In this paper, we present the joint optimization of computation path selection and workload allocation for Directed Acyclic Graph (DAG) tasks in edge computing networks. Existing works primarily focus on end-to-end latency and are often restricted to simple task chains, neglecting critical factors such as server operational costs and the dynamics of arrival of tasks. To bridge this gap, we formulate the online scheduling problem as a mixed integer program to minimize server operational costs and latency.We then decompose this problem into a minimum-latency path selection subproblem and a task scheduling subproblem formulated as a Markov Decision Process (MDP). Our solution consists of a latency-aware transmission scheduling algorithm and a novel online scheduler based on Proximal Policy Optimization (PPO). Furthermore, we leverage Graph Neural Networks (GNNs) and Long Short-Term Memory networks (LSTMs) to encode the system state, thereby significantly improving the agent’s perception of the complex environment. Finally, extensive simulation results demonstrate that the proposed algorithm shows good adaptability and outperforms the state-of-the-art algorithms.

Rewards serve as proxies for human preferences and play a crucial role in Reinforcement Learning from Human Feedback (RLHF).However, if these rewards are inherently imperfect, exhibiting various biases, they can adversely affect the alignment of large language models (LLMs).In this paper, we collectively define the various biases present in rewards as the problem of reward unfairness.We propose a bias-agnostic method to address the issue of reward fairness from a resource allocation perspective, without specifically designing for each type of bias, yet effectively mitigating them.Specifically, we model preference learning as a resource allocation problem, treating rewards as resources to be allocated while considering the trade-off between utility and fairness in their distribution.We propose two methods, Fairness Regularization and Fairness Coefficient, to achieve fairness in rewards.We apply our methods in both verification and reinforcement learning scenarios to obtain a fairness reward model and a policy model, respectively.Experiments conducted in these scenarios demonstrate that our approach aligns LLMs with human preferences in a more fair manner.Our data and code are available at https://github.com/ shoyua/Towards-Reward-Fairness.

In recent years, the field of heterogeneous graph learning has garnered significant interest. Various efforts have been made towards learning heterogeneous graph representations, such as designing meta-paths to mine implicit graph knowledge or directly applying Graph Neural Networks (GNNs) for graph representation. However, these methods fail to fully capture available graph knowledge while ensuring scalability across diverse graph settings. In this paper, we address these challenges by introducing IEGraph, a heterogeneous Graph learning approach that capitalizes on both implicit and explicit graph knowledge. This encompasses two training stages: the implicit label-free stage and the explicit label-based stage, fostering comprehensive utilization of graph information. The label-free stage extracts implicit graph knowledge by constructing local and global training samples for contrastive pre-training, while the label-based stage further employs explicit labeled data to fine-tune the model. We carry out experiments on diverse heterogeneous graphs, and the results show that IEGraph achieves commendable performance compared to other state-of-the-art baselines.

Fault localization (FL) is a critical but time-consuming part of software debugging. With the improvement of the Large Language Models (LLMs) in their code capabilities, the increasing demand for automated software development has encouraged more research on building LLM-based Fault Localization (LLMFL) systems. However, existing LLMFL techniques are typically restricted to predicting bug locations by analyzing static code, while overlooking crucial dynamic program state of the software. This lack of context makes LLMs prone to generating "hallucinations", incorrectly identifying bug-free code as suspicious. To address this, this paper introduces PingFL, the LLMFL system that incorporates program dynamic information for more accurate automatic fault localization. PingFL comprises a Fault Localization (FL) agent and a Print Debugging (PD) agent. The FL agent is tasked with understanding the root cause through a set of callable tools. When the FL agent nominates a location as suspicious, it would entrust the PD agent to verify the suspected issue through multiple rounds of print debugging. In particular, these two agents communicate efficiently by conveying the textual thought generated by the LLM. The evaluation on 812 real-world bugs from the Defects4J benchmark shows that PingFL can localize 450 bugs within Top-1, which significantly outperforms other LLM-based approaches by 41% to 122%. A deeper dive into PingFL’s performance reveals that it exhibits specific FL strategies and tool usage patterns even without explicit instructions. Finally, PingFL proves to be cost-effective, spending an average of $0.23 and 104.62 seconds per bug, with the print debugging mechanism accounting for only $0.07 and 48.14 seconds.

