{"total":222,"rows":[{"id":"2102.11409","title":"On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty","created":"2021-02-22","cited_by_count":87,"area":null},{"id":"2102.12108","title":"The Promises and Pitfalls of Deep Kernel Learning","created":"2021-02-24","cited_by_count":81,"area":null},{"id":"2106.15482","title":"Personalized Federated Learning with Gaussian Processes","created":"2021-06-29","cited_by_count":76,"area":null},{"id":"2107.00649","title":"On the Practicality of Deterministic Epistemic Uncertainty","created":"2021-07-01","cited_by_count":62,"area":null},{"id":"2304.08151","title":"Prediction-Oriented Bayesian Active Learning","created":"2023-04-17","cited_by_count":62,"area":null},{"id":"2205.00403","title":"A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness","created":"2022-05-01","cited_by_count":49,"area":null},{"id":"2111.01732","title":"Spatio-Temporal Variational Gaussian Processes","created":"2021-11-02","cited_by_count":32,"area":null},{"id":"2301.10123","title":"Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation","created":"2023-01-24","cited_by_count":32,"area":null},{"id":"2107.00363","title":"Valid prediction intervals for regression problems","created":"2021-07-01","cited_by_count":29,"area":null},{"id":"2111.02275","title":"Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data","created":"2021-11-03","cited_by_count":28,"area":null},{"id":"2102.07868","title":"GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning","created":"2021-02-15","cited_by_count":26,"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":"2205.10186","title":"Bayesian Active Learning with Fully Bayesian Gaussian Processes","created":"2022-05-20","cited_by_count":26,"area":null},{"id":"2311.03721","title":"ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning","created":"2023-11-07","cited_by_count":25,"area":null},{"id":"2110.15172","title":"Conditioning Sparse Variational Gaussian Processes for Online Decision-making","created":"2021-10-28","cited_by_count":23,"area":null},{"id":"2303.15746","title":"qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization","created":"2023-03-28","cited_by_count":23,"area":null},{"id":"2410.15320","title":"Amortized Probabilistic Conditioning for Optimization, Simulation and Inference","created":"2024-10-20","cited_by_count":23,"area":null},{"id":"2106.07512","title":"Last Layer Marginal Likelihood for Invariance Learning","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2102.08314","title":"Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients","created":"2021-02-16","cited_by_count":20,"area":null},{"id":"2111.03412","title":"Dual Parameterization of Sparse Variational Gaussian Processes","created":"2021-11-05","cited_by_count":19,"area":null},{"id":"2206.09861","title":"Additive Gaussian Processes Revisited","created":"2022-06-20","cited_by_count":19,"area":null},{"id":"2504.14889","title":"Latent Bayesian Optimization via Autoregressive Normalizing Flows","created":"2025-04-21","cited_by_count":19,"area":null},{"id":"2106.01121","title":"Connections and Equivalences between the Nystr\\\"om Method and Sparse Variational Gaussian Processes","created":"2021-06-02","cited_by_count":17,"area":null},{"id":"2207.05543","title":"Markovian Gaussian Process Variational Autoencoders","created":"2022-07-12","cited_by_count":16,"area":null},{"id":"2107.04061","title":"Scaling Gaussian Processes with Derivative Information Using Variational Inference","created":"2021-07-08","cited_by_count":15,"area":null},{"id":"2109.01545","title":"Large-Scale Learning with Fourier Features and Tensor Decompositions","created":"2021-09-03","cited_by_count":14,"area":null},{"id":"2110.15739","title":"Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation","created":"2021-10-29","cited_by_count":14,"area":null},{"id":"2302.14578","title":"Interactive Segmentation as Gaussian Process Classification","created":"2023-02-28","cited_by_count":14,"area":null},{"id":"2104.05674","title":"GPflux: A Library for Deep Gaussian Processes","created":"2021-04-12","cited_by_count":13,"area":null},{"id":"2111.01721","title":"Bayes-Newton Methods for Approximate Bayesian Inference with PSD Guarantees","created":"2021-11-02","cited_by_count":13,"area":null},{"id":"2310.17247","title":"Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity","created":"2023-10-26","cited_by_count":13,"area":null},{"id":"2504.06265","title":"Large language models as uncertainty-calibrated optimizers for experimental discovery","created":"2025-04-08","cited_by_count":13,"area":null},{"id":"2110.12752","title":"Adaptive Gaussian Processes on Graphs via Spectral Graph