Approximation

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  1. NeuralChaos: Optimal Adapted Approximation of Square Integrable Predictable Processes

    Jul 15, 2026Anastasis Kratsios, Giulia Livieri, Philipp SchmockerNeural-Network ApproximationStochastic Systems

  2. Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

    Jul 15, 2026Hao Qin, Chicheng ZhangGeneralized Linear BanditCombinations

  3. Approximation of solutions of parameter-dependent problems by residual neural networks

    Jul 15, 2026Ana CarpioNeural-Network ApproximationNeural Network

  4. Graph Partitioning with Demands: Generalized Conductance and its Applications

    Jul 14, 2026Michał Szyfelbein, Dariusz DereniowskiMaximum Independent SetApproximation Algorithms

  5. Hierarchical F\mathcal{F}-Clustering: Approximation and Hardness of Clustering into Trees and Bounded Diameter Graphs

    Jul 14, 2026Michał Szyfelbein, Dariusz DereniowskiImage ClusteringApproximation Algorithms

  6. Deep Learning-based Surrogate Modelling of the LOD Method for Multiscale Problems

    Jul 14, 2026Marc Haltmayer, Jaemin Seo, Yuseung Lee +3Multiscale DynamicsBoundary Value Problems

  7. Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width

    Jul 12, 2026Yanming Lai, Defeng Sun, Yang WangRectified Linear Unit NetworksNeural-Network Approximation

  8. An optimal control approach for neural network architecture adaptation with a posteriori error estimation

    Jul 8, 2026C G Krishnanunni, Thomas Scott, Tan Bui-ThanhNeural-Network ApproximationNeural Network Optimization

  9. On Explicit Super-Expressive Approximation for Neural Networks

    Jul 7, 2026Feng-Lei Fan, Ze-Yu Li, Chen-Yu Wang +1Neural-Network ApproximationApproximation

  10. The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression

    Jul 7, 2026Stephen MussmannActive LearningEmpirical Risk Minimization

  11. A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems

    Jul 7, 2026Fabian Schneider, Tapio Helin, Leila TaghizadehIll-Posed Inverse ProblemNeural-Network Approximation

  12. Data-dependent Evaluations for Budgeted Submodular Maximization

    Jul 7, 2026Lejian Zhang, Xueyan Tang, Jing TangSubmodular MaximizationKnapsack Constraint

  13. Non-asymptotic Convergence of Stochastic Gradient Descent in Score-based Generative Models

    Jul 6, 2026Stanislas Strasman, Sobihan Surendran, Sylvain Le CorffStochastic Gradient DescentGenerative Models

  14. Minimum Block Width for Universal Approximation by Residual Neural Networks with Inner Width One

    Jul 6, 2026Qi Zhou, Xuan Zhou, Xiao-Song YangNeural-Network ApproximationResidual Connections

  15. Foundations of Equivariant Deep Learning: Unifying Graph and Sheaf Neural Networks

    Jul 4, 2026Yoshihiro MaruyamaEquivariant Neural NetworksGeometric Deep Learning

  16. TSP with Predictions: Heatmap to Tour with Provable Guarantees

    Jul 4, 2026Marek Eliáš, Fabrizio Grandoni, Adam Polak +1Salesman ProblemApproximation Algorithms

  17. Privacy-Preserving and Verifiable Approximate Distributed Coded Computing

    Jul 2, 2026Xavier Martínez-Luaña, Alba Gude-Santos, Manuel Fernández-Veiga +1Privacy-Preserving FrameworkFederated Learning

  18. Scalable and Distributed Silhouette Approximation

    Jul 2, 2026Ilie Sarpe, Federico Altieri, Andrea Pietracaprina +2Image ClusteringApproximation Algorithms

  19. From Approximation to Emergence: A Theory of Deep Learning

    Jul 1, 2026Zhilin ZhaoOver-Parameterized Neural NetworksDeep Learning

  20. From Spectral Methods to Sample Complexity Bounds for Fourier Neural Operators

    Jul 1, 2026Nisha Chandramoorthy, Daniel Sanz-Alonso, Nathan WaniorekFourier Neural OperatorsNeural Operators

