Deep Kernel Learning

Latest papers

8 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Deep kernel hedging
Sep 28, 2026Jean-Loup Dupret, Donatien Hainaut, Edouard MotteDeep Kernel Learning Quantitative Finance
University of Amsterdam, Amsterdam School of Economics · ETH Zurich, Department of Mathematics, RiskLab · Université Catholique de Louvain, LIDAM–ISBA
Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing
Sep 15, 2026Hojin Lee, Youngim Nam, Sanghun Lee +1Probabilistic Trajectory Prediction Deep Kernel Learning
Department of Mechanical Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
Finding the Needle in a Haystack: Test-Time Analog Circuit Representation Adaptation for Bayesian Optimization
Aug 13, 2026Fin Amin, Sounak Dutta, Paul D. FranzonDeep Kernel Learning Representation Learning
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, USA
Closed-loop discovery of out-of-distribution processing protocols by evolutionary search and uncertainty-aware learning
Jun 11, 2026Yu Liu, Stanislav Udovenko, Ching-Che Lin +4Deep Kernel Learning Materials Science
Department of Materials Science and Engineering, University of Tennessee, Knoxville, Tennessee 37996, USA · Materials Science and Engineering Department, Materials Research Institute, the Pennsylvania State University, University Park, Pennsylvania 16802, USA · Department of Materials Science and NanoEngineering, Rice University, Houston, TX 77005, USA +4
Beyond Scalar Objectives: Expert-Feedback-Driven Autonomous Experimentation for Scientific Discovery at the Nanoscale
May 20, 2026Ralph Bulanadi, Jefferey Baxter, Arpan Biswas +5Pairwise Preference Learning Deep Kernel Learning
Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA · University of Tennessee-Oak Ridge Innovation Institute, University of Tennessee, Knoxville, TN 37996, USA · Department of Material Science and Engineering, School of Materials and Chemical Technology, Institute of Science Tokyo, Yokohama, 226-8502, Japan +3
Transformed Latent Variable Multi-Output Gaussian Processes
May 6, 2026Xiaoyu Jiang, Xinxing Shi, Sokratia Georgaka +2Deep Kernel Learning Latent Variable Models
Department of Computer Science, University of Manchester, Manchester, UK · Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
Deep Kernel Learning for Stratifying Glaucoma Trajectories
May 1, 2026Bruce Rushing, Angela Danquah, Alireza Namazi +2Deep Kernel Learning Clinical Outcome Prediction
Research Computing, University of Virginia · School of Data Science, University of Virginia · School of Medicine, University of Virginia
Novelty-Driven Target-Space Discovery in Automated Electron and Scanning Probe Microscopy
Mar 17, 2026Utkarsh Pratiush, Kamyar Barakati, Boris N. Slautin +4Deep Kernel Learning Scanning Transmission Electron Microscopy
Department of Materials Science and Engineering, University of Tennessee, Knoxville, TN 37996, USA · Department of Chemistry, University of Washington, Seattle, WA 98195, USA · Pacific Northwest National Laboratory, Richland, WA 99354, USA

Sep 28, 2026J/K move · Enter open · S save

Deep kernel hedging

Jean-Loup Dupret, Donatien Hainaut, Edouard Motte

University of Amsterdam, Amsterdam School of Economics · ETH Zurich, Department of Mathematics, RiskLab · Université Catholique de Louvain, LIDAM–ISBA

Deep Kernel Learning Quantitative Finance Reproducing Kernel Hilbert Spaces Kernel Methods

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We introduce a deep kernel hedging framework that combines the flexibility of deep learning with the structural inductive bias of kernel methods. The hedging functional is restricted to a reproducing kernel Hilbert space whose kernel is parameterized through a neural network embedding of the input features. The framework minimizes a regularized empirical risk under convex loss functions and can accommodate path-dependent information through truncated time-augmented signature features. We derive a generalized representer theorem for the joint hedging problem, reducing the empirical optimization to a finite-dimensional problem. To further reduce the computational cost associated with large kernel matrices, we develop a scalable random Fourier feature approximation and establish convergence guarantees. The random Fourier parameters are sampled once and remain fixed throughout training, while the deep kernel adapts to market data through the learned neural representation. We evaluate the performance of the proposed deep kernel approach on both synthetic and real data and compare it with standard kernel methods and classical deep hedging architectures. Numerical results indicate competitive and robust hedging performance, particularly in low-data regimes, which highlights the benefits of combining expressive neural representations with the inductive bias of kernel methods.