Gaussian Process Regression

Also known as GP

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11 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Differentiable latent structure discovery for interpretable forecasting in clinical time series
Apr 30, 2026Ivan Lerner, Jean Feydy, Alexandre Kalimouttou +2Irregular Time-Series Modeling Healthcare
Universit´e Paris Cit´e and Universit´e Sorbonne Paris Nord, INRIA, Inserm, HEKA, Paris, 75015, France. · Department of Medical Informatics, Assistance Publique Hˆopitaux de Paris (AP-HP), Georges Pompidou European Hospital, Paris, France. · SIERRA, Inria Paris, Paris, 75015, France. +2
Rethinking Trust Region Bayesian Optimization in High Dimensions
Apr 24, 2026Wei-Ting Tang, Joel A. PaulsonBayesian Optimization Black-Box Optimization
University of Wisconsin–Madison
Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data
Mar 23, 2026Atticus Rex, Elizabeth Qian, David PetersonSurrogate Modeling Multifidelity Modeling
School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA · School of Computational Science & Engineering, Georgia Institute of Technology, Atlanta, GA · Air Force Research Lab, Wright-Patterson AFB, Dayton, Ohio
Time-adaptive infinite-dimensional Gaussian process regression on manifolds
Mar 22, 2026MD Ruiz-Medina, AE Madrid, A Torres-Signes +1Empirical Bayes Bayesian Inference
Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function
Mar 17, 2026Amon Lahr, Anna Scampicchio, Johannes Köhler +1Kernel Methods Gaussian Process Regression
Institute for Dynamical Systems and Control, ETH Zurich, Zurich, Switzerland
Correcting Boundary Bias and Observation Independence in Bayesian Experimental Design
Feb 2, 2026Sanna Jarl, Jens Sjölund, Jonathan J. S. Scragg +1Active Learning Adaptive Sampling
Department of Material Science & Engineering Uppsala University RISE · Department of Information Technology Uppsala University · RISE
No-Regret Gaussian Process Optimization of Time-Varying Functions
Nov 29, 2025Eliabelle Mauduit, Eloïse Berthier, Andrea SimonettoMulti-Armed Bandits Bayesian Optimization
Unit´e de Math´ematiques Appliqu´ees ENSTA, Institut Polytechnique de Paris Palaiseau, France · U2IS ENSTA, Institut Polytechnique de Paris Palaiseau, France
Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences
Jun 20, 2025Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic +1Gaussian Processes Reproducing Kernel Hilbert Spaces
EURECOM · University of Tübingen, Tübingen AI Center Max Planck Institute for Intelligent Systems · Adelaide University Australian Institute for Machine Learning +1

Apr 30, 2026, cs.LGJ/K move · Enter open · S save

Differentiable latent structure discovery for interpretable forecasting in clinical time series

Ivan Lerner, Jean Feydy, Alexandre Kalimouttou, Anita Burgun+1

Universit´e Paris Cit´e and Universit´e Sorbonne Paris Nord, INRIA, Inserm, HEKA, Paris, 75015, France. · Department of Medical Informatics, Assistance Publique Hˆopitaux de Paris (AP-HP), Georges Pompidou European Hospital, Paris, France. · SIERRA, Inria Paris, Paris, 75015, France.+2

Irregular Time-Series Modeling Healthcare Causal Structure Learning Longitudinal EHR Modeling Probabilistic Time Series Forecasting Gaussian Process Regression

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Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approaches either impute to a grid or sacrifice interpretability. We introduce StructGP, a continuous-time multi-task Gaussian process that couples process convolutions with differentiable structure learning to uncover a sparse, ordered directed acyclic graph (DAG) of inter-variable dependencies while preserving principled uncertainty. We further propose LP-StructGP, which augments StructGP with latent pathways-shared, temporally shifted trajectories inferred via subject-specific coupling filters and a softmax gating mechanism-to capture cross-patient progression patterns. Both models are trained under sparsity and acyclicity constraints (augmented Lagrangian, Adam) using scalable low-rank updates. Results: In simulations, the approach reliably recovers ground-truth graphs (Structural Hamming Distance approaching 0 as cohorts grow) and pathway assignments (high Adjusted Rand Index). On a MIMIC-IV septic shock cohort (n=1,008; norepinephrine, creatinine, mean arterial pressure), StructGP improves short-horizon (6 h) forecasting over independent-task baselines (average RMSE 0.68 [95%CI: 0.63--0.74] vs. 0.88 [0.83-0.94]) and, with 15 additional inputs, markedly outperforms unstructured kernels (0.63 [0.58-0.69] vs. 3.02 [2.85-3.18]) with superior calibration (coverage 0.96 vs. 0.84). On the PhysioNet Challenge (12k patients, 41 variables), StructGP attains competitive accuracy (MAE 3.72e-2) relative to a state-of-the-art graph neural model while maintaining calibrated uncertainty. Conclusion: These results show that structured process convolutions with latent pathways deliver interpretable, scalable, and well-calibrated forecasting for irregular clinical time series.

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