Papers
1,854 papers found
RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection
Ran Iwamoto, Masahiro Yukawa
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
Théo Galy-Fajou, Florian Wenzel, Christian Donner et al.
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser et al.
GPIRT: A Gaussian Process Model for Item Response Theory
JBrandon Duck-Mayr, Roman Garnett, Jacob Montgomery
An Interpretable and Sample Efficient Deep Kernel for Gaussian Process
Yijue Dai, Tianjian Zhang, Zhidi Lin et al.
Sensor Placement for Spatial Gaussian Processes with Integral Observations
Krista Longi, Chang Rajani, Tom Sillanpää et al.
Probabilistic selection of inducing points in sparse Gaussian processes
Anders Kirk Uhrenholt, Valentin Charvet, Bjørn Sand Jensen
Gaussian process nowcasting: application to COVID-19 mortality reporting
Iwona Hawryluk, Henrique Hoeltgebaum, Swapnil Mishra et al.
Similarity measure for sparse time course data based on Gaussian processes
Zijing Liu, Mauricio Barahona
Subset-of-data variational inference for deep Gaussian-processes regression
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan
Learning to learn with Gaussian processes
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Multi-output Gaussian Processes for uncertainty-aware recommender systems
Yinchong Yang, Florian Buettner
Information theoretic meta learning with Gaussian processes
Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos et al.
Combining pseudo-point and state space approximations for sum-separable Gaussian Processes
Will Tebbutt, Arno Solin, Richard E. Turner
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki et al.
Laplace approximated Gaussian process state-space models
Jakob Lindinger, Barbara Rakitsch, Christoph Lippert
Low-precision arithmetic for fast Gaussian processes
Wesley J. Maddox, Andres Potapcynski, Andrew Gordon Wilson
Learning linear non-Gaussian polytree models
Daniele Tramontano, Anthea Monod, Mathias Drton
Robust Gaussian process regression with the trimmed marginal likelihood
Daniel Andrade, Akiko Takeda
Learning Choice Functions with Gaussian Processes
Alessio Benavoli, Dario Azzimonti, Dario Piga
Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels
Matthias Bitzer, Mona Meister, Christoph Zimmer
Deep Gaussian mixture ensembles
Yousef El-Laham, Niccolo Dalmasso, Elizabeth Fons et al.
Gaussian Process Surrogate Models for Neural Networks
Michael Y. Li, Erin Grant, Thomas L. Griffiths
Benefits of monotonicity in safe exploration with Gaussian processes
Arpan Losalka, Jonathan Scarlett