{"total":132,"rows":[{"id":"2310.10688","title":"A decoder-only foundation model for time-series forecasting","created":"2023-10-14","cited_by_count":692,"area":null},{"id":"2310.07820","title":"Large Language Models Are Zero-Shot Time Series Forecasters","created":"2023-10-11","cited_by_count":500,"area":null},{"id":"2105.06868","title":"Priors in Bayesian Deep Learning: A Review","created":"2021-05-14","cited_by_count":101,"area":null},{"id":"2101.07667","title":"Few-Shot Bayesian Optimization with Deep Kernel Surrogates","created":"2021-01-19","cited_by_count":71,"area":null},{"id":"2304.05366","title":"The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning","created":"2023-04-11","cited_by_count":63,"area":null},{"id":"2202.11678","title":"Bayesian Model Selection, the Marginal Likelihood, and Generalization","created":"2022-02-23","cited_by_count":57,"area":null},{"id":"2503.02113","title":"Deep Learning is Not So Mysterious or Different","created":"2025-03-03","cited_by_count":56,"area":null},{"id":"2106.01309","title":"Benchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains","created":"2021-05-23","cited_by_count":48,"area":null},{"id":"2102.05829","title":"Causal Inference for Time series Analysis: Problems, Methods and Evaluation","created":"2021-02-11","cited_by_count":42,"area":null},{"id":"2402.17879","title":"Automated Statistical Model Discovery with Language Models","created":"2024-02-27","cited_by_count":41,"area":null},{"id":"2406.06248","title":"Compute Better Spent: Replacing Dense Layers with Structured Matrices","created":"2024-06-10","cited_by_count":28,"area":null},{"id":"2105.04504","title":"Deep Neural Networks as Point Estimates for Deep Gaussian Processes","created":"2021-05-10","cited_by_count":26,"area":null},{"id":"2103.01454","title":"Kernel Interpolation for Scalable Online Gaussian Processes","created":"2021-03-02","cited_by_count":18,"area":null},{"id":"2109.02606","title":"Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications","created":"2021-09-06","cited_by_count":17,"area":null},{"id":"2212.14319","title":"Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients","created":"2022-12-29","cited_by_count":17,"area":null},{"id":"2509.17998","title":"Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs","created":"2025-09-22","cited_by_count":17,"area":null},{"id":"2102.07542","title":"High-Dimensional Gaussian Process Inference with Derivatives","created":"2021-02-15","cited_by_count":16,"area":null},{"id":"2207.05543","title":"Markovian Gaussian Process Variational Autoencoders","created":"2022-07-12","cited_by_count":16,"area":null},{"id":"2110.08323","title":"On Learning the Transformer Kernel","created":"2021-10-15","cited_by_count":15,"area":null},{"id":"2309.10656","title":"A spectrum of physics-informed Gaussian processes for regression in engineering","created":"2023-09-19","cited_by_count":14,"area":null},{"id":"2311.04465","title":"Solving High Frequency and Multi-Scale PDEs with Gaussian Processes","created":"2023-11-08","cited_by_count":14,"area":null},{"id":"2110.13561","title":"Non-Gaussian Gaussian Processes for Few-Shot Regression","created":"2021-10-26","cited_by_count":12,"area":null},{"id":"2205.14283","title":"Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2202.11154","title":"Parallel MCMC Without Embarrassing Failures","created":"2022-02-22","cited_by_count":11,"area":null},{"id":"2407.00809","title":"Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning","created":"2024-06-30","cited_by_count":11,"area":null},{"id":"2206.08366","title":"Scalable First-Order Bayesian Optimization via Structured Automatic Differentiation","created":"2022-06-16","cited_by_count":10,"area":null},{"id":"2310.03111","title":"Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data","created":"2023-10-04","cited_by_count":9,"area":null},{"id":"2106.05992","title":"Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition","created":"2021-06-10","cited_by_count":8,"area":null},{"id":"2202.09233","title":"Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures","created":"2022-02-18","cited_by_count":8,"area":null},{"id":"2206.15303","title":"Physics-informed machine learning for Structural Health Monitoring","created":"2022-06-30","cited_by_count":8,"area":null},{"id":"2210.11836","title":"Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport","created":"2022-10-21","cited_by_count":8,"area":null},{"id":"2112.09519","title":"Correlated Product of Experts for Sparse Gaussian Process Regression","created":"2021-12-17","cited_by_count":7,"area":null},{"id":"2407.21176","title":"DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks","created":"2024-07-30","cited_by_count":7,"area":null},{"id":"2106.06510","title":"Measuring the robustness of Gaussian processes to kernel choice","created":"2021-06-11","cited_by_count":6,"area":null},{"id":"2101.11751","title":"Faster Kernel Interpolation for Gaussian Processes","created":"2021-01-28","cited_by_count":5,"area":null},{"id":"2103.10134","title":"Recent