Author name cluster

Xiongye Xiao

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8 papers

2 author rows

ICML Conference 2025 Conference Paper

End-to-End Learning Framework for Solving Non-Markovian Optimal Control

Xiaole Zhang
Peiyu Zhang 0002
Xiongye Xiao
Shixuan Li
Vasileios Tzoumas
Vijay Gupta
Paul Bogdan

Integer-order calculus fails to capture the long-range dependence (LRD) and memory effects found in many complex systems. Fractional calculus addresses these gaps through fractional-order integrals and derivatives, but fractional-order dynamical systems pose substantial challenges in system identification and optimal control tasks. In this paper, we theoretically derive the optimal control via linear quadratic regulator (LQR) for fractional-order linear time-invariant (FOLTI) systems and develop an end-to-end deep learning framework based on this theoretical foundation. Our approach establishes a rigorous mathematical model, derives analytical solutions, and incorporates deep learning to achieve data-driven optimal control of FOLTI systems. Our key contributions include: (i) proposing a novel method for system identification and optimal control strategy in FOLTI systems, (ii) developing the first end-to-end data-driven learning framework, Fractional-Order Learning for Optimal Control (FOLOC), that learns control policies from observed trajectories, and (iii) deriving theoretical bounds on the sample complexity for learning accurate control policies under fractional-order dynamics. Experimental results indicate that our method accurately approximates fractional-order system behaviors without relying on Gaussian noise assumptions, pointing to promising avenues for advanced optimal control.

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NeurIPS Conference 2025 Conference Paper

MIHC: Multi-View Interpretable Hypergraph Neural Networks with Information Bottleneck for Chip Congestion Prediction

Zeyue Zhang
Heng Ping
Peiyu Zhang
Nikos Kanakaris
Xiaoling LU
Paul Bogdan
Xiongye Xiao

With AI advancement and increasing circuit complexity, efficient chip design through Electronic Design Automation (EDA) is critical. Fast and accurate congestion prediction in chip layout and routing can significantly enhance automated design performance. Existing congestion modeling methods are limited by (i) ineffective processing and fusion of multi-view circuit data information, and (ii) insufficient reliability and interpretability in the prediction process. To address these challenges, We propose M ulti-view I nterpretable H ypergraph for C hip ( MIHC ), a trustworthy 'multi-view hypergraph neural network'-based framework that (i) processes both graph and image information in unified hypergraph representations, capturing topological and geometric circuit data, and (ii) implements a novel subgraph Information Bottleneck mechanism identifying critical congestion-correlated regions to guide predictions. This represents the first attempt to incorporate such interpretability into congestion prediction through informative graph reasoning. Experiments show our model reduces NMAE by 16. 67% and 8. 57% in cell-based and grid-based predictions on ISPD2015, and 5. 26% and 2. 44% on CircuitNet-N28, respectively, compared to state-of-the-art methods. Rigorous cross-design generalization experiments further validate our method’s capability to handle entirely unseen circuit designs.

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ICLR Conference 2025 Conference Paper

Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models

Xiongye Xiao
Heng Ping
Chenyu Zhou
Defu Cao
Yaxing Li
Yizhuo Zhou
Shixuan Li
Nikos Kanakaris

In recent years, there has been increasing attention on the capabilities of large-scale models, particularly in handling complex tasks that small-scale models are unable to perform. Notably, large language models (LLMs) have demonstrated ``intelligent'' abilities such as complex reasoning and abstract language comprehension, reflecting cognitive-like behaviors. However, current research on emergent abilities in large models predominantly focuses on the relationship between model performance and size, leaving a significant gap in the systematic quantitative analysis of the internal structures and mechanisms driving these emergent abilities. Drawing inspiration from neuroscience research on brain network structure and self-organization, we propose (i) a general network representation of large models, (ii) a new analytical framework — *Neuron-based Multifractal Analysis (NeuroMFA)* - for structural analysis, and (iii) a novel structure-based metric as a proxy for emergent abilities of large models. By linking structural features to the capabilities of large models, *NeuroMFA* provides a quantitative framework for analyzing emergent phenomena in large models. Our experiments show that the proposed method yields a comprehensive measure of the network's evolving heterogeneity and organization, offering theoretical foundations and a new perspective for investigating emergence in large models.

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NeurIPS Conference 2024 Conference Paper

A Structure-Aware Framework for Learning Device Placements on Computation Graphs

Shukai Duan
Heng Ping
Nikos Kanakaris
Xiongye Xiao
Panagiotis Kyriakis
Nesreen K. Ahmed
Peiyu Zhang
Guixiang Ma

Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing approaches rely on two types of architectures known as grouper-placer and encoder-placer, respectively. In this work, we bridge the gap between encoder-placer and grouper-placer techniques and propose a novel framework for the task of device placement, relying on smaller computation graphs extracted from the OpenVINO toolkit. The framework consists of five steps, including graph coarsening, node representation learning and policy optimization. It facilitates end-to-end training and takes into account the DAG nature of the computation graphs. We also propose a model variant, inspired by graph parsing networks and complex network analysis, enabling graph representation learning and jointed, personalized graph partitioning, using an unspecified number of groups. To train the entire framework, we use reinforcement learning using the execution time of the placement as a reward. We demonstrate the flexibility and effectiveness of our approach through multiple experiments with three benchmark models, namely Inception-V3, ResNet, and BERT. The robustness of the proposed framework is also highlighted through an ablation study. The suggested placements improve the inference speed for the benchmark models by up to $58. 2\%$ over CPU execution and by up to $60. 24\%$ compared to other commonly used baselines.

