms state-of-the-art methods in both clustering accuracy and representation quality.

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AAAI Conference 2026 Conference Paper

Hierarchical Cross-View Alignment for Multi-View Clustering via Decoupled Information Distillation

  • Taichun Zhou
  • Siwei Wang
  • Zhibin Dong
  • Jiaqi Jin
  • Ke Liang
  • Baili Xiao
  • Miaomiao Li
  • Xinwang Liu

Multi-view clustering aims to uncover shared semantics and complementary information across different views. However, the inherent heterogeneity among views poses significant challenges to effective collaborative modeling and information integration. While recent studies have introduced distillation-based mechanisms to enhance cross-view consistency and alleviate heterogeneity, these approaches often rely on manually defined knowledge transfer paths or fixed fusion weights, which are inflexible in handling complex and dynamic view relationships in practice. To address this issue, we propose HOARD: a novel framework for Hierarchical crOss-view Alignment for multi-view clusteRing via Decoupled information distillation. HOARD structurally decouples multi-view representations into shared and specific components, and performs hierarchical alignment. Specifically, we introduce a granular-ball contrastive alignment to enhance the semantic consistency of shared features, and a prototype collaborative transmission alignment strategy to align specific features while preserving view-specific structural characteristics. Moreover, we design an information distillation unit to adaptively model cross-view knowledge transfer in both feature spaces. An attention mechanism is further employed to integrate shared and specific information. Extensive experiments on benchmark datasets demonstrate that HOARD significantly improves alignment quality and clustering performance, achieving state-of-the-art results.

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AAAI Conference 2026 Conference Paper

Parameter-Free Clustering via Self-Supervised Consensus Maximization

  • Lijun Zhang
  • Suyuan Liu
  • Siwei Wang
  • Shengju Yu
  • Xueling Zhu
  • Miaomiao Li
  • Xinwang Liu

Clustering is a fundamental task in unsupervised learning, but most existing methods heavily rely on hyperparameters such as the number of clusters or other sensitive settings, limiting their applicability in real-world scenarios. To address this long-standing challenge, we propose a novel and fully parameter-free clustering framework via Self-supervised Consensus Maximization, named SCMax. Our framework performs hierarchical agglomerative clustering and cluster evaluation in a single, integrated process. At each step of agglomeration, it creates a new, structure-aware data representation through a self-supervised learning task guided by the current clustering structure. We then introduce a nearest neighbor consensus score, which measures the agreement between the nearest neighbor-based merge decisions suggested by the original representation and the self-supervised one. The moment at which consensus maximization occurs can serve as a criterion for determining the optimal number of clusters. Extensive experiments on multiple datasets demonstrate that the proposed framework outperforms existing clustering approaches designed for scenarios with an unknown number of clusters.

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AAAI Conference 2026 Conference Paper

TVChain: Leveraging Textual-Visual Prompt Chains for Jailbreaking Large Vision-Language Models

  • Hao Yu
  • Ke Liang
  • Junxian Duan
  • Jun Wang
  • Siwei Wang
  • Chuan Ma
  • Xinwang Liu

Large Vision-Language Models (LVLMs) enhance the capabilities of Large Language Models by integrating visual inputs, thereby enabling advanced multimodal reasoning across diverse applications. However, these enhanced reasoning capabilities introduce new security risks, particularly to jailbreaking attacks that bypass built-in safety mechanisms to elicit harmful or unauthorized outputs. While recent efforts have explored adversarial and typographic prompts, most existing attacks suffer from three key limitations: reliance on auxiliary models, limited effectiveness in black-box scenarios, and inadequate exploitation of the LVLMs' intrinsic reasoning abilities. In this work, we propose TVChain, a novel black-box jailbreaking framework that explicitly intervenes in both the visual and textual reasoning processes of LVLMs. TVChain decomposes malicious prompts into a sequence of semantically meaningful sub-images that represent relevant objects and behaviors, thereby circumventing direct exposure of illicit content. In parallel, a carefully designed chain-of-thought (CoT) textual prompt is employed to steer the model's reasoning toward reconstructing the intended activity in a covert yet effective manner. We demonstrate that this compositional prompting strategy reduces the likelihood of triggering safety mechanisms while preserving attack efficacy. Extensive evaluations on eleven LVLMs (seven open-source and four commercial) across two benchmark datasets and three state-of-the-art defenses validate the effectiveness and robustness of TVChain.

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NeurIPS Conference 2025 Conference Paper

Bit-swapping Oriented Twin-memory Multi-view Clustering in Lifelong Incomplete Scenarios

  • Shengju Yu
  • Pei Zhang
  • Siwei Wang
  • Suyuan Liu
  • Xinhang Wan
  • Zhibin Dong
  • Tiejun Li
  • Xinwang Liu

Although receiving notable improvements, current multi-view clustering (MVC) techniques generally rely on feature library mechanisms to propagate accumulated knowledge from historical views to newly-arrived data, which overlooks the information pertaining to basis embedding within each view. Moreover, the mapping paradigm inevitably alters the values of learned landmarks and built affinities due to the uninterruption nature, accordingly disarraying the hierarchical cluster structures. To mitigate these two issues, we in the paper provide a named BSTM algorithm. Concretely, we firstly synchronize with the distinct dimensions by introducing a group of specialized projectors, and then establish unified anchors for all views collected so far to capture intrinsic patterns. Afterwards, departing from per-view architectures, we devise a shared bipartite graph construction via indicators to quantify similarity, which not only avoids redundant data-recalculations but alleviates the representation distortion caused by fusion. Crucially, there two components are optimized within an integrated framework, and collectively facilitate knowledge transfer upon encountering incoming views. Subsequently, to flexibly do transformation on anchors and meanwhile maintain numerical consistency, we develop a bit-swapping scheme operating exclusively on 0 and 1. It harmonizes anchors on current view and that on previous views through one-hot encoded row and column attributes, and the graph structures are correspondingly reordered to reach a matched configuration. Furthermore, a computationally efficient four-step updating strategy with linear complexity is designed to minimize the associated loss. Extensive experiments organized on publicly-available benchmark datasets with varying missing percentages confirm the superior effectiveness of our BSTM.

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AAMAS Conference 2025 Conference Paper

Efficient and Optimal Policy Gradient Algorithm for Corrupted Multi-armed Bandits

  • Jiayuan Liu
  • Siwei Wang
  • Zhixuan Fang

In this paper, we consider the stochastic multi-armed bandits problem with adversarial corruptions, where the random rewards of the arms are partially modified by an adversary to fool the algorithm. We apply the policy gradient algorithm SAMBA to this setting, and show that it is computationally efficient, and achieves a state-of-the-art 𝑂(𝐾 log𝑇/Δ) +𝑂(𝐶/Δ) regret upper bound, where 𝐾 is the number of arms, 𝐶 is the unknown corruption level, Δ is the minimum expected reward gap between the best arm and other ones, and 𝑇 is the time horizon. Compared with the best existing efficient algorithm (e. g. , CBARBAR), whose regret upper bound is 𝑂(𝐾 log2 𝑇/Δ) +𝑂(𝐶), we show that SAMBA reduces one log𝑇 factor in the regret bound, while maintaining the corruptiondependent term to be linear with 𝐶. This is indeed asymptotically optimal. We also conduct simulations to demonstrate the effectiveness of SAMBA, and the results show that SAMBA outperforms existing baselines.

