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Affinity Workshop
Mon 8:55 Computation of Discrete Flows Over Networks via Constrained Wasserstein Barycenters
Ferran Arque, Cesar Uribe
Spotlight
Wed 7:30 Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh
Poster
Wed 9:00 Bayesian Optimistic Optimisation with Exponentially Decaying Regret
Hung Tran-The, Sunil Gupta, Santu Rana, Svetha Venkatesh
Spotlight
Wed 18:20 Gaussian Process-Based Real-Time Learning for Safety Critical Applications
Armin Lederer, Alejandro Ordóñez Conejo, Korbinian Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche
Poster
Wed 21:00 Gaussian Process-Based Real-Time Learning for Safety Critical Applications
Armin Lederer, Alejandro Ordóñez Conejo, Korbinian Maier, Wenxin Xiao, Jonas Umlauft, Sandra Hirche
Oral
Thu 5:00 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Artem Artemev, David Burt, Mark van der Wilk
Spotlight
Thu 5:20 Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen, Søren Hauberg
Spotlight
Thu 5:25 Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor, Theofanis Karaletsos, Thang Bui
Spotlight
Thu 5:30 Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone
Spotlight
Thu 5:35 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V Bonilla, Theo Damoulas, Terry Lyons
Spotlight
Thu 5:40 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
Spotlight
Thu 5:45 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Wilson, Roger Grosse
Spotlight
Thu 6:20 Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
Gustavo Malkomes, Harvey Cheng, Eric Lee, Michael McCourt
Spotlight
Thu 7:40 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 Beyond the Pareto Efficient Frontier: Constraint Active Search for Multiobjective Experimental Design
Gustavo Malkomes, Harvey Cheng, Eric Lee, Michael McCourt
Poster
Thu 9:00 Isometric Gaussian Process Latent Variable Model for Dissimilarity Data
Martin Jørgensen, Søren Hauberg
Poster
Thu 9:00 Variational Auto-Regressive Gaussian Processes for Continual Learning
Sanyam Kapoor, Theofanis Karaletsos, Thang Bui
Poster
Thu 9:00 Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
Artem Artemev, David Burt, Mark van der Wilk
Poster
Thu 9:00 Sparse within Sparse Gaussian Processes using Neighbor Information
Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone
Poster
Thu 9:00 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data
Maud Lemercier, Cristopher Salvi, Thomas Cass, Edwin V Bonilla, Theo Damoulas, Terry Lyons
Poster
Thu 9:00 Bayesian Quadrature on Riemannian Data Manifolds
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Poster
Thu 9:00 On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
Tim G. J. Rudner, Oscar Key, Yarin Gal, Tom Rainforth
Poster
Thu 9:00 Scalable Variational Gaussian Processes via Harmonic Kernel Decomposition
Shengyang Sun, Jiaxin Shi, Andrew Wilson, Roger Grosse
Spotlight
Thu 17:20 Objective Bound Conditional Gaussian Process for Bayesian Optimization
Taewon Jeong, Heeyoung Kim
Spotlight
Thu 17:35 Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John Cunningham
Oral
Thu 19:20 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Wilson
Spotlight
Thu 20:30 Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai, Selwyn Gomes, Jonathan Scarlett
Spotlight
Thu 20:35 Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael Hutchinson, Yee-Whye Teh
Spotlight
Thu 20:40 Value-at-Risk Optimization with Gaussian Processes
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Spotlight
Thu 20:45 High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig
Spotlight
Thu 20:50 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya
Poster
Thu 21:00 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, Ethan Fetaya
Poster
Thu 21:00 Lenient Regret and Good-Action Identification in Gaussian Process Bandits
Xu Cai, Selwyn Gomes, Jonathan Scarlett
Poster
Thu 21:00 Objective Bound Conditional Gaussian Process for Bayesian Optimization
Taewon Jeong, Heeyoung Kim
Poster
Thu 21:00 Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes
Peter Holderrieth, Michael Hutchinson, Yee-Whye Teh
Poster
Thu 21:00 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes
Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Wilson
Poster
Thu 21:00 High-Dimensional Gaussian Process Inference with Derivatives
Filip de Roos, Alexandra Gessner, Philipp Hennig
Poster
Thu 21:00 Bias-Free Scalable Gaussian Processes via Randomized Truncations
Andres Potapczynski, Luhuan Wu, Dan Biderman, Geoff Pleiss, John Cunningham
Poster
Thu 21:00 Value-at-Risk Optimization with Gaussian Processes
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Workshop
Sat 12:23 Selective Focusing Learning in Conditional GANs
Kyeongbo Kong, Kyunghun Kim, Woo-jin Song, Suk-Ju Kang
Workshop
Examining the Human Perceptibility of Black-Box Adversarial Attacks on Face Recognition
Benjamin Spetter-Goldstein, Nataniel Ruiz, Sarah Bargal
Workshop
A unified PAC-Bayesian framework for machine unlearning via information risk minimization
Sharu Jose, Osvaldo Simeone
Workshop
Counterfactual Explanations for Graph Neural Networks
Ana Lucic, Maartje ter Hoeve, Gabriele Tolomei, Maarten de Rijke, Fabrizio Silvestri
Workshop
Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks
Eshaan Nichani, Adit Radhakrishnan, Caroline Uhler
Workshop
Selective Focusing Learning in Conditional GANs
Kyeongbo Kong, Kyunghun Kim, Woo-jin Song, Suk-Ju Kang