Advances in Data-Driven Wireless Communication Using Gaussian Processes: A Comprehensive Survey","created":"2021-03-18","cited_by_count":5,"area":null},{"id":"2107.07098","title":"Hida-Mat\\'ern Kernel","created":"2021-07-15","cited_by_count":5,"area":null},{"id":"2208.06028","title":"Gaussian Process Surrogate Models for Neural Networks","created":"2022-08-11","cited_by_count":5,"area":null},{"id":"2301.02665","title":"Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space","created":"2023-01-06","cited_by_count":5,"area":null},{"id":"2301.08843","title":"Towards Flexibility and Interpretability of Gaussian Process State-Space Model","created":"2023-01-21","cited_by_count":5,"area":null},{"id":"2307.10052","title":"FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation","created":"2023-07-14","cited_by_count":5,"area":null},{"id":"2402.09758","title":"Extrapolation-Aware Nonparametric Statistical Inference","created":"2024-02-15","cited_by_count":5,"area":null},{"id":"2405.01365","title":"Dynamic Online Ensembles of Basis Expansions","created":"2024-05-02","cited_by_count":5,"area":null},{"id":"2405.15310","title":"Spectraformer: A Unified Random Feature Framework for Transformer","created":"2024-05-24","cited_by_count":5,"area":null},{"id":"2106.05582","title":"Learning Nonparametric Volterra Kernels with Gaussian Processes","created":"2021-06-10","cited_by_count":4,"area":null},{"id":"2106.07452","title":"Marginalising over Stationary Kernels with Bayesian Quadrature","created":"2021-06-14","cited_by_count":4,"area":null},{"id":"2108.02606","title":"Self-supervised optimization of random material microstructures in the small-data regime","created":"2021-08-05","cited_by_count":4,"area":null},{"id":"2306.08352","title":"Bayesian Non-linear Latent Variable Modeling via Random Fourier Features","created":"2023-06-14","cited_by_count":4,"area":null},{"id":"2404.01697","title":"Preventing Model Collapse in Gaussian Process Latent Variable Models","created":"2024-04-02","cited_by_count":4,"area":null},{"id":"2407.00397","title":"Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes","created":"2024-06-29","cited_by_count":4,"area":null},{"id":"2603.24654","title":"Spectral methods: crucial for machine learning, natural for quantum computers?","created":"2026-03-25","cited_by_count":4,"area":null},{"id":"2102.11249","title":"Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting","created":"2021-02-22","cited_by_count":3,"area":null},{"id":"2103.06950","title":"The Minecraft Kernel: Modelling correlated Gaussian Processes in the Fourier domain","created":"2021-03-11","cited_by_count":3,"area":null},{"id":"2212.10306","title":"Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting","created":"2022-12-20","cited_by_count":3,"area":null},{"id":"2301.05729","title":"GAR: Generalized Autoregression for Multi-Fidelity Fusion","created":"2023-01-13","cited_by_count":3,"area":null},{"id":"2302.09580","title":"Non-separable Covariance Kernels for Spatiotemporal Gaussian Processes based on a Hybrid Spectral Method and the Harmonic Oscillator","created":"2023-02-19","cited_by_count":3,"area":null},{"id":"2602.15472","title":"Fluids You Can Trust: Property-Preserving Operator Learning for Incompressible Flows","created":"2026-02-17","cited_by_count":3,"area":null},{"id":"2105.03781","title":"MetaKernel: Learning Variational Random Features with Limited Labels","created":"2021-05-08","cited_by_count":2,"area":null},{"id":"2106.07369","title":"A Self-Supervised Framework for Function Learning and Extrapolation","created":"2021-06-14","cited_by_count":2,"area":null},{"id":"2109.09692","title":"Modeling Regime Shifts in Multiple Time Series","created":"2021-09-20","cited_by_count":2,"area":null},{"id":"2203.06997","title":"Modelling Non-Smooth Signals with Complex Spectral Structure","created":"2022-03-14","cited_by_count":2,"area":null},{"id":"2204.12466","title":"Meta-free few-shot learning via representation learning with weight averaging","created":"2022-04-26","cited_by_count":2,"area":null},{"id":"2205.07384","title":"Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel","created":"2022-05-15","cited_by_count":2,"area":null},{"id":"2209.03117","title":"Non-Gaussian Process Regression","created":"2022-09-07","cited_by_count":2,"area":null},{"id":"2307.01872","title":"A hybrid machine learning framework for clad characteristics prediction in metal additive manufacturing","created":"2023-07-04","cited_by_count":2,"area":null},{"id":"2309.08201","title":"Sparsity-Aware Distributed Learning for Gaussian Processes with Linear Multiple Kernel","created":"2023-09-15","cited_by_count":2,"area":null},{"id":"2310.17023","title":"On the Identifiability and Interpretability of Gaussian Process Models","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2407.02476","title":"Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference","created":"2024-07-02","cited_by_count":2,"area":null},{"id":"2410.03581","title":"Nonstationary