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AAMAS Conference 2025 Conference Paper

Learning with Limited Shared Information in Multi-agent Multi-armed Bandit

  • Junning Shao
  • Siwei Wang
  • Zhixuan Fang

Multi-agent multi-armed bandit (MAMAB) is a classic collaborative learning model and has gained much attention in recent years. However, existing studies do not consider the case where an agent may refuse to share all her information with others, e. g. , when some of the data contains personal privacy. In this paper, we propose a novel limited shared information multi-agent multi-armed bandit (LSI-MAMAB) model in which each agent only shares the information that she is willing to share, and propose the Balanced- ETC algorithm to help multiple agents collaborate efficiently with limited shared information. Our analysis shows that Balanced-ETC is asymptotically optimal and its average regret (on each agent) approaches a constant when there are sufficient agents involved. Moreover, to encourage agents to participate in this collaborative learning, an incentive mechanism is proposed to make sure each agent can benefit from the collaboration system. Finally, we present experimental results to validate our theoretical results.

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AAAI Conference 2025 Conference Paper

Max-Mahalanobis Anchors Guidance for Multi-View Clustering

  • Pei Zhang
  • Yuangang Pan
  • Siwei Wang
  • Shengju Yu
  • Huiying Xu
  • En Zhu
  • Xinwang Liu
  • Ivor Tsang

Anchor selection or learning has become a critical component in large-scale multi-view clustering. Existing anchor-based methods, which either select-then-fix or initialize-then-optimize with orthogonality, yield promising performance. However, these methods still suffer from instability of initialization or insufficient depiction of data distribution. Moreover, the desired properties of anchors in multi-view clustering remain unspecified. To address these issues, this paper first formalizes the desired characteristics of anchors, namely Diversity, Balance and Compactness. We then devise and mathematically validate anchors that satisfy these properties by maximizing the Mahalanobis distance between anchors. Furthermore, we introduce a novel method called Max-Mahalanobis Anchors Guidance for multi-view Clustering (MAGIC), which guides the cross-view representations to progressively align with our well-defined anchors. This process yields highly discriminative and compact representations, significantly enhancing the performance of multi-view clustering. Experimental results show that our meticulously designed strategy significantly outperforms existing anchor-based methods in enhancing anchor efficacy, leading to substantial improvement in multi-view clustering performance.

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NeurIPS Conference 2025 Conference Paper

Mechanism Design for LLM Fine-tuning with Multiple Reward Models

  • Haoran Sun
  • Yurong Chen
  • Siwei Wang
  • Chu Xu
  • Wei Chen
  • Xiaotie Deng

Fine-tuning large language models (LLMs) to aggregate multiple preferences has attracted considerable research attention. With aggregation algorithms advancing, a potential economic scenario arises where fine-tuning services are provided to agents with different preferences. In this context, agents may benefit from strategically misreporting their preferences, but this could harm the aggregation performance. This paper addresses such incentive issues by framing it as a mechanism design problem: an LLM provider determines the fine-tuning objective (training rule) and the pricing scheme (payment rule) for agents. We primarily focus on training rules that maximize social welfare subject to certain regularizations, referred to as SW-Max rules. First, we show that under most circumstances, truthful reporting is sub-optimal with simply a SW-Max rule, thereby highlighting the necessity of payments. Second, we extend the VCG payment to implement SW-Max rules in dominant-strategy incentive compatibility (DSIC). We characterize sufficient conditions for payment equivalence and derive the necessary conditions for a payment rule to implement a SW-Max rule in DSIC and other principles. Third, we demonstrate that our mechanism is approximately DSIC with perturbed input, showcasing its robustness against the inevitable errors in real-world applications. Experiments on real LLM training results further confirm the practical implications of our results.

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IJCAI Conference 2025 Conference Paper

Soft Reasoning Paths for Knowledge Graph Completion

  • Yanning Hou
  • Sihang Zhou
  • Ke Liang
  • Lingyuan Meng
  • Xiaoshu Chen
  • Ke Xu
  • Siwei Wang
  • Xinwang Liu

Reasoning paths are reliable information in knowledge graph completion (KGC) in which algorithms can find strong clues of the actual relation between entities. However, in real-world applications, it is difficult to guarantee that computationally affordable paths exist toward all candidate entities. According to our observation, the prediction accuracy drops significantly when paths are absent. To make the proposed algorithm more stable against the missing path circumstances, we introduce soft reasoning paths. Concretely, a specific learnable latent path embedding is concatenated to each relation to help better model the characteristics of the corresponding paths. The combination of the relation and the corresponding learnable embedding is termed a soft path in our paper. By aligning the soft paths with the reasoning paths, a learnable embedding is guided to learn a generalized path representation of the corresponding relation. In addition, we introduce a hierarchical ranking strategy to make full use of information about the entity, relation, path, and soft path to help improve both the efficiency and accuracy of the model. Extensive experimental results illustrate that our algorithm outperforms the compared state-of-the-art algorithms by a notable margin. Our code will be released at https: //github. com/7HHHHH/SRP-KGC.

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AAAI Conference 2025 Conference Paper

Structure-Adaptive Multi-View Graph Clustering for Remote Sensing Data

  • Renxiang Guan
  • Wenxuan Tu
  • Siwei Wang
  • Jiyuan Liu
  • Dayu Hu
  • Chang Tang
  • Yu Feng
  • Junhong Li

Multi-view clustering (MVC) for remote sensing data is a critical and challenging task in Earth observation. Although recent advances in graph neural network (GNN)-based MVC have shown remarkable success, the most prevalent approaches have two major limitations: 1) heavily relying on a predefined yet fixed graph, which limits the performance of clustering because the large number of indistinguishable background samples contained in remote sensing data would introduce noise information and increase structure heterogeneity; 2) ignoring the effect of confusing samples on cluster structure compactness, which leads to fluffy cluster structure and decrease feature discriminability. To address these issues, we propose a Structure-Adaptive Multi-View Graph Clustering method named SAMVGC on remote sensing data which boosts the structure homogeneity and cluster compactness by adaptively learning the graph and cluster structures, respectively. Concretely, we use the geometric structure within the feature embedding space to refine adjacency matrices. The adjacency matrices are dynamically fused with the previous ones to improve the homogeneity and stability of structure information. Additionally, the samples are separated into two categories, including the central (intra-cluster center samples) and the confusing (inter-cluster boundary samples). On the basis, we deploy the contrastive learning paradigm on the central samples within views and the consistent learning paradigm on the confusing samples between views, improving the cluster compactness and consistency. Finally, we conduct extensive experiments on four benchmarks and achieve promising results, well demonstrating the effectiveness and superiority of the proposed method.