  21. Domain-Decomposed Randomized Neural Networks for Partial Differential Equations in Unbounded Domains

    Jun 30, 2026Haixin Wang, Haoning Dang, Fei Wang +1Partial Differential EquationsBayesian Quadrature

  22. Fast approximation and learning of binary classification tasks in o-minimal structures using ReLU neural networks

    Jun 29, 2026Clemens Kinn, Philipp PetersenRectified Linear Unit NetworksBinary Classification

  23. Harvesting AI Computation at the Edge via Generic Approximation

    Jun 28, 2026Yihan Wang, Huiru Yan, Luxin Zhang +6Heterogeneous Edge DevicesCentral Processing Units

  24. A Posteriori Error Analysis for Decoupled Neural Approximations of Fully Coupled FBSDEs with Control Mismatch

    Jun 28, 2026Xichuan ZhangStochastic Differential EquationsNeural-Network Approximation

  25. Ribbon: Scalable Approximation and Robust Uncertainty Quantification

    Jun 25, 2026Graham Gibson, John Tipton, Kellin Rumsey +1Reliable Uncertainty QuantificationBayesian

  26. Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

    Jun 25, 2026Anastasis Kratsios, Simone Brugiapaglia, Bum Jun Kim +2Neural-Network ApproximationApproximation

  27. Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling

    Jun 25, 2026Ziyan Chen, Zhongzhu Zhou, Ding-Xuan ZhouContrastive LearningScaling Laws

  28. Compiler-Driven Approximation Tuning for Hyperdimensional Computing

    Jun 25, 2026Xavier Routh, Abdul Rafae Noor, Akash Kothari +4Code OptimizationApproximation

  29. An Analysis of Posterior Collapse, Parameterization and Initialization in Variational Deep Gaussian Processes

    Jun 24, 2026Francisco Javier Sáez-Maldonado, Juan Maroñas, Daniel Hernández-LobatoVariational InferenceVariational

  30. Dynamic estimation of slowly varying sequences

    Jun 22, 2026Prashant Gokhale, Mikhail Khodak, Sandeep SilwalEstimatorsTime-Varying Systems

  31. EML Trees Are Universal Approximators

    Jun 22, 2026Joe Germany, Elie Abdo, Joseph BakarjiNeural-Network ApproximationApproximation

  32. On the Expressive Power of Weight Quantization in Large Language Models

    Jun 20, 2026Shao-Qun ZhangLarge Language Model QuantizationQuantization

  33. Recurrent neural networks approximate continuous functions

    Jun 18, 2026Valentin Abadie, Clemens Hutter, Helmut BölcskeiNeural-Network ApproximationRecurrent Neural Networks

  34. Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures

    Jun 18, 2026Xinhe Mu, Zaijiu Shang, Zhaoqi Zhou +4Score-Based Diffusion ModelLow-Dimensional Structure

  35. Learning universal approximations for partial differential equations with Physics-Informed Broad Learning System

    Jun 18, 2026Zhiwen Yu, Derong Yang, Liujian Zhang +5Partial Differential Equation BenchmarksPartial Differential Equations

  36. LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms

    Jun 17, 2026Masaya Hagai, Yuta Suzuki, Tomoya Murata +2Computational ChemistryQuantum Approximate Optimization Algorithm

  37. Generalization Guarantees for Multi-Input Neural Operator Learning in Sobolev Spaces

    Jun 16, 2026Yahong Yang, Zecheng Zhang, Wei Zhu +2Logarithmic Sobolev InequalitiesProbabilistic Operator Learning

  38. Finsler Geometry, Graph Neural Networks, and You

    Jun 15, 2026T. Mitchell Roddenberry, Richard G. BaraniukGraph LaplaciansGraph Neural Networks

  39. Sobolev Approximation by Fixed-Size Neural Networks with Arbitrary Accuracy

    Jun 15, 2026Baicheng Li, Haizhao Yang, Shijun ZhangNeural-Network ApproximationLogarithmic Sobolev Inequalities

  40. Closing the Approximation Gap in Simulation-free Latent SDEs

    Jun 15, 2026Henry D. Smith, Brian L. Trippe, Scott W. LindermanStochastic Differential EquationsVariational Inference