Generative graph self-supervised learning (SSL), represented by masked graph autoencoders (GAEs), has shown great potential in graph representation learning. Existing masked GAEs typically rely on reconstruction criteria, such as mean squared error, to measure the discrepancy between the input graph and the reconstructed output. However, this learning paradigm struggles with perturbed graph characteristics, hindering the learning of robust graph representations. To address this, we introduce AMGAE -- an Adversarial Masked Graph AutoEncoder, which enhances the robustness of masked GAEs by integrating an adversarial learning strategy. Specifically, we design AMGAE to comprise a generator and a discriminator, optimized alternately and interconnected by a binary discrimination task (BDT). We treat the entire masked GAE as the generator, which produces a reconstructed output using the visible graph features. Then, we synthesize the reconstructed output by substituting the visible node features with the corresponding raw input features. Finally, we employ an additional GNN layer as the discriminator to determine the authenticity of the node-level features synthesized by BDT. By introducing the adversarial strategy, AMGAE reformulates masked GAE learning into a min-max game, which facilitates the learning of robust graph representations. We conduct extensive experiments on three graph tasks, demonstrating that AMGAE performs favorably against diverse baselines.

In the existing spectral GNNs, polynomial-based methods occupy the mainstream in designing a filter through the Laplacian matrix. However, polynomial combinations factored by the Laplacian matrix naturally have limitations in message passing (e.g., over-smoothing). Furthermore, most existing spectral GNNs are based on polynomial bases, which struggle to capture the high-frequency parts of the graph spectral signal. Additionally, we also find that even increasing the polynomial order does not change this situation, which means polynomial-based models have a natural deficiency when facing high-frequency signals. To tackle these problems, we propose WaveNet, which aims to effectively capture the high-frequency part of the graph spectral signal from the perspective of wavelet bases through reconstructing the message propagation matrix. We utilize Multi-Resolution Analysis (MRA) to model this question, and our proposed method can reconstruct arbitrary filters theoretically. We also conduct node classification experiments on real-world graph benchmarks and achieve superior performance on most datasets. Our code is available at https://github.com/Bufordyang/WaveNet

Generative self-supervised learning, represented by graph autoencoders (GAEs), has begun to exhibit significant potential in addressing graph tasks. However, GAEs often rely on Graph Neural Networks (GNNs) for encoding and decoding, this can pose a computation challenge due to the inherent complexities of the aggregation mechanism in GNNs. Furthermore, the bipartite structure of GAEs introduces additional computational burdens. In contrast, Multi-Layer Perceptrons (MLPs) have no graph dependency and can train much faster than GNNs. Motivated by this, in this work, we introduce a simple yet effective alternative: the GNN-Free Masked AutoEncoder (GFMAE), which employs MLPs rather than GNNs to serve as the backbone model to speed up training. Additionally, we devise comprehensive decoding strategies to compensate for the inability of MLPs in characterizing the graph. Our comprehensive experiments conducted on eight datasets demonstrate that GFMAE achieves performance comparable to GNNs while also enhancing the training efficiency of generative models with GNNs as the backbone.

Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs. Our work introduces a novel approach, called the "Query Latent Semantic Calibrator (QLSC)", designed as an auxiliary module for existing MRC models. We propose a unique scaling strategy to capture latent semantic center features of queries. These features are then seamlessly integrated into traditional query and passage embeddings using an attention mechanism. By deepening the comprehension of the semantic queries-passage relationship, our approach diminishes sensitivity to variations in text format and boosts the model’s capability in pinpointing accurate answers. Experimental results on robust Question-Answer datasets confirm that our approach effectively handles format-variant but semantically identical queries, highlighting the effectiveness and adaptability of our proposed method.

The theoretical analysis of spectral clustering is mainly devoted to consistency, while there is little research on its generalization performance. In this paper, we study the excess risk bounds of the popular spectral clustering algorithms: relaxed RatioCut and relaxed NCut. Our analysis follows the two practical steps of spectral clustering algorithms: continuous solution and discrete solution. Firstly, we provide the convergence rate of the excess risk bounds between the empirical continuous optimal solution and the population-level continuous optimal solution. Secondly, we show the fundamental quantity influencing the excess risk between the empirical discrete optimal solution and the population-level discrete optimal solution. At the empirical level, algorithms can be designed to reduce this quantity. Based on our theoretical analysis, we propose two novel algorithms that can penalize this quantity and, additionally, can cluster the out-of-sample data without re-eigendecomposition on the overall samples. Numerical experiments on toy and real datasets confirm the effectiveness of our proposed algorithms.