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TIST Journal 2024 Journal Article

A Game-theoretic Framework for Privacy-preserving Federated Learning

  • Xiaojin Zhang
  • Lixin Fan
  • Siwei Wang
  • Wenjie Li
  • Kai Chen
  • Qiang Yang

In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of privacy leakage cannot be ignored in the presence of semi-honest adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and attackers seems never-ending, we are concerned with one critical question: Is it possible to prevent potential attacks in advance? To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks. We name this game the federated learning privacy game (FLPG), in which neither defenders nor attackers are aware of all participants’ payoffs. To handle the incomplete information inherent in this situation, we propose associating the FLPG with an oracle that has two primary responsibilities. First, the oracle provides lower and upper bounds of the payoffs for the players. Second, the oracle acts as a correlation device, privately providing suggested actions to each player. With this novel framework, we analyze the optimal strategies of defenders and attackers. Furthermore, we derive and demonstrate conditions under which the attacker, as a rational decision-maker, should always follow the oracle’s suggestion not to attack.

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AAAI Conference 2024 Conference Paper

A Non-parametric Graph Clustering Framework for Multi-View Data

  • Shengju Yu
  • Siwei Wang
  • Zhibin Dong
  • Wenxuan Tu
  • Suyuan Liu
  • Zhao Lv
  • Pan Li
  • Miao Wang

Multi-view graph clustering (MVGC) derives encouraging grouping results by seamlessly integrating abundant information inside heterogeneous data, and has captured surging focus recently. Nevertheless, the majority of current MVGC works involve at least one hyper-parameter, which not only requires additional efforts for tuning, but also leads to a complicated solving procedure, largely harming the flexibility and scalability of corresponding algorithms. To this end, in the article we are devoted to getting rid of hyper-parameters, and devise a non-parametric graph clustering (NpGC) framework to more practically partition multi-view data. To be specific, we hold that hyper-parameters play a role in balancing error item and regularization item so as to form high-quality clustering representations. Therefore, under without the assistance of hyper-parameters, how to acquire high-quality representations becomes the key. Inspired by this, we adopt two types of anchors, view-related and view-unrelated, to concurrently mine exclusive characteristics and common characteristics among views. Then, all anchors' information is gathered together via a consensus bipartite graph. By such ways, NpGC extracts both complementary and consistent multi-view features, thereby obtaining superior clustering results. Also, linear complexities enable it to handle datasets with over 120000 samples. Numerous experiments reveal NpGC's strong points compared to lots of classical approaches.

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NeurIPS Conference 2024 Conference Paper

Alleviate Anchor-Shift: Explore Blind Spots with Cross-View Reconstruction for Incomplete Multi-View Clustering

  • Suyuan Liu
  • Siwei Wang
  • Ke Liang
  • Junpu Zhang
  • Zhibin Dong
  • Tianrui Liu
  • En Zhu
  • Kunlun He

Incomplete multi-view clustering aims to learn complete correlations among samples by leveraging complementary information across multiple views for clustering. Anchor-based methods further establish sample-level similarities for representative anchor generation, effectively addressing scalability issues in large-scale scenarios. Despite efficiency improvements, existing methods overlook the misguidance in anchors learning induced by partial missing samples, i. e. , the absence of samples results in shift of learned anchors, further leading to sub-optimal clustering performance. To conquer the challenges, our solution involves a cross-view reconstruction strategy that not only alleviate the anchor shift problem through a carefully designed cross-view learning process, but also reconstructs missing samples in a way that transcends the limitations imposed by convex combinations. By employing affine combinations, our method explores areas beyond the convex hull defined by anchors, thereby illuminating blind spots in the reconstruction of missing samples. Experimental results on four benchmark datasets and three large-scale datasets validate the effectiveness of our proposed method.

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NeurIPS Conference 2024 Conference Paper

ALPINE: Unveiling The Planning Capability of Autoregressive Learning in Language Models

  • Siwei Wang
  • Yifei Shen
  • Shi Feng
  • Haoran Sun
  • Shang-Hua Teng
  • Wei Chen

Planning is a crucial element of both human intelligence and contemporary large language models (LLMs). In this paper, we initiate a theoretical investigation into the emergence of planning capabilities in Transformer-based LLMs via their next-word prediction mechanisms. We model planning as a network path-finding task, where the objective is to generate a valid path from a specified source node to a designated target node. Our mathematical characterization shows that Transformer architectures can execute path-finding by embedding the adjacency and reachability matrices within their weights. Furthermore, our theoretical analysis of gradient-based learning dynamics reveals that LLMs can learn both the adjacency and a limited form of the reachability matrices. These theoretical insights are then validated through experiments, which demonstrate that Transformer architectures indeed learn the adjacency and an incomplete reachability matrices, consistent with our theoretical predictions. When applying our methodology to the real-world planning benchmark Blocksworld, our observations remain consistent. Additionally, our analyses uncover a fundamental limitation of current Transformer architectures in path-finding: these architectures cannot identify reachability relationships through transitivity, which leads to failures in generating paths when concatenation is required. These findings provide new insights into how the internal mechanisms of autoregressive learning facilitate intelligent planning and deepen our understanding of how future LLMs might achieve more advanced and general planning-and-reasoning capabilities across diverse applications.

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AAMAS Conference 2024 Conference Paper

Balanced and Incentivized Learning with Limited Shared Information in Multi-agent Multi-armed Bandit

  • Junning Shao
  • Siwei Wang
  • Zhixuan Fang

Multi-agent multi-armed bandit (MAMAB) is a classic collaborative learning model and has gained much attention in recent years. However, existing studies do not consider the case where an agent may refuse to share all her information with others, e. g. , when some of the data contains personal privacy. In this paper, we propose a novel limited shared information multi-agent multi-armed bandit (LSI-MAMAB) model in which each agent only shares the information that she is willing to share, and propose the Balanced- ETC algorithm to help multiple agents collaborate efficiently with limited shared information. Our analysis shows that Balanced-ETC is asymptotically optimal, and its average regret (on each agent) approaches a constant when there are sufficient agents involved. Moreover, to encourage agents to participate in this collaborative learning, an incentive mechanism is proposed to make sure each agent can benefit from the collaboration system. Finally, we present experimental results to validate our theoretical results.

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NeurIPS Conference 2024 Conference Paper

Can Graph Learning Improve Planning in LLM-based Agents?

  • Xixi Wu
  • Yifei Shen
  • Caihua Shan
  • Kaitao Song
  • Siwei Wang
  • Bohang Zhang
  • Jiarui Feng
  • Hong Cheng

Task planning in language agents is emerging as an important research topic alongside the development of large language models (LLMs). It aims to break down complex user requests in natural language into solvable sub-tasks, thereby fulfilling the original requests. In this context, the sub-tasks can be naturally viewed as a graph, where the nodes represent the sub-tasks, and the edges denote the dependencies among them. Consequently, task planning is a decision-making problem that involves selecting a connected path or subgraph within the corresponding graph and invoking it. In this paper, we explore graph learning-based methods for task planning, a direction that is orthogonal to the prevalent focus on prompt design. Our interest in graph learning stems from a theoretical discovery: the biases of attention and auto-regressive loss impede LLMs' ability to effectively navigate decision-making on graphs, which is adeptly addressed by graph neural networks (GNNs). This theoretical insight led us to integrate GNNs with LLMs to enhance overall performance. Extensive experiments demonstrate that GNN-based methods surpass existing solutions even without training, and minimal training can further enhance their performance. The performance gain increases with a larger task graph size.

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NeurIPS Conference 2024 Conference Paper

Clustering then Propagation: Select Better Anchors for Knowledge Graph Embedding

  • Ke Liang
  • Yue Liu
  • Hao Li
  • Lingyuan Meng
  • Suyuan Liu
  • Siwei Wang
  • Sihang Zhou
  • Xinwang Liu

Traditional knowledge graph embedding (KGE) models map entities and relations to unique embedding vectors in a shallow lookup manner. As the scale of data becomes larger, this manner will raise unaffordable computational costs. Anchor-based strategies have been treated as effective ways to alleviate such efficiency problems by propagation on representative entities instead of the whole graph. However, most existing anchor-based KGE models select the anchors in a primitive manner, which limits their performance. To this end, we propose a novel anchor-based strategy for KGE, i. e. , a relational clustering-based anchor selection strategy (RecPiece), where two characteristics are leveraged, i. e. , (1) representative ability of the cluster centroids and (2) descriptive ability of relation types in KGs. Specifically, we first perform clustering over features of factual triplets instead of entities, where cluster number is naturally set as number of relation types since each fact can be characterized by its relation in KGs. Then, representative triplets are selected around the clustering centroids, further mapped into corresponding anchor entities. Extensive experiments on six datasets show that RecPiece achieves higher performances but comparable or even fewer parameters compared to previous anchor-based KGE models, indicating that our model can select better anchors in a more scalable way.

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AAAI Conference 2024 Conference Paper

DVSAI: Diverse View-Shared Anchors Based Incomplete Multi-View Clustering

  • Shengju Yu
  • Siwei Wang
  • Pei Zhang
  • Miao Wang
  • Ziming Wang
  • Zhe Liu
  • Liming Fang
  • En Zhu

In numerous real-world applications, it is quite common that sample information is partially available for some views due to machine breakdown or sensor failure, causing the problem of incomplete multi-view clustering (IMVC). While several IMVC approaches using view-shared anchors have successfully achieved pleasing performance improvement, (1) they generally construct anchors with only one dimension, which could deteriorate the multi-view diversity, bringing about serious information loss; (2) the constructed anchors are typically with a single size, which could not sufficiently characterize the distribution of the whole samples, leading to limited clustering performance. For generating view-shared anchors with multi-dimension and multi-size for IMVC, we design a novel framework called Diverse View-Shared Anchors based Incomplete multi-view clustering (DVSAI). Concretely, we associate each partial view with several potential spaces. In each space, we enable anchors to communicate among views and generate the view-shared anchors with space-specific dimension and size. Consequently, spaces with various scales make the generated view-shared anchors enjoy diverse dimensions and sizes. Subsequently, we devise an integration scheme with linear computational and memory expenditures to integrate the outputted multi-scale unified anchor graphs such that running spectral algorithm generates the spectral embedding. Afterwards, we theoretically demonstrate that DVSAI owns linear time and space costs, thus well-suited for tackling large-size datasets. Finally, comprehensive experiments confirm the effectiveness and advantages of DVSAI.

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NeurIPS Conference 2024 Conference Paper

Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering

  • Fangdi Wang
  • Jiaqi Jin
  • Jingtao Hu
  • Suyuan Liu
  • Xihong Yang
  • Siwei Wang
  • Xinwang Liu
  • En Zhu

The fundamental goal of deep multi-view clustering is to achieve preferable task performance through inter-view cooperation. Although numerous DMVC approaches have been proposed, the collaboration role of individual views have not been well investigated in existing literature. Moreover, how to further enhance view cooperation for better fusion still needs to be explored. In this paper, we firstly consider DMVC as an unsupervised cooperative game where each view can be regarded as a participant. Then, we introduce the Shapley value and propose a novel MVC framework termed Shapley-based Cooperation Enhancing Multi-view Clustering (SCE-MVC), which evaluates view cooperation with game theory. Specially, we employ the optimal transport distance between fused cluster distributions and single view component as the utility function for computing shapley values. Afterwards, we apply shapley values to assess the contribution of each view and utilize these contributions to promote view cooperation. Comprehensive experimental results well support the effectiveness of our framework adopting to existing DMVC frameworks, demonstrating the importance and necessity of enhancing the cooperation among views.

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NeurIPS Conference 2024 Conference Paper

InfiBench: Evaluating the Question-Answering Capabilities of Code Large Language Models

  • Linyi Li
  • Shijie Geng
  • Zhenwen Li
  • Yibo He
  • Hao Yu
  • Ziyue Hua
  • Guanghan Ning
  • Siwei Wang

Large Language Models for code (code LLMs) have witnessed tremendous progress in recent years. With the rapid development of code LLMs, many popular evaluation benchmarks, such as HumanEval, DS-1000, and MBPP, have emerged to measure the performance of code LLMs with a particular focus on code generation tasks. However, they are insufficient to cover the full range of expected capabilities of code LLMs, which span beyond code generation to answering diverse coding-related questions. To fill this gap, we propose InfiBench, the first large-scale freeform question-answering (QA) benchmark for code to our knowledge, comprising 234 carefully selected high-quality Stack Overflow questions that span across 15 programming languages. InfiBench uses four types of model-free automatic metrics to evaluate response correctness where domain experts carefully concretize the criterion for each question. We conduct a systematic evaluation for over 100 latest code LLMs on InfiBench, leading to a series of novel and insightful findings. Our detailed analyses showcase potential directions for further advancement of code LLMs. InfiBench is fully open source at https: //infi-coder. github. io/infibench and continuously expanding to foster more scientific and systematic practices for code LLM evaluation.

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AAAI Conference 2024 Conference Paper

MINES: Message Intercommunication for Inductive Relation Reasoning over Neighbor-Enhanced Subgraphs

  • Ke Liang
  • Lingyuan Meng
  • Sihang Zhou
  • Wenxuan Tu
  • Siwei Wang
  • Yue Liu
  • Meng Liu
  • Long Zhao

GraIL and its variants have shown their promising capacities for inductive relation reasoning on knowledge graphs. However, the uni-directional message-passing mechanism hinders such models from exploiting hidden mutual relations between entities in directed graphs. Besides, the enclosing subgraph extraction in most GraIL-based models restricts the model from extracting enough discriminative information for reasoning. Consequently, the expressive ability of these models is limited. To address the problems, we propose a novel GraIL-based framework, termed MINES, by introducing a Message Intercommunication mechanism on the Neighbor-Enhanced Subgraph. Concretely, the message intercommunication mechanism is designed to capture the omitted hidden mutual information. It introduces bi-directed information interactions between connected entities by inserting an undirected/bi-directed GCN layer between uni-directed RGCN layers. Moreover, inspired by the success of involving more neighbors in other graph-based tasks, we extend the neighborhood area beyond the enclosing subgraph to enhance the information collection for inductive relation reasoning. Extensive experiments prove the promising capacity of the proposed MINES from various aspects, especially for the superiority, effectiveness, and transfer ability.

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JMLR Journal 2024 Journal Article

Optimal Learning Policies for Differential Privacy in Multi-armed Bandits

  • Siwei Wang
  • Jun Zhu

This paper studies the multi-armed bandit problem with a requirement of differential privacy guarantee or global differential privacy guarantee. We first prove that, the lower bound for the extra regret to protect $(\epsilon,\delta)$-global differential privacy is $\Omega({N\over \epsilon }\log {(e^{\epsilon} -1)T + \delta T \over (e^{\epsilon}-1) + \delta T})$ ($N$ is the number of arms and $T$ is the time horizon), which is independent with $T$ for $\delta > 0$ and large enough $T$. Moreover, the lower bound for the extra regret to protect $(\epsilon,\delta)$-differential privacy can be no more than the above bound. This means that, different with the case $\delta = 0$, it is possible to design algorithms that protect privacy and achieve the same asymptotical regret upper bound as the non-private algorithms when $\delta > 0$. Then we adapt the Follow the Perturbed Leader (FTPL) framework, and propose learning policies with both Gaussian and Beta perturbed distributions (DP-FTPL-Gauss and DP-FTPL-Beta) to protect $(\epsilon,\delta)$-differential privacy. The analysis shows that they achieve an $O({N\log T\over \Delta_{\min}} + N \min\{{1\over \delta^2}, {1\over \epsilon^2}\log{1\over \delta}\})$ regret upper bound, where $\Delta_{\min}$ is the minimum expected reward gap between the optimal arm and any other ones. We also design a unique perturbed distribution to protect $(\epsilon,\delta)$-differential privacy in the FTPL framework (DP-FTPL-New), which reduces the regret upper bound to $O({N\log T\over \Delta_{\min}} + {N\over \epsilon }\log {(e^{\epsilon} -1)T + \delta T \over (e^{\epsilon}-1) + \delta T})$. We further show that this perturbed distribution could also be used to protect $(\epsilon,\delta)$-global differential privacy, and design a corresponding algorithm GDP-Elim-New. We show that its regret upper bound is $O({\Delta_{\max} \over \Delta_{\min}}({N\log T\over \Delta_{\min}} + {N\over \epsilon }\log {(e^{\epsilon} -1)T + \delta T \over (e^{\epsilon}-1) + \delta T}))$. This shows that our $\Omega({N\over \epsilon }\log {(e^{\epsilon} -1)T + \delta T \over (e^{\epsilon}-1) + \delta T})$ regret lower bound is tight (e.g. when ${\Delta_{\max}\over \Delta_{\min}}$ is bounded). [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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AAAI Conference 2024 Conference Paper

Sample-Level Cross-View Similarity Learning for Incomplete Multi-View Clustering

  • Suyuan Liu
  • Junpu Zhang
  • Yi Wen
  • Xihong Yang
  • Siwei Wang
  • Yi Zhang
  • En Zhu
  • Chang Tang

Incomplete multi-view clustering has attracted much attention due to its ability to handle partial multi-view data. Recently, similarity-based methods have been developed to explore the complete relationship among incomplete multi-view data. Although widely applied to partial scenarios, most of the existing approaches are still faced with two limitations. Firstly, fusing similarities constructed individually on each view fails to yield a complete unified similarity. Moreover, incomplete similarity generation may lead to anomalous similarity values with column sum constraints, affecting the final clustering results. To solve the above challenging issues, we propose a Sample-level Cross-view Similarity Learning (SCSL) method for Incomplete Multi-view Clustering. Specifically, we project all samples to the same dimension and simultaneously construct a complete similarity matrix across views based on the inter-view sample relationship and the intra-view sample relationship. In addition, a simultaneously learning consensus representation ensures the validity of the projection, which further enhances the quality of the similarity matrix through the graph Laplacian regularization. Experimental results on six benchmark datasets demonstrate the ability of SCSL in processing incomplete multi-view clustering tasks. Our code is publicly available at https://github.com/Tracesource/SCSL.

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ICLR Conference 2024 Conference Paper

Whittle Index with Multiple Actions and State Constraint for Inventory Management

  • Chuheng Zhang
  • Xiangsen Wang
  • Wei Jiang 0024
  • Xianliang Yang
  • Siwei Wang
  • Lei Song 0001
  • Jiang Bian 0002

Whittle index is a heuristic tool that leads to good performance for the restless bandits problem. In this paper, we extend Whittle index to a new multi-agent reinforcement learning (MARL) setting with multiple discrete actions and a possibly changing constraint on the state space, resulting in WIMS (Whittle Index with Multiple actions and State constraint). This setting is common for inventory management where each agent chooses a replenishing quantity level for the corresponding stock-keeping-unit (SKU) such that the total profit is maximized while the total inventory does not exceed a certain limit. Accordingly, we propose a deep MARL algorithm based on WIMS for inventory management. Empirically, our algorithm is evaluated on real large-scale inventory management problems with up to 2307 SKUs and outperforms operation-research-based methods and baseline MARL algorithms.

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AAAI Conference 2023 Conference Paper

Auto-Weighted Multi-View Clustering for Large-Scale Data

  • Xinhang Wan
  • Xinwang Liu
  • Jiyuan Liu
  • Siwei Wang
  • Yi Wen
  • Weixuan Liang
  • En Zhu
  • Zhe Liu

Multi-view clustering has gained broad attention owing to its capacity to exploit complementary information across multiple data views. Although existing methods demonstrate delightful clustering performance, most of them are of high time complexity and cannot handle large-scale data. Matrix factorization-based models are a representative of solving this problem. However, they assume that the views share a dimension-fixed consensus coefficient matrix and view-specific base matrices, limiting their representability. Moreover, a series of large-scale algorithms that bear one or more hyperparameters are impractical in real-world applications. To address the two issues, we propose an auto-weighted multi-view clustering (AWMVC) algorithm. Specifically, AWMVC first learns coefficient matrices from corresponding base matrices of different dimensions, then fuses them to obtain an optimal consensus matrix. By mapping original features into distinctive low-dimensional spaces, we can attain more comprehensive knowledge, thus obtaining better clustering results. Moreover, we design a six-step alternative optimization algorithm proven to be convergent theoretically. Also, AWMVC shows excellent performance on various benchmark datasets compared with existing ones. The code of AWMVC is publicly available at https://github.com/wanxinhang/AAAI-2023-AWMVC.

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AAAI Conference 2023 Conference Paper

Cluster-Guided Contrastive Graph Clustering Network

  • Xihong Yang
  • Yue Liu
  • Sihang Zhou
  • Siwei Wang
  • Wenxuan Tu
  • Qun Zheng
  • Xinwang Liu
  • Liming Fang

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms. The code of CCGC is available at https://github.com/xihongyang1999/CCGC on Github.

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AAAI Conference 2023 Conference Paper

Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View

  • Jingcan Duan
  • Siwei Wang
  • Pei Zhang
  • En Zhu
  • Jingtao Hu
  • Hu Jin
  • Yue Liu
  • Zhibin Dong

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task. The source code is released at https://github.com/FelixDJC/GRADATE.

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AAAI Conference 2023 Conference Paper

Let the Data Choose: Flexible and Diverse Anchor Graph Fusion for Scalable Multi-View Clustering

  • Pei Zhang
  • Siwei Wang
  • Liang Li
  • Changwang Zhang
  • Xinwang Liu
  • En Zhu
  • Zhe Liu
  • Lu Zhou

In the past few years, numerous multi-view graph clustering algorithms have been proposed to enhance the clustering performance by exploring information from multiple views. Despite the superior performance, the high time and space expenditures limit their scalability. Accordingly, anchor graph learning has been introduced to alleviate the computational complexity. However, existing approaches can be further improved by the following considerations: (i) Existing anchor-based methods share the same number of anchors across views. This strategy violates the diversity and flexibility of multi-view data distribution. (ii) Searching for the optimal anchor number within hyper-parameters takes much extra tuning time, which makes existing methods impractical. (iii) How to flexibly fuse multi-view anchor graphs of diverse sizes has not been well explored in existing literature. To address the above issues, we propose a novel anchor-based method termed Flexible and Diverse Anchor Graph Fusion for Scalable Multi-view Clustering (FDAGF) in this paper. Instead of manually tuning optimal anchor with massive hyper-parameters, we propose to optimize the contribution weights of a group of pre-defined anchor numbers to avoid extra time expenditure among views. Most importantly, we propose a novel hybrid fusion strategy for multi-size anchor graphs with theoretical proof, which allows flexible and diverse anchor graph fusion. Then, an efficient linear optimization algorithm is proposed to solve the resultant problem. Comprehensive experimental results demonstrate the effectiveness and efficiency of our proposed framework. The source code is available at https://github.com/Jeaninezpp/FDAGF.

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NeurIPS Conference 2022 Conference Paper

Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences

  • Siwei Wang
  • Xinwang Liu
  • Suyuan Liu
  • Jiaqi Jin
  • Wenxuan Tu
  • Xinzhong Zhu
  • En Zhu

Multi-view anchor graph clustering selects representative anchors to avoid full pair-wise similarities and therefore reduce the complexity of graph methods. Although widely applied in large-scale applications, existing approaches do not pay sufficient attention to establishing correct correspondences between the anchor sets across views. To be specific, anchor graphs obtained from different views are not aligned column-wisely. Such an Anchor-Unaligned Problem (AUP) would cause inaccurate graph fusion and degrade the clustering performance. Under multi-view scenarios, generating correct correspondences could be extremely difficult since anchors are not consistent in feature dimensions. To solve this challenging issue, we propose the first study of the generalized and flexible anchor graph fusion framework termed Fast Multi-View Anchor-Correspondence Clustering (FMVACC). Specifically, we show how to find anchor correspondence with both feature and structure information, after which anchor graph fusion is performed column-wisely. Moreover, we theoretically show the connection between FMVACC and existing multi-view late fusion and partial view-aligned clustering, which further demonstrates our generality. Extensive experiments on seven benchmark datasets demonstrate the effectiveness and efficiency of our proposed method. Moreover, the proposed alignment module also shows significant performance improvement applying to existing multi-view anchor graph competitors indicating the importance of anchor alignment. Our code is available at \url{https: //github. com/wangsiwei2010/NeurIPS22-FMVACC}.

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NeurIPS Conference 2022 Conference Paper

Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms

  • Xutong Liu
  • Jinhang Zuo
  • Siwei Wang
  • Carlee Joe-Wong
  • John C. S. Lui
  • Wei Chen

In this paper, we study the combinatorial semi-bandits (CMAB) and focus on reducing the dependency of the batch-size $K$ in the regret bound, where $K$ is the total number of arms that can be pulled or triggered in each round. First, for the setting of CMAB with probabilistically triggered arms (CMAB-T), we discover a novel (directional) triggering probability and variance modulated (TPVM) condition that can replace the previously-used smoothness condition for various applications, such as cascading bandits, online network exploration and online influence maximization. Under this new condition, we propose a BCUCB-T algorithm with variance-aware confidence intervals and conduct regret analysis which reduces the $O(K)$ factor to $O(\log K)$ or $O(\log^2 K)$ in the regret bound, significantly improving the regret bounds for the above applications. Second, for the setting of non-triggering CMAB with independent arms, we propose a SESCB algorithm which leverages on the non-triggering version of the TPVM condition and completely removes the dependency on $K$ in the leading regret. As a valuable by-product, the regret analysis used in this paper can improve several existing results by a factor of $O(\log K)$. Finally, experimental evaluations show our superior performance compared with benchmark algorithms in different applications.

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NeurIPS Conference 2022 Conference Paper

Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness

  • Qingsong Liu
  • Weihang Xu
  • Siwei Wang
  • Zhixuan Fang

This paper proposes and studies for the first time the problem of combinatorial multi-armed bandits with linear long-term constraints. Our model generalizes and unifies several prominent lines of work, including bandits with fairness constraints, bandits with knapsacks (BwK), etc. We propose an upper-confidence bound LP-style algorithm for this problem, called UCB-LP, and prove that it achieves a logarithmic problem-dependent regret bound and zero constraint violations in expectation. In the special case of fairness constraints, we further provide a sharper constant regret bound for UCB-LP. Our regret bounds outperform the existing literature on BwK and bandits with fairness constraints simultaneously. We also develop another low-complexity version of UCB-LP and show that it yields $\tilde{O}(\sqrt{T})$ problem-independent regret and zero constraint violations with high-probability. Finally, we conduct numerical experiments to validate our theoretical results.

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AAAI Conference 2022 Conference Paper

Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

  • Suyuan Liu
  • Siwei Wang
  • Pei Zhang
  • Kai Xu
  • Xinwang Liu
  • Changwang Zhang
  • Feng Gao

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the subsequential anchor graph construction, which may adversely affect the clustering performance. In addition, postprocessing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we combine anchor learning and graph construction into a uniform framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. Our proposed EOMSC-CA is proven to be linear complexity respecting to the data size. The superiority of our EOMSC-CA over the effectiveness and efficiency is demonstrated by extensive experiments. Our code is publicly available at https: //github. com/Tracesource/EOMSC-CA.

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NeurIPS Conference 2022 Conference Paper

Learning low-dimensional generalizable natural features from retina using a U-net

  • Siwei Wang
  • Benjamin Hoshal
  • Elizabeth de Laittre
  • Olivier Marre
  • Michael Berry
  • Stephanie Palmer

Much of sensory neuroscience focuses on sensory features that are chosen by the experimenter because they are thought to be behaviorally relevant to the organism. However, it is not generally known what these features are in complex, natural scenes. This work focuses on using the retinal encoding of natural movies to determine the presumably behaviorally-relevant features that the brain represents. It is prohibitive to parameterize a natural movie and its respective retinal encoding fully. We use time within a natural movie as a proxy for the whole suite of features evolving across the scene. We then use a task-agnostic deep architecture, an encoder-decoder, to model the retinal encoding process and characterize its representation of ``time in the natural scene'' in a compressed latent space. In our end-to-end training, an encoder learns a compressed latent representation from a large population of salamander retinal ganglion cells responding to natural movies, while a decoder samples from this compressed latent space to generate the appropriate movie frame. By comparing latent representations of retinal activity from three movies, we find that the retina performs transfer learning to encode time: the precise, low-dimensional representation of time learned from one movie can be used to represent time in a different movie, with up to 17ms resolution. We then show that static textures and velocity features of a natural movie are synergistic. The retina simultaneously encodes both to establishes a generalizable, low-dimensional representation of time in the natural scene.

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NeurIPS Conference 2022 Conference Paper

Matching in Multi-arm Bandit with Collision

  • YiRui Zhang
  • Siwei Wang
  • Zhixuan Fang

In this paper, we consider the matching of multi-agent multi-armed bandit problem, i. e. , while agents prefer arms with higher expected reward, arms also have preferences on agents. In such case, agents pulling the same arm may encounter collisions, which leads to a reward of zero. For this problem, we design a specific communication protocol which uses deliberate collision to transmit information among agents, and propose a layer-based algorithm that helps establish optimal stable matching between agents and arms. With this subtle communication protocol, our algorithm achieves a state-of-the-art $O(\log T)$ regret in the decentralized matching market, and outperforms existing baselines in experimental results.

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AAAI Conference 2022 Conference Paper

Robust Graph-Based Multi-View Clustering

  • Weixuan Liang
  • Xinwang Liu
  • Sihang Zhou
  • Jiyuan Liu
  • Siwei Wang
  • En Zhu

Graph-based multi-view clustering (G-MVC) constructs a graphical representation of each view and then fuses them to a unified graph for clustering. Though demonstrating promising clustering performance in various applications, we observe that their formulations are usually non-convex, leading to a local optimum. In this paper, we propose a novel MVC algorithm termed robust graph-based multi-view clustering (RG-MVC) to address this issue. In particular, we define a min-max formulation for robust learning and then rewrite it as a convex and differentiable objective function whose convexity and differentiability are carefully proved. Thus, we can efficiently solve the resultant problem using a reduced gradient descent algorithm, and the corresponding solution is guaranteed to be globally optimal. As a consequence, although our algorithm is free of hyper-parameters, it has shown good robustness against noisy views. Extensive experiments on benchmark datasets verify the superiority of the proposed method against the compared state-of-the-art algorithms. Our codes and appendix are available at https: //github. com/wxliang/RG-MVC.

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NeurIPS Conference 2022 Conference Paper

Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel

  • Weixuan Liang
  • Xinwang Liu
  • Yong Liu
  • Sihang Zhou
  • Jun-Jie Huang
  • Siwei Wang
  • Jiyuan Liu
  • Yi Zhang

Multiple kernel clustering (MKC) is an important research topic that has been widely studied for decades. However, current methods still face two problems: inefficient when handling out-of-sample data points and lack of theoretical study of the stability and generalization of clustering. In this paper, we propose a novel method that can efficiently compute the embedding of out-of-sample data with a solid generalization guarantee. Specifically, we approximate the eigen functions of the integral operator associated with the linear combination of base kernel functions to construct low-dimensional embeddings of out-of-sample points for efficient multiple kernel clustering. In addition, we, for the first time, theoretically study the stability of clustering algorithms and prove that the single-view version of the proposed method has uniform stability as $\mathcal{O}\left(Kn^{-3/2}\right)$ and establish an upper bound of excess risk as $\widetilde{\mathcal{O}}\left(Kn^{-3/2}+n^{-1/2}\right)$, where $K$ is the cluster number and $n$ is the number of samples. We then extend the theoretical results to multiple kernel scenarios and find that the stability of MKC depends on kernel weights. As an example, we apply our method to a novel MKC algorithm termed SimpleMKKM and derive the upper bound of its excess clustering risk, which is tighter than the current results. Extensive experimental results validate the effectiveness and efficiency of the proposed method.

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AAAI Conference 2021 Conference Paper

A One-Size-Fits-All Solution to Conservative Bandit Problems

  • Yihan Du
  • Siwei Wang
  • Longbo Huang

In this paper, we study a family of conservative bandit problems (CBPs) with sample-path reward constraints, i. e. , the learner’s reward performance must be at least as well as a given baseline at any time. We propose a general one-sizefits-all solution to CBPs and present its applications to three encompassed problems, i. e. , conservative multi-armed bandits (CMAB), conservative linear bandits (CLB) and conservative contextual combinatorial bandits (CCCB). Different from previous works which consider high probability constraints on the expected reward, our algorithms guarantee sample-path constraints on the actual received reward, and achieve better theoretical guarantees (T-independent additive regrets instead of T-dependent) and empirical performance. Furthermore, we extend the results and consider a novel conservative mean-variance bandit problem (MV- CBP), which measures the learning performance in both the expected reward and variability. We design a novel algorithm with O(1/T) normalized additive regrets (T-independent in the cumulative form) and validate this result through empirical evaluation.

