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Workshop
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Big Batch Bayesian Active Learning by Considering Predictive Probabilities
Sebastian Ober · Sam Power · Tom Diethe · Henry Moss
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Workshop
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Improving the Gaussian Approximation in Neural Networks: Para-Gaussians and Edgeworth Expansions
Mihai Nica · Janosch Ortmann
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Poster
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Thu 11:00
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Training Binary Neural Networks via Gaussian Variational Inference and Low-Rank Semidefinite Programming
Lorenzo Orecchia · Jiawei Hu · Xue He · Wang Mark · Xulei Yang · Min Wu · Xue Geng
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Workshop
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Sat 11:10
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Contributed Talk: Taiwo Adebiyi - Gaussian Process Thompson Sampling via Rootfinding
Taiwo Adebiyi
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Workshop
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Mode Collapse in Variational Deep Gaussian Processes
Francisco Sáez-Maldonado · Juan Maroñas · Daniel Hernández-Lobato
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Poster
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Thu 16:30
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NeuroGauss4D-PCI: 4D Neural Fields and Gaussian Deformation Fields for Point Cloud Interpolation
Chaokang Jiang · Dalong Du · Jiuming Liu · Siting Zhu · Zhenqiang Liu · Zhuang Ma · Zhujin Liang · Jie Zhou
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Workshop
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Uncovering Neural Encoding Variability with Infinite Gaussian Process Factor Analysis
Changmin Yu · Mate Lengyel
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Workshop
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Sat 11:00
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Contributed Talk: Mathieu Alain - Graph Classification Gaussian Processes via Hodgelet Spectral Features
Mathieu Alain
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Workshop
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Gaussian Process Thompson Sampling via Rootfinding
Taiwo Adebiyi · Bach Do · Ruda Zhang
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Workshop
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Sat 9:30
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Invited Talk: Mark van der Wilk - Open Problems in Gaussian Process Approximation and Benchmarking
Mark van der Wilk
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Poster
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Fri 16:30
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Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes
Yunyue Wei · Vincent Zhuang · Saraswati Soedarmadji · Yanan Sui
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Workshop
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A scalable Bayesian continual learning framework for online and sequential decision making
Hanwen Xing · Christopher Yau
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