  41. The Information-Theoretic Benefit of Shared Representations under Orthogonality Constraints

    Jun 14, 2026Thomas Dittrich, Oliver Potocki, Philipp GrohsNeural-Network ApproximationInformation-Theoretic Limits

  42. The Data Manifold under the Microscope

    Jun 14, 2026Marios Koulakis, Constantin SeiboldStatistical ManifoldManifolds

  43. From Meta Idea to Advanced Mathematical Discovery -- Human-AI Co-Discovery of Sign-Embedding Quantum Algorithms

    Jun 12, 2026Yanqiao Wang, Jin-Peng Liu, Peng Li +1Quantum AdvantageHuman-Ai Collaboration

  44. Approximating Gaussian Whittle-Matern Fields over Well-Centered Triangulations of Riemannian Manifolds

    Jun 11, 2026Srinivas NambirajanRiemannian ManifoldsKernel Hilbert Spaces

  45. Learning-Augmented Approximation for Unrelated-Machines Makespan Scheduling

    Jun 11, 2026Kaito Baba, Evripidis Bampis, Giorgos MitropoulosApproximation AlgorithmsApproximation

  46. Multi-Objective Coevolution of Prompts and Templates for Circuit Approximation

    Jun 11, 2026Martin Tomasovic, Lukas SekaninaEvolutionary SearchCircuits

  47. Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning

    Jun 10, 2026Jeongho Bang, Kyoungho Cho, Jeongwoo JaeQuantum Machine LearningOptimal Sample Complexity

  48. SirenFNO: Efficient and Full Frequency Learning of Fourier Neural Operators

    Jun 9, 2026Pengqing Shi, Jie Yin, Stephen Tierney +1Fourier Neural OperatorsNeural Operators

  49. Convergence Rates for Neural-Network Estimation with Current-Status Data

    Jun 8, 2026Yuan Wu, Tianhui ZhouTime-To-Event DataNeural-Network Approximation

  50. Weighted universal approximation of differentiable maps on infinite-dimensional manifolds

    Jun 8, 2026Philipp Schmocker, Josef TeichmannNeural-Network ApproximationApproximation

  51. SPARX: Secure and Privacy-Aware Approximate CNN Acceleration with Edge RISC-V SoC

    Jun 8, 2026Sonu Kumar, Akash Sankhe, Mukul Lokhande +1Sparse Deep Neural Network AcceleratorsRisc-V

  52. Fourier Neural Operators with rank-1 lattice points and hyperbolic cross

    Jun 7, 2026Jakob Dilen, Alexander Keller, Frances Y. Kuo +1Fourier Neural OperatorsFourier

  53. Compositional Approximation Can Strictly Outperform Superpositional Approximation

    Jun 7, 2026Dennis Elbrächter, Philipp PetersenApproximationEntropy Coding

  54. Where the Score Lives: A Wavelet View of Diffusion

    Jun 6, 2026Emma Finn, Binxu Wang, T. Anderson Keller +1Score-Based Diffusion ModelDiscrete Diffusion Model

  55. Online KL-Regularized Reinforcement Learning with Function Approximation under Misspecification

    Jun 4, 2026Haoyang Hong, Zichen Wang, Quanquan Gu +1Kullback-Leibler RegularizationLinear Contextual Bandits

  56. When Good Enough Is Optimal: Multiplication-Only Matrix Inversion Approximation for Quantized Gated DeltaNet

    Jun 4, 2026Luoming Zhang, Yuwei Ren, Kui Zhang +7Matrix MultiplicationQuadratic Attention

  57. A General Framework for Dynamic Consistent Submodular Maximization

    Jun 3, 2026Paul Dütting, Federico Fusco, Silvio Lattanzi +3Submodular MaximizationApproximation Algorithms

  58. Fitting scattered data with optional monotonicity constraints on GPU: LipFit package

    Jun 3, 2026Gleb BeliakovLipschitz ConstantInterpolation

  59. Learning symplectic model reduction based on a approximation theorem of symplectic embeddings

    Jun 3, 2026Liyi Feng, Yifa Tang, Yulin Xie +2Reduced-Order ModelsHamiltonian Physics

  60. Shortcomings and capacities of real-constrained neural networks in complex spaces

    Jun 3, 2026Andrew GracykNeural-Network ApproximationNeural Network