Alert button
Picture for Dino Sejdinovic

Dino Sejdinovic

Alert button

RKHS-SHAP: Shapley Values for Kernel Methods

Oct 18, 2021
Siu Lun Chau, Javier Gonzalez, Dino Sejdinovic

Figure 1 for RKHS-SHAP: Shapley Values for Kernel Methods
Figure 2 for RKHS-SHAP: Shapley Values for Kernel Methods
Figure 3 for RKHS-SHAP: Shapley Values for Kernel Methods
Figure 4 for RKHS-SHAP: Shapley Values for Kernel Methods
Viaarxiv icon

BayesIMP: Uncertainty Quantification for Causal Data Fusion

Jun 07, 2021
Siu Lun Chau, Jean-François Ton, Javier González, Yee Whye Teh, Dino Sejdinovic

Figure 1 for BayesIMP: Uncertainty Quantification for Causal Data Fusion
Figure 2 for BayesIMP: Uncertainty Quantification for Causal Data Fusion
Figure 3 for BayesIMP: Uncertainty Quantification for Causal Data Fusion
Figure 4 for BayesIMP: Uncertainty Quantification for Causal Data Fusion
Viaarxiv icon

Deconditional Downscaling with Gaussian Processes

Jun 05, 2021
Siu Lun Chau, Shahine Bouabid, Dino Sejdinovic

Figure 1 for Deconditional Downscaling with Gaussian Processes
Figure 2 for Deconditional Downscaling with Gaussian Processes
Figure 3 for Deconditional Downscaling with Gaussian Processes
Figure 4 for Deconditional Downscaling with Gaussian Processes
Viaarxiv icon

Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes

Jun 02, 2021
Veit Wild, Motonobu Kanagawa, Dino Sejdinovic

Figure 1 for Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Figure 2 for Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Viaarxiv icon

Time-to-event regression using partially monotonic neural networks

Mar 26, 2021
David Rindt, Robert Hu, David Steinsaltz, Dino Sejdinovic

Figure 1 for Time-to-event regression using partially monotonic neural networks
Figure 2 for Time-to-event regression using partially monotonic neural networks
Figure 3 for Time-to-event regression using partially monotonic neural networks
Figure 4 for Time-to-event regression using partially monotonic neural networks
Viaarxiv icon

Inter-domain Deep Gaussian Processes

Nov 01, 2020
Tim G. J. Rudner, Dino Sejdinovic, Yarin Gal

Figure 1 for Inter-domain Deep Gaussian Processes
Viaarxiv icon

Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint

Aug 23, 2020
Xingyue Pu, Siu Lun Chau, Xiaowen Dong, Dino Sejdinovic

Figure 1 for Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Figure 2 for Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Figure 3 for Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Figure 4 for Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Viaarxiv icon

Benign Overfitting and Noisy Features

Aug 06, 2020
Zhu Li, Weijie Su, Dino Sejdinovic

Figure 1 for Benign Overfitting and Noisy Features
Figure 2 for Benign Overfitting and Noisy Features
Viaarxiv icon

Variational Inference with Continuously-Indexed Normalizing Flows