Gaussian Processes

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

5 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 685 papers with code (1,963 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
  6 Jun 2015 29 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 4 pointer-only (licence)
  In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost.
• Conditional Neural Processes
  4 Jul 2018 18 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function.
• Gaussian Processes for Big Data
  26 Sep 2013 9 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  We introduce stochastic variational inference for Gaussian process models.
• Doubly Stochastic Variational Inference for Deep Gaussian Processes
  24 May 2017 8 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice.
• Adversarial Robustness Toolbox v1.0.0
  3 Jul 2018 7 repositories listed
  Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging…
• Deep Neural Networks as Gaussian Processes
  1 Nov 2017 7 repositories listed
  As such, previous work has not identified that these kernels can be used as covariance functions for GPs and allow fully Bayesian prediction with a deep neural network.
• Chronos: Learning the Language of Time Series
  12 Mar 2024 6 repositories listed Syntology ran 13 of 28 samples · 15 unverified
  We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models.
• Neural Tangent Kernel: Convergence and Generalization in Neural Networks
  20 Jun 2018 6 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  While the NTK is random at initialization and varies during training, in the infinite-width limit it converges to an explicit limiting kernel and it stays constant during training.
• Efficiently Sampling Functions from Gaussian Process Posteriors
  21 Feb 2020 5 repositories listed
  Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty.
• Deep Kernel Learning
  6 Nov 2015 5 repositories listed Syntology ran 1 of 5 samples · 4 unverified · 1 pointer-only (licence)
  We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods.
• Pre-trained Gaussian Processes for Bayesian Optimization
  16 Sep 2021 4 repositories listed Syntology ran 3 of 14 samples · 11 unverified · 2 pointer-only (licence)
  Contrary to a common expectation that BO is suited to optimizing black-box functions, it actually requires domain knowledge about those functions to deploy BO successfully.
• GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
  28 Sep 2018 4 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.
• Differentiable Compositional Kernel Learning for Gaussian Processes
  12 Jun 2018 4 repositories listed Syntology ran 1 of 15 samples · 14 unverified
  The NKN architecture is based on the composition rules for kernels, so that each unit of the network corresponds to a valid kernel.
• Probabilistic Recurrent State-Space Models
  31 Jan 2018 4 repositories listed
  State-space models (SSMs) are a highly expressive model class for learning patterns in time series data and for system identification.
• Convolutional Gaussian Processes
  6 Sep 2017 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.
• Scalable Bayesian Optimization Using Deep Neural Networks
  19 Feb 2015 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations.
• Kernels for Vector-Valued Functions: a Review
  30 Jun 2011 4 repositories listed
  Kernel methods are among the most popular techniques in machine learning.
• Federated Causal Inference from Observational Data
  24 Aug 2023 3 repositories listed Syntology ran 4 of 6 samples · 2 unverified · 6 pointer-only (licence)
  In this article, we propose a framework to estimate causal effects from decentralized data sources.
• Isotropic Gaussian Processes on Finite Spaces of Graphs
  3 Nov 2022 3 repositories listed
  We propose a principled way to define Gaussian process priors on various sets of unweighted graphs: directed or undirected, with or without loops.
• TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity Analysis
  1 Sep 2022 3 repositories listed
  The task of quantifying the complexity of written language presents an interesting endeavor, particularly in the opportunity that it presents for aiding language learners.
• Integrated Gradient attribution for Gaussian Processes with non-Gaussian likelihoods
  25 May 2022 3 repositories listed
  One active line of research in explainable machine learning are gradient-based methods which have been successfully applied to complex neural networks.
• On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty
  22 Feb 2021 3 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 1 pointer-only (licence)
  Inducing point Gaussian process approximations are often considered a gold standard in uncertainty estimation since they retain many of the properties of the exact GP and scale to large datasets.
• An Intuitive Tutorial to Gaussian Process Regression
  22 Sep 2020 3 repositories listed
  This tutorial is accessible to a broad audience, including those new to machine learning, ensuring a clear understanding of GPR fundamentals.
• Bayesian Deep Ensembles via the Neural Tangent Kernel
  11 Jul 2020 3 repositories listed
  We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs).
• How Good are Low-Rank Approximations in Gaussian Process Regression?
  3 Apr 2020 3 repositories listed
  In particular, we bound the Kullback-Leibler divergence between an exact GP and one resulting from one of the afore-described low-rank approximations to its kernel, as well as between their corresponding predictive…
• PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
  13 Feb 2020 3 repositories listed
  Meta-learning can successfully acquire useful inductive biases from data.
• Scalable Exact Inference in Multi-Output Gaussian Processes
  14 Nov 2019 3 repositories listed Syntology ran 0 of 4 samples · 4 unverified
  Multi-output Gaussian processes (MOGPs) leverage the flexibility and interpretability of GPs while capturing structure across outputs, which is desirable, for example, in spatio-temporal modelling.
• Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels
  11 Oct 2019 3 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Recently, different machine learning methods have been introduced to tackle the challenging few-shot learning scenario that is, learning from a small labeled dataset related to a specific task.
• Deep Bayesian Optimization on Attributed Graphs
  31 May 2019 3 repositories listed Syntology ran 8 of 18 samples · 10 unverified
  Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications.
• Exact Gaussian Processes on a Million Data Points
  19 Mar 2019 3 repositories listed
  Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data.

Syntology lines on 17 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.