Wavelets","created":"2021-10-25","cited_by_count":11,"area":null},{"id":"2201.06314","title":"Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression","created":"2022-01-17","cited_by_count":11,"area":null},{"id":"2302.08436","title":"Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow","created":"2023-02-16","cited_by_count":11,"area":null},{"id":"2304.01518","title":"Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimation","created":"2023-04-04","cited_by_count":11,"area":null},{"id":"2306.14916","title":"Uncertainty Estimation for Molecules: Desiderata and Methods","created":"2023-06-20","cited_by_count":11,"area":null},{"id":"2310.17137","title":"Large-Scale Gaussian Processes via Alternating Projection","created":"2023-10-26","cited_by_count":11,"area":null},{"id":"2101.03906","title":"Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks","created":"2021-01-11","cited_by_count":10,"area":null},{"id":"2103.10710","title":"Sparse Algorithms for Markovian Gaussian Processes","created":"2021-03-19","cited_by_count":10,"area":null},{"id":"2210.01672","title":"Bringing motion taxonomies to continuous domains via GPLVM on hyperbolic manifolds","created":"2022-10-04","cited_by_count":10,"area":null},{"id":"2210.02676","title":"Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture","created":"2022-10-06","cited_by_count":10,"area":null},{"id":"2306.09819","title":"Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels","created":"2023-06-16","cited_by_count":10,"area":null},{"id":"2106.06695","title":"SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes","created":"2021-06-12","cited_by_count":9,"area":null},{"id":"2207.01242","title":"Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection","created":"2022-07-04","cited_by_count":9,"area":null},{"id":"2302.04534","title":"Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes","created":"2023-02-09","cited_by_count":9,"area":null},{"id":"2411.01036","title":"Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference","created":"2024-11-01","cited_by_count":9,"area":null},{"id":"2106.05960","title":"Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features","created":"2021-06-10","cited_by_count":8,"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":"2202.12981","title":"Scalable Gaussian-process regression and variable selection using Vecchia approximations","created":"2022-02-25","cited_by_count":8,"area":null},{"id":"2302.04061","title":"Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning","created":"2023-02-08","cited_by_count":8,"area":null},{"id":"2309.10068","title":"A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes","created":"2023-09-18","cited_by_count":8,"area":null},{"id":"2312.05568","title":"Sparse Variational Student-t Processes","created":"2023-12-09","cited_by_count":8,"area":null},{"id":"2406.01870","title":"Understanding Stochastic Natural Gradient Variational Inference","created":"2024-06-04","cited_by_count":8,"area":null},{"id":"2106.08598","title":"Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization","created":"2021-06-16","cited_by_count":7,"area":null},{"id":"2106.10210","title":"Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes","created":"2021-06-18","cited_by_count":7,"area":null},{"id":"2205.15008","title":"Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification","created":"2022-05-30","cited_by_count":7,"area":null},{"id":"2211.01053","title":"Fantasizing with Dual GPs in Bayesian Optimization and Active Learning","created":"2022-11-02","cited_by_count":7,"area":null},{"id":"2211.01689","title":"Isotropic Gaussian Processes on Finite Spaces of Graphs","created":"2022-11-03","cited_by_count":7,"area":null},{"id":"2309.09222","title":"Bayesian Gaussian Process ODEs via Double Normalizing Flows","created":"2023-09-17","cited_by_count":7,"area":null},{"id":"2407.00584","title":"Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features","created":"2024-06-30","cited_by_count":7,"area":null},{"id":"2105.11535","title":"Scalable Cross Validation Losses for Gaussian Process Models","created":"2021-05-24","cited_by_count":6,"area":null},{"id":"2203.09751","title":"Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation","created":"2022-03-18","cited_by_count":6,"area":null},{"id":"2210.02419","title":"Boundary-Aware Uncertainty for Feature Attribution Explainers","created":"2022-10-05","cited_by_count":6,"area":null},{"id":"2303.01925","title":"Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes","created":"2023-03-03","cited_by_count":6,"area":null},{"id":"2309.12658","title":"Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes","created":"2023-09-22","cited_by_count":6,"area":null},{"id":"2402.07692","title":"Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints","created":"2024-02-12","cited_by_count":6,"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":"2107.07281","title":"Input