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1. A dimension-reduced variational approach for solving physics-based inverse problems using generative adversarial network priors and normalizing flows

   https://doi.org/10.1016/j.cma.2023.116682  

   Dasgupta, Agnimitra; Patel, Dhruv V; Ray, Deep; Johnson, Erik A; Oberai, Assad A (February 2024, Computer Methods in Applied Mechanics and Engineering)

   We propose a novel modular inference approach combining two different generative models — generative adversarial networks (GAN) and normalizing flows — to approximate the posterior distribution of physics-based Bayesian inverse problems framed in high-dimensional ambient spaces. We dub the proposed framework GAN-Flow. The proposed method leverages the intrinsic dimension reduction and superior sample generation capabilities of GANs to define a low-dimensional data-driven prior distribution. Once a trained GAN-prior is available, the inverse problem is solved entirely in the latent space of the GAN using variational Bayesian inference with normalizing flow-based variational distribution, which approximates low-dimensional posterior distribution by transforming realizations from the low-dimensional latent prior (Gaussian) to corresponding realizations of a low-dimensional variational posterior distribution. The trained GAN generator then maps realizations from this approximate posterior distribution in the latent space back to the high-dimensional ambient space. We also propose a two-stage training strategy for GAN-Flow wherein we train the two generative models sequentially. Thereafter, GAN-Flow can estimate the statistics of posterior-predictive quantities of interest at virtually no additional computational cost. The synergy between the two types of generative models allows us to overcome many challenges associated with the application of Bayesian inference to large-scale inverse problems, chief among which are describing an informative prior and sampling from the high-dimensional posterior. GAN-Flow does not involve Markov chain Monte Carlo simulation, making it particularly suitable for solving large-scale inverse problems. We demonstrate the efficacy and flexibility of GAN-Flow on various physics-based inverse problems of varying ambient dimensionality and prior knowledge using different types of GANs and normalizing flows. Notably, one of the applications we consider involves a 65,536-dimensional inverse problem of phase retrieval wherein an object is reconstructed from sparse noisy measurements of the magnitude of its Fourier transform. 

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2. Generating Realistic Stock Market Order Streams

   https://doi.org/10.1609/aaai.v34i01.5415  

   Li, Junyi; Wang, Xintong; Lin, Yaoyang; Sinha, Arunesh; Wellman, Michael (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data. 

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3. P4DDPI: Securing P4-Programmable Data Plane Networks via DNS Deep Packet Inspection

   AlSabeh, Ali; Kfoury, Elie; Crichigno, Jorge; Bou-Harb, Elias (April 2022, NDSS Symposium 2022)

   One of the main roles of the Domain Name System (DNS) is to map domain names to IP addresses. Despite the importance of this function, DNS traffic often passes without being analyzed, thus making the DNS a center of attacks that keep evolving and growing. Software-based mitigation approaches and dedicated state-of-the-art firewalls can become a bottleneck and are subject to saturation attacks, especially in high-speed networks. The emerging P4-programmable data plane can implement a variety of network security mitigation approaches at high-speed rates without disrupting legitimate traffic. This paper describes a system that relies on programmable switches and their stateful processing capabilities to parse and analyze DNS traffic solely in the data plane, and subsequently apply security policies on domains according to the network administrator. In particular, Deep Packet Inspection (DPI) is leveraged to extract the domain name consisting of any number of labels and hence, apply filtering rules (e.g., blocking malicious domains). Evaluation results show that the proposed approach can parse more domain labels than any state-of-the-art P4-based approach. Additionally, a significant performance gain is attained when comparing it to a traditional software firewall -pfsense-, in terms of throughput, delay, and packet loss. The resources occupied by the implemented P4 program are minimal, which allows for more security functionalities to be added. 

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4. C3-GAN+: Complex-Condition-Controlled Generative Adversarial Networks with Enhanced Embedding

   https://doi.org/10.1145/3712264  

   Zhang, Yingxue; Li, Yanhua; Zhou, Xun; Liu, Zhenming; Luo, Jun (February 2025, ACM Transactions on Knowledge Discovery from Data)

   Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity, and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. Recently, we proposed a Complex-Condition-Controlled Generative Adversarial Network C3-GAN, which tackles both of the challenges and solves the urban traffic estimation problem under various complex conditions by adding a fixed embedding network and an inference network on top of the standard conditional GAN model. The randomly chosen embedding network transforms the complex conditions to latent vectors, and the inference network enhances the connections between the embedded vectors and the traffic data. However, a randomly chosen embedding network cannot always successfully extract features of complex urban conditions, which indicates C3-GAN is unable to uniquely map different urban conditions to proper latent distributions. Thus, C3-GAN would fail in certain traffic estimation tasks. Besides, C3-GAN is hard to train due to vanishing gradients and mode collapse problems. To address these issues, in this article, we extend our prior work by introducing a new deep generative model, namely, C3-GAN+, which significantly improves the estimation performance and model stability. C3-GAN+ has new objective, architecture, and training algorithm. The new objective applies Wasserstein loss to the conditional generation case to encourage stable training. Shared convolutional layers between the discriminator and the inference network help to capture spatial dependencies of traffic more efficiently, part of the shared convolutional layers are used to update the embedding network periodically aiming to encourage good representation and avoid model divergence. Extensive experiments on real-world datasets demonstrate that our C3-GAN+ produces high-quality traffic estimations and outperforms state-of-the-art baseline methods. 

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5. SPLIDT: Partitioned Decision Trees for Scalable Stateful Inference at Line Rate

   Parvez, Murayyiam; Zulfiqar, Annus; Beltiukov, Roman; Landau_Feibish, Shir; Willinger, Walter; Gupta, Arpit; Shahbaz, Muhammad (May 2026, 23rd USENIX Symposium on Networked Systems Design and Implementation (NSDI '26))

   Machine learning (ML) is increasingly being deployed in programmable data planes (switches and SmartNICs) to enable real-time traffic analysis, security monitoring, and in-network decision-making. Decision trees (DTs) are particularly well-suited for these tasks due to their interpretability and compatibility with data-plane architectures, i.e., match-action tables (MATs). However, existing in-network DT implementations are constrained by the need to compute all input features upfront, forcing models to rely on a small, fixed set of features per flow. This significantly limits model accuracy and scalability under stringent hardware resource constraints. We present SPLIDT, a system that rethinks DT deployment in the data plane by enabling partitioned inference over sliding windows of packets. SPLIDT introduces two key innovations: (1) it groups individual subtrees of a DT into partitions and allows each subtree to have its own feature set, and (2) it leverages an in-band control channel (via recirculation) to reuse data-plane resources (both stateful registers and match keys) across partitions at line rate. These insights allow SPLIDT to scale the number of stateful features a model can use without exceeding hardware limits. To support this architecture, SPLIDT incorporates a custom training and design-space exploration (DSE) framework that jointly optimizes feature allocation, tree partitioning, and DT model depth. Evaluation across multiple real-world datasets shows that SPLIDT achieves higher accuracy while supporting up to 5x more stateful features than prior approaches (e.g., NetBeacon and Leo). It maintains the same low time-to-detection (TTD) as these systems, while scaling to millions of flows with minimal recirculation overhead (≤ 0.05%). 

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