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ICLR Conference 2024 Conference Paper

Neuro-Inspired Information-Theoretic Hierarchical Perception for Multimodal Learning

Xiongye Xiao
Gengshuo Liu
Gaurav Gupta
Defu Cao
Shixuan Li
Yaxing Li
Tianqing Fang
Mingxi Cheng

Integrating and processing information from various sources or modalities are critical for obtaining a comprehensive and accurate perception of the real world in autonomous systems and cyber-physical systems. Drawing inspiration from neuroscience, we develop the Information-Theoretic Hierarchical Perception (ITHP) model, which utilizes the concept of information bottleneck. Different from most traditional fusion models that incorporate all modalities identically in neural networks, our model designates a prime modality and regards the remaining modalities as detectors in the information pathway, serving to distill the flow of information. Our proposed perception model focuses on constructing an effective and compact information flow by achieving a balance between the minimization of mutual information between the latent state and the input modal state, and the maximization of mutual information between the latent states and the remaining modal states. This approach leads to compact latent state representations that retain relevant information while minimizing redundancy, thereby substantially enhancing the performance of multimodal representation learning. Experimental evaluations on the MUStARD, CMU-MOSI, and CMU-MOSEI datasets demonstrate that our model consistently distills crucial information in multimodal learning scenarios, outperforming state-of-the-art benchmarks. Remarkably, on the CMU-MOSI dataset, ITHP surpasses human-level performance in the multimodal sentiment binary classification task across all evaluation metrics (i.e., Binary Accuracy, F1 Score, Mean Absolute Error, and Pearson Correlation).

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ICLR Conference 2023 Conference Paper

Coupled Multiwavelet Operator Learning for Coupled Differential Equations

Xiongye Xiao
Defu Cao
Ruochen Yang
Gaurav Gupta
Gengshuo Liu
Chenzhong Yin
Radu Balan
Paul Bogdan

Coupled partial differential equations (PDEs) are key tasks in modeling the complex dynamics of many physical processes. Recently, neural operators have shown the ability to solve PDEs by learning the integral kernel directly in Fourier/Wavelet space, so the difficulty of solving the coupled PDEs depends on dealing with the coupled mappings between the functions. Towards this end, we propose a \textit{coupled multiwavelets neural operator} (CMWNO) learning scheme by decoupling the coupled integral kernels during the multiwavelet decomposition and reconstruction procedures in the Wavelet space. The proposed model achieves significantly higher accuracy compared to previous learning-based solvers in solving the coupled PDEs including Gray-Scott (GS) equations and the non-local mean field game (MFG) problem. According to our experimental results, the proposed model exhibits a $2X-4X$ improvement relative $L$2 error compared to the best results from the state-of-the-art models.

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ICLR Conference 2022 Conference Paper

Non-Linear Operator Approximations for Initial Value Problems

Gaurav Gupta
Xiongye Xiao
Radu Balan
Paul Bogdan

Time-evolution of partial differential equations is the key to model several dynamical processes, events forecasting but the operators associated with such problems are non-linear. We propose a Padé approximation based exponential neural operator scheme for efficiently learning the map between a given initial condition and activities at a later time. The multiwavelets bases are used for space discretization. By explicitly embedding the exponential operators in the model, we reduce the training parameters and make it more data-efficient which is essential in dealing with scarce real-world datasets. The Padé exponential operator uses a $\textit{recurrent structure with shared parameters}$ to model the non-linearity compared to recent neural operators that rely on using multiple linear operator layers in succession. We show theoretically that the gradients associated with the recurrent Padé network are bounded across the recurrent horizon. We perform experiments on non-linear systems such as Korteweg-de Vries (KdV) and Kuramoto–Sivashinsky (KS) equations to show that the proposed approach achieves the best performance and at the same time is data-efficient. We also show that urgent real-world problems like Epidemic forecasting (for example, COVID-19) can be formulated as a 2D time-varying operator problem. The proposed Padé exponential operators yield better prediction results ($\textbf{53\%} (\textbf{52\%})$ better MAE than best neural operator (non-neural operator deep learning model)) compared to state-of-the-art forecasting models.

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NeurIPS Conference 2021 Conference Paper

Multiwavelet-based Operator Learning for Differential Equations

Gaurav Gupta
Xiongye Xiao
Paul Bogdan

The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space. Towards this end, we introduce a $\textit{multiwavelet-based neural operator learning scheme}$ that compresses the associated operator's kernel using fine-grained wavelets. By explicitly embedding the inverse multiwavelet filters, we learn the projection of the kernel onto fixed multiwavelet polynomial bases. The projected kernel is trained at multiple scales derived from using repeated computation of multiwavelet transform. This allows learning the complex dependencies at various scales and results in a resolution-independent scheme. Compare to the prior works, we exploit the fundamental properties of the operator's kernel which enable numerically efficient representation. We perform experiments on the Korteweg-de Vries (KdV) equation, Burgers' equation, Darcy Flow, and Navier-Stokes equation. Compared with the existing neural operator approaches, our model shows significantly higher accuracy and achieves state-of-the-art in a range of datasets. For the time-varying equations, the proposed method exhibits a ($2X-10X$) improvement ($0. 0018$ ($0. 0033$) relative $L2$ error for Burgers' (KdV) equation). By learning the mappings between function spaces, the proposed method has the ability to find the solution of a high-resolution input after learning from lower-resolution data.

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