Dependent Sparse Gaussian Processes","created":"2021-07-15","cited_by_count":5,"area":null},{"id":"2108.06514","title":"Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations","created":"2021-08-14","cited_by_count":5,"area":null},{"id":"2204.04648","title":"Gaussian Processes for Missing Value Imputation","created":"2022-04-10","cited_by_count":5,"area":null},{"id":"2204.11589","title":"Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation","created":"2022-04-02","cited_by_count":5,"area":null},{"id":"2209.04636","title":"Revisiting Active Sets for Gaussian Process Decoders","created":"2022-09-10","cited_by_count":5,"area":null},{"id":"2302.00406","title":"Learning Choice Functions with Gaussian Processes","created":"2023-02-01","cited_by_count":5,"area":null},{"id":"2106.09179","title":"Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter Recommendation","created":"2021-06-17","cited_by_count":4,"area":null},{"id":"2211.02476","title":"Sparse Gaussian Process Hyperparameters: Optimize or Integrate?","created":"2022-11-04","cited_by_count":4,"area":null},{"id":"2301.13303","title":"Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization","created":"2023-01-30","cited_by_count":4,"area":null},{"id":"2304.05091","title":"Actually Sparse Variational Gaussian Processes","created":"2023-04-11","cited_by_count":4,"area":null},{"id":"2306.03770","title":"Graph Classification Gaussian Processes via Spectral Features","created":"2023-06-06","cited_by_count":4,"area":null},{"id":"2308.15364","title":"Heterogeneous Multi-Task Gaussian Cox Processes","created":"2023-08-29","cited_by_count":4,"area":null},{"id":"2311.16849","title":"Identifiable Feature Learning for Spatial Data with Nonlinear ICA","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2408.06069","title":"Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations","created":"2024-08-12","cited_by_count":4,"area":null},{"id":"2106.00072","title":"Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data","created":"2021-05-31","cited_by_count":3,"area":null},{"id":"2106.00474","title":"Gaussian Processes with Differential Privacy","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2106.00638","title":"Quantifying Predictive Uncertainty in Medical Image Analysis with Deep Kernel Learning","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2106.04221","title":"Multi-output Gaussian Processes for Uncertainty-aware Recommender Systems","created":"2021-06-08","cited_by_count":3,"area":null},{"id":"2106.04967","title":"GP-ConvCNP: Better Generalization for Convolutional Conditional Neural Processes on Time Series Data","created":"2021-06-09","cited_by_count":3,"area":null},{"id":"2110.11044","title":"Bayesian Meta-Learning Through Variational Gaussian Processes","created":"2021-10-21","cited_by_count":3,"area":null},{"id":"2208.14989","title":"Learning Multiscale Non-stationary Causal Structures","created":"2022-08-31","cited_by_count":3,"area":null},{"id":"2308.14142","title":"Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes","created":"2023-08-27","cited_by_count":3,"area":null},{"id":"2401.07711","title":"Efficient Nonparametric Tensor Decomposition for Binary and Count Data","created":"2024-01-15","cited_by_count":3,"area":null},{"id":"2403.19500","title":"Tensor Network-Constrained Kernel Machines as Gaussian Processes","created":"2024-03-28","cited_by_count":3,"area":null},{"id":"2409.10972","title":"Towards Gaussian Process for operator learning: an uncertainty aware resolution independent operator learning algorithm for computational mechanics","created":"2024-09-17","cited_by_count":3,"area":null},{"id":"2501.15458","title":"Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies From Simulated Nonparametric Functions","created":"2025-01-26","cited_by_count":3,"area":null},{"id":"2502.04750","title":"Tighter sparse variational Gaussian processes","created":"2025-02-07","cited_by_count":3,"area":null},{"id":"2502.19086","title":"Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood","created":"2025-02-26","cited_by_count":3,"area":null},{"id":"2504.08721","title":"Surrogate-based optimization of system architectures subject to hidden constraints","created":"2025-04-11","cited_by_count":3,"area":null},{"id":"2505.13723","title":"Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project","created":"2025-05-19","cited_by_count":3,"area":null},{"id":"2507.07853","title":"Optimization Guarantees for Square-Root Natural-Gradient Variational Inference","created":"2025-07-10","cited_by_count":3,"area":null},{"id":"2102.07106","title":"Healing Products of Gaussian Processes","created":"2021-02-14","cited_by_count":2,"area":null}],"next_cursor":"100"}