Sparse Spectral Permanental Process","created":"2024-10-04","cited_by_count":2,"area":null},{"id":"2503.13404","title":"Fed-Joint: Joint Modeling of Nonlinear Degradation Signals and Failure Events for Remaining Useful Life Prediction using Federated Learning","created":"2025-03-17","cited_by_count":2,"area":null},{"id":"2504.17804","title":"Spectral Dictionary Learning for Generative Image Modeling","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2602.00987","title":"Scalable Random Wavelet Features: Efficient Non-Stationary Kernel Approximation with Convergence Guarantees","created":"2026-02-01","cited_by_count":2,"area":null},{"id":"2102.05208","title":"Attentive Gaussian processes for probabilistic time-series generation","created":"2021-02-10","cited_by_count":1,"area":null},{"id":"2103.12165","title":"Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy","created":"2021-03-22","cited_by_count":1,"area":null},{"id":"2104.06204","title":"Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels","created":"2021-04-13","cited_by_count":1,"area":null},{"id":"2203.07411","title":"On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel","created":"2022-03-14","cited_by_count":1,"area":null},{"id":"2204.09889","title":"Inducing Gaussian Process Networks","created":"2022-04-21","cited_by_count":1,"area":null},{"id":"2205.09909","title":"Sparse Infinite Random Feature Latent Variable Modeling","created":"2022-05-20","cited_by_count":1,"area":null},{"id":"2206.08972","title":"Shallow and Deep Nonparametric Convolutions for Gaussian Processes","created":"2022-06-17","cited_by_count":1,"area":null},{"id":"2207.06544","title":"Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes","created":"2022-07-13","cited_by_count":1,"area":null},{"id":"2306.11487","title":"Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks","created":"2023-06-20","cited_by_count":1,"area":null},{"id":"2402.07139","title":"Towards Robust Car Following Dynamics Modeling via Blackbox Models: Methodology, Analysis, and Recommendations","created":"2024-02-11","cited_by_count":1,"area":null},{"id":"2412.00123","title":"Electricity Price Prediction Using Multi-Kernel Gaussian Process Regression Combined with Kernel-Based Support Vector Regression","created":"2024-11-28","cited_by_count":1,"area":null},{"id":"2502.08253","title":"Multi-View Oriented GPLVM: Expressiveness and Efficiency","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2503.08555","title":"An Analysis of Safety Guarantees in Multi-Task Bayesian Optimization","created":"2025-03-11","cited_by_count":1,"area":null},{"id":"2505.09026","title":"Probabilistic Wind Power Modelling via Heteroscedastic Non-Stationary Gaussian Processes","created":"2025-05-13","cited_by_count":1,"area":null},{"id":"2506.11055","title":"PolyMicros: Bootstrapping a Foundation Model for Polycrystalline Material Structure","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2506.16316","title":"Bayesian Optimization over Bounded Domains with the Beta Product Kernel","created":"2025-06-19","cited_by_count":1,"area":null},{"id":"2605.03262","title":"A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2102.09676","title":"Robust non-parametric mortality and fertility modelling and forecasting: Gaussian process regression approaches","created":"2021-02-18","cited_by_count":0,"area":null},{"id":"2107.06473","title":"Spectrum Gaussian Processes Based On Tunable Basis Functions","created":"2021-07-14","cited_by_count":0,"area":null},{"id":"2209.02912","title":"Optimal Sensor Placement in Body Surface Networks using Gaussian Processes","created":"2022-09-07","cited_by_count":0,"area":null},{"id":"2209.09397","title":"Partial sequence labeling with structured Gaussian Processes","created":"2022-09-20","cited_by_count":0,"area":null},{"id":"2210.05394","title":"Computationally-efficient initialisation of GPs: The generalised variogram method","created":"2022-10-11","cited_by_count":0,"area":null},{"id":"2211.10642","title":"On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process","created":"2022-11-19","cited_by_count":0,"area":null},{"id":"2307.16105","title":"TMPNN: High-Order Polynomial Regression Based on Taylor Map Factorization","created":"2023-07-30","cited_by_count":0,"area":null},{"id":"2310.12032","title":"Exact and general decoupled solutions of the LMC Multitask Gaussian Process model","created":"2023-10-18","cited_by_count":0,"area":null},{"id":"2403.09215","title":"On the Laplace Approximation as Model Selection Criterion for Gaussian Processes","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2404.12478","title":"A New Reliable & Parsimonious Learning Strategy Comprising Two Layers of Gaussian Processes, to Address Inhomogeneous Empirical Correlation Structures","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2406.00438","title":"Stein Random Feature Regression","created":"2024-06-01","cited_by_count":0,"area":null}],"next_cursor":"100"}