We study blind deconvolution of signals defined on the nodes of an undirected graph. Although observations are bilinear functions of both unknowns, namely the forward convolutional filter coefficients and the graph signal input, a filter invertibility requirement along with input sparsity allow for an efficient linear programming reformulation. Unlike prior art that relied on perfect knowledge of the graph eigenbasis, here we derive stable recovery conditions in the presence of small graph perturbations. We also contribute a provably convergent robust algorithm, which alternates between blind deconvolution of graph signals and eigenbasis denoising in the Stiefel manifold. Reproducible numerical tests showcase the algorithm's robustness under several graph eigenbasis perturbation models.

Subgraph counting is a fundamental component for many downstream applications such as graph representation learning and query optimization.Since obtaining the exact count is often intractable,there have been a plethora of approximation methods on graph sampling techniques. Nonetheless, the state-of-the-art sampling methods still require massive samples to produce accurate approximations on large data graphs.We propose gSWORD, a GPU framework that leverages the massive parallelism of GPUs to accelerate iterative sampling algorithms for subgraph counting. Despite the embarrassingly parallel nature of the samples, there are unique challenges in accelerating subgraph counting due to its irregular computation logic. To address these challenges, we introduce two GPU-centric optimizations: (1) sample inheritance, enabling threads to inherit samples from neighboring threads to avoid idling, and (2) warp streaming, effectively distributing workloads among threads through a streaming process. Moreover, we propose a CPU-GPU co-processing pipeline that overlaps the sampling and enumeration processes to mitigate the underestimation issue. Experimental results demonstrate that deploying state-of-the-art sampling algorithms on gSWORD can perform millions of samples per second. The co-processing pipeline substantially improves the estimation accuracy in the cases where existing methods encounter severe underestimations with negligible overhead.

Graph analytical queries (GAQs) are becoming increasingly important in various domains, including social networks, recommendation systems, and bioinformatics, among others.GAQs typically require iterative processing of the graph data to compute various metrics and identify patterns or anomalies. Parallel to the burgeoning demand for graph analytics, the need for Concurrent Graph Analytical Queries (CGAQs), allowing simultaneous execution of multiple graph queries, is increasing. Within social networks,CGAQs bolster real-time analytics, concurrently investigate various network properties, such as community detection, path analysis, and influence propagation. In transportation,CGAQs concurrently optimize multiple routes and manage real-time traffic data, contributing significantly to efficient supply chain strategies and traffic management. The key property ofCGAQs lies in their capacity for shared processing, exploiting the synergies between concurrent queries, which in return opens opportunities for improved system scalability and throughput. In this survey, we present a comprehensive review ofsystem-levelandalgorithm-levelefforts to supportCGAQprocessing. We introduce a novel survey framework based on three aspects: 1) What are the sharing opportunities exploited? 2) What are the scheduling techniques proposed to maximize sharing? 3) What are the optimizations employed? We also identify important gaps and promising research directions forCGAQprocessing.

Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we proposeGLP, a GPU-based framework to enable efficientLPprocessing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-acceleratedLPprocessing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficientLPalgorithms on GPUs with ease. To achieve better performance, we propose novel GPU-centric optimizations by leveraging the community as well as power-law properties of large graphs. Further, we significantly reduce the expensive data transfer cost between CPUs and GPUs by enablingLPprocessing on compressed graphs. Extensive experiments have confirmed the effectiveness of our proposed approaches over the state-of-the-art GPU methods. Furthermore, our proposed solution supports a real billion-scale graph workload for fraud detection and achieves 13.2× speedup to the current in-house solution running on a high-end multicore machine with compressed graphs.

Human spatial attention conveys information about the regions of visual scenes that are important for performing visual tasks. Prior work has shown that the information about human attention can be leveraged to benefit various supervised vision tasks. Might providing this weak form of supervision be useful for self-supervised representation learning? Addressing this question requires collecting large datasets with human attention labels. Yet, collecting such large scale data is very expensive. To address this challenge, we construct an auxiliary teacher model to predict human attention, trained on a relatively small labeled dataset. This teacher model allows us to generate image (pseudo) attention labels for ImageNet. We then train a model with a primary contrastive objective; to this standard configuration, we add a simple output head trained to predict the attention map for each image, guided by the pseudo labels from teacher model. We measure the quality of learned representations by evaluating classification performance from the frozen learned embeddings as well as performance on image retrieval tasks (see supplementary material). We find that the spatial-attention maps predicted from the contrastive model trained with teacher guidance aligns better with human attention compared to vanilla contrastive models. Moreover, we find that our approach improves classification accuracy and robustness of the contrastive models on ImageNet and ImageNet-C. Further, we find that model representations become more useful for image retrieval task as measured by precision-recall performance on ImageNet, ImageNet-C, CIFAR10, and CIFAR10-C datasets.

CleanRL is an open-source library that provides high-quality single-file implementations of Deep Reinforcement Learning (DRL) algorithms. These single-file implementations are self-contained algorithm variant files such as dqn.py, ppo.py, and ppo_atari.py that individually include all algorithm variant's implementation details. Such a paradigm significantly reduces the complexity and the lines of code (LOC) in each implemented variant, which makes them quicker and easier to understand. This paradigm gives the researchers the most fine-grained control over all aspects of the algorithm in a single file, allowing them to prototype novel features quickly. Despite having succinct implementations, CleanRL's codebase is thoroughly documented and benchmarked to ensure performance is on par with reputable sources. As a result, CleanRL produces a repository tailor-fit for two purposes: 1) understanding all implementation details of DRL algorithms and 2) quickly prototyping novel features. CleanRL's source code can be found at https://github.com/vwxyzjn/cleanrl.

On-demand meal delivery (ODMD) platforms such as DoorDash and Ele.me have experienced explosive growth in recent years. Effective logistics optimization strategies that could guarantee high service standards with controlled costs are crucial for the long-term sustainability of these platforms, and yet are also non-trivial due to the nature of ODMD operations. In particular, most of the orders are not known until they are placed by the customers, and any dispatching policy that only considers known requests would risk making myopic decisions in such a setting.

Fraud detection is a pressing challenge for most financial and commercial platforms. In this paper, we study the processing pipeline of fraud detection in a large e-commerce platform of TaoBao. Graph label propagation (LP) is a core component in this pipeline to detect suspicious clusters from the user-interaction graph.Furthermore, the run-time of the LP component occupies 75% overhead of TaoBao's automated detection pipeline. To enable real-time fraud detection, we propose a GPU-based framework, called GLP, to support large-scale LP workloads in enterprises.We have identified two key challenges when integrating GPU acceleration into TaoBao's data processing pipeline: (1)programmability for evolving fraud detection logics; (2)demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. We propose novel GPU-centric optimizations by leveraging the community as well as power-law properties of large graphs. Extensive experiments have confirmed the effectiveness of our proposed optimizations. With a single GPU, GLP supports a real billion-scale graph workload from the fraud detection pipeline of TaoBao and achieves 8.2x speedup to the current in-house distributed solution running on high-end multicore machines.

Deep Reinforcement Learning (DRL) has shown impressive performance on domains with visual inputs, in particular various games. However, the agent is usually trained on a fixed environment, e.g. a fixed number of levels. A growing mass of evidence suggests that these trained models fail to generalize to even slight variations of the environments they were trained on. This paper advances the hypothesis that the lack of generalization is partly due to the input representation, and explores how rotation, cropping and translation could increase generality. We show that a cropped, translated and rotated observation can get better generalization on unseen levels of two-dimensional arcade games from the GVGAI framework. The generality of the agents is evaluated on both human-designed and procedurally generated levels.