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AAAI Conference 2021 Conference Paper

Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback

  • Siwei Wang
  • Haoyun Wang
  • Longbo Huang

We study the multi-armed bandit (MAB) problem with composite and anonymous feedback. In this model, the reward of pulling an arm spreads over a period of time (we call this period as reward interval) and the player receives partial rewards of the action, convoluted with rewards from pulling other arms, successively. Existing results on this model require prior knowledge about the reward interval size as an input to their algorithms. In this paper, we propose adaptive algorithms for both the stochastic and the adversarial cases, without requiring any prior information about the reward interval. For the stochastic case, we prove that our algorithm guarantees a regret that matches the lower bounds (in order). For the adversarial case, we propose the first algorithm to jointly handle non-oblivious adversary and unknown reward interval size. We also conduct simulations based on real-world dataset. The results show that our algorithms outperform existing benchmarks.

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NeurIPS Conference 2021 Conference Paper

Continuous Mean-Covariance Bandits

  • Yihan Du
  • Siwei Wang
  • Zhixuan Fang
  • Longbo Huang

Existing risk-aware multi-armed bandit models typically focus on risk measures of individual options such as variance. As a result, they cannot be directly applied to important real-world online decision making problems with correlated options. In this paper, we propose a novel Continuous Mean-Covariance Bandit (CMCB) model to explicitly take into account option correlation. Specifically, in CMCB, there is a learner who sequentially chooses weight vectors on given options and observes random feedback according to the decisions. The agent's objective is to achieve the best trade-off between reward and risk, measured with option covariance. To capture different reward observation scenarios in practice, we consider three feedback settings, i. e. , full-information, semi-bandit and full-bandit feedback. We propose novel algorithms with optimal regrets (within logarithmic factors), and provide matching lower bounds to validate their optimalities. The experimental results also demonstrate the superiority of our algorithms. To the best of our knowledge, this is the first work that considers option correlation in risk-aware bandits and explicitly quantifies how arbitrary covariance structures impact the learning performance. The novel analytical techniques we developed for exploiting the estimated covariance to build concentration and bounding the risk of selected actions based on sampling strategy properties can likely find applications in other bandit analysis and be of independent interests.

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AAAI Conference 2021 Conference Paper

Hierarchical Multiple Kernel Clustering

  • Jiyuan Liu
  • Xinwang Liu
  • Siwei Wang
  • Sihang Zhou
  • Yuexiang Yang

Current multiple kernel clustering algorithms compute a partition with the consensus kernel or graph learned from the pre-specified ones, while the emerging late fusion methods firstly construct multiple partitions from each kernel separately, and then obtain a consensus one with them. However, both of them directly distill the clustering information from kernels or graphs to partition matrices, where the sudden dimension drop would result in loss of advantageous details for clustering. In this paper, we provide a brief insight of the aforementioned issue and propose a hierarchical approach to perform clustering while preserving advantageous details maximumly. Specifically, we gradually group samples into fewer clusters, together with generating a sequence of intermediary matrices of descending sizes. The consensus partition with is simultaneously learned and conversely guides the construction of intermediary matrices. Nevertheless, this cyclic process is modeled into an unified objective and an alternative algorithm is designed to solve it. In addition, the proposed method is validated and compared with other representative multiple kernel clustering algorithms on benchmark datasets, demonstrating state-of-the-art performance by a large margin.

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AAAI Conference 2020 Conference Paper

CBNet: A Novel Composite Backbone Network Architecture for Object Detection

  • Yudong Liu
  • Yongtao Wang
  • Siwei Wang
  • Tingting Liang
  • Qijie Zhao
  • Zhi Tang
  • Haibin Ling

In existing CNN based detectors, the backbone network is a very important component for basic feature1 extraction, and the performance of the detectors highly depends on it. In this paper, we aim to achieve better detection performance by building a more powerful backbone from existing ones like ResNet and ResNeXt. Specifically, we propose a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones, to form a more powerful backbone named Composite Backbone Network (CBNet). In this way, CBNet iteratively feeds the output features of the previous backbone, namely high-level features, as part of input features to the succeeding backbone, in a stage-by-stage fashion, and finally the feature maps of the last backbone (named Lead Backbone) are used for object detection. We show that CBNet can be very easily integrated into most state-of-the-art detectors and significantly improve their performances. For example, it boosts the mAP of FPN, Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1. 5 to 3. 0 points. Moreover, experimental results show that the instance segmentation results can be improved as well. Specifically, by simply integrating the proposed CBNet into the baseline detector Cascade Mask R-CNN, we achieve a new state-of-the-art result on COCO dataset (mAP of 53. 3) with a single model, which demonstrates great effectiveness of the proposed CBNet architecture. Code will be made available at https: //github. com/PKUbahuangliuhe/CBNet.

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NeurIPS Conference 2020 Conference Paper

Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits

  • Siwei Wang
  • Longbo Huang
  • John C. S. Lui

We study the online restless bandit problem, where the state of each arm evolves according to a Markov chain, and the reward of pulling an arm depends on both the pulled arm and the current state of the corresponding Markov chain. In this paper, we propose Restless-UCB, a learning policy that follows the explore-then-commit framework. In Restless-UCB, we present a novel method to construct offline instances, which only requires $O(N)$ time-complexity ($N$ is the number of arms) and is exponentially better than the complexity of existing learning policy. We also prove that Restless-UCB achieves a regret upper bound of $\tilde{O}((N+M^3)T^{2\over 3})$, where $M$ is the Markov chain state space size and $T$ is the time horizon. Compared to existing algorithms, our result eliminates the exponential factor (in $M, N$) in the regret upper bound, due to a novel exploitation of the sparsity in transitions in general restless bandit problems. As a result, our analysis technique can also be adopted to tighten the regret bounds of existing algorithms. Finally, we conduct experiments based on real-world dataset, to compare the Restless-UCB policy with state-of-the-art benchmarks. Our results show that Restless-UCB outperforms existing algorithms in regret, and significantly reduces the running time.

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IJCAI Conference 2019 Conference Paper

Multi-view Clustering via Late Fusion Alignment Maximization

  • Siwei Wang
  • Xinwang Liu
  • En Zhu
  • Chang Tang
  • Jiyuan Liu
  • Jingtao Hu
  • Jingyuan Xia
  • Jianping Yin

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.

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NeurIPS Conference 2018 Conference Paper

Multi-armed Bandits with Compensation

  • Siwei Wang
  • Longbo Huang

We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for $T$ steps. In each step, one short-term player arrives to the system. Upon arrival, the player greedily selects an arm with the current best average reward and receives a stochastic reward associated with the arm. In order to incentivize players to explore other arms, the controller provides proper payment compensation to players. The objective of the controller is to maximize the total reward collected by players while minimizing the compensation. We first give a compensation lower bound $\Theta(\sum_i {\Delta_i\log T\over KL_i})$, where $\Delta_i$ and $KL_i$ are the expected reward gap and Kullback-Leibler (KL) divergence between distributions of arm $i$ and the best arm, respectively. We then analyze three algorithms to solve the KCMAB problem, and obtain their regrets and compensations. We show that the algorithms all achieve $O(\log T)$ regret and $O(\log T)$ compensation that match the theoretical lower bound. Finally, we use experiments to show the behaviors of those algorithms.

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