Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of modality-invariant information (MII) and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal inputs for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses.

Large language model (LLM) applications in cloud root cause analysis (RCA) have been actively explored recently. However, current methods are still reliant on manual workflow settings and do not unleash LLMs' decision-making and environment interaction capabilities. We present RCAgent, a tool-augmented LLM autonomous agent framework for practical and privacy-aware industrial RCA usage. Running on an internally deployed model rather than GPT families, RCAgent is capable of free-form data collection and comprehensive analysis with tools. Our framework combines a variety of enhancements, including a unique Self-Consistency for action trajectories, and a suite of methods for context management, stabilization, and importing domain knowledge. Our experiments show RCAgent's evident and consistent superiority over ReAct across all aspects of RCA--predicting root causes, solutions, evidence, and responsibilities--and tasks covered or uncovered by current rules, as validated by both automated metrics and human evaluations. Furthermore, RCAgent has already been integrated into the diagnosis and issue discovery workflow of the Real-time Compute Platform for Apache Flink of Alibaba Cloud.

Encoding only the task-related information from the raw data, i.e., disentangled representation learning, can greatly contribute to the robustness and generalizability of models. Although significant advances have been made by regularizing the information in representations with information theory, two major challenges remain: 1) the representation compression inevitably leads to performance drop; 2) the disentanglement constraints on representations are in complicated optimization. To these issues, we introduce Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement. Building upon this framework, we propose DisTIB (Transmitted Information Bottleneck for Disentangled representation learning), a novel objective that navigates the balance between information compression and preservation. We employ variational inference to derive a tractable estimation for DisTIB. This estimation can be simply optimized via standard gradient descent with a reparameterization trick. Moreover, we theoretically prove that DisTIB can achieve optimal disentanglement, underscoring its superior efficacy. To solidify our claims, we conduct extensive experiments on various downstream tasks to demonstrate the appealing efficacy of DisTIB and validate our theoretical analyses.

Few-shot learning (FSL) poses a significant challenge in classifying unseen classes with limited samples, primarily stemming from the scarcity of data. Although numerous generative approaches have been investigated for FSL, their generation process often results in entangled outputs, exacerbating the distribution shift inherent in FSL. Consequently, this considerably hampers the overall quality of the generated samples. Addressing this concern, we present a pioneering framework called DisGenIB, which leverages an Information Bottleneck (IB) approach for Disentangled Generation. Our framework ensures both discrimination and diversity in the generated samples, simultaneously. Specifically, we introduce a groundbreaking Information Theoretic objective that unifies disentangled representation learning and sample generation within a novel framework. In contrast to previous IB-based methods that struggle to leverage priors, our proposed DisGenIB effectively incorporates priors as invariant domain knowledge of sub-features, thereby enhancing disentanglement. This innovative approach enables us to exploit priors to their full potential and facilitates the overall disentanglement process. Moreover, we establish the theoretical foundation that reveals certain prior generative and disentanglement methods as special instances of our DisGenIB, underscoring the versatility of our proposed framework. To solidify our claims, we conduct comprehensive experiments on demanding FSL benchmarks, affirming the remarkable efficacy and superiority of DisGenIB. Furthermore, the validity of our theoretical analyses is substantiated by the experimental results. Our code is available at https://github.com/eric-hang/DisGenIB.

Recent studies have revealed that GNNs are vulnerable to adversarial attacks. Most existing robust graph learning methods measure model robustness based on label information, rendering them infeasible when label information is not available. A straightforward direction is to employ the widely used Infomax technique from typical Unsupervised Graph Representation Learning (UGRL) to learn robust unsupervised representations. Nonetheless, directly transplanting the Infomax technique from typical UGRL to robust UGRL may involve a biased assumption. In light of the limitation of Infomax, we propose a novel unbiased robust UGRL method calledRobust Graph Information Bottleneck(RGIB), which is grounded in the Information Bottleneck (IB) principle. Our RGIB attempts to learn robust node representations against adversarial perturbations by preserving the original information in the benign graph while eliminating the adversarial information in the adversarial graph. There are mainly two challenges to optimizing RGIB: 1) high complexity of adversarial attack to perturb node features and graph structure jointly in the training procedure; 2) mutual information estimation upon adversarially attacked graphs. To tackle these problems, we further propose an efficient adversarial training strategy with only feature perturbations and an effective mutual information estimator with the subgraph-level summary. Moreover, we theoretically establish a connection between our proposed RGIB and the robustness of downstream classifiers, revealing that RGIB can provide a lower bound on the adversarial risk of downstream classifiers. Extensive experiments over several benchmarks and downstream tasks demonstrate the effectiveness and superiority of our proposed method.

Researchers recently investigated to explain Graph Neural Networks (GNNs) on the access to a task-specific GNN, which may hinder their wide applications in practice. Specifically, task-specific explanation methods are incapable of explaining pretrained GNNs whose downstream tasks are usually inaccessible, not to mention giving explanations for the transferable knowledge in pretrained GNNs. Additionally, task-specific methods only consider target models' output in the label space, which are coarse-grained and insufficient to reflect the model's internal logic. To address these limitations, we consider a two-stage explanation strategy, i.e., explainers are first pretrained in a task-agnostic fashion in the representation space and then further fine-tuned in the task-specific label space and representation space jointly if downstream tasks are accessible. The two-stage explanation strategy endows post-hoc graph explanations with the applicability to pretrained GNNs where downstream tasks are inaccessible and the capacity to explain the transferable knowledge in the pretrained GNNs. Moreover, as the two-stage explanation strategy explains the GNNs in the representation space, the fine-grained information in the representation space also empowers the explanations. Furthermore, to achieve a trade-off between the fidelity and intelligibility of explanations, we propose an explanation framework based on the Information Bottleneck principle, named Explainable Graph Information Bottleneck (EGIB). EGIB subsumes the task-specific explanation and task-agnostic explanation into a unified framework. To optimize EGIB objective, we derive a tractable bound and adopt a simple yet effective explanation generation architecture. Based on the unified framework, we further theoretically prove that task-agnostic explanation is a relaxed sufficient condition of task-specific explanation, which indicates the transferability of task-agnostic explanations. Extensive experimental results demonstrate the effectiveness of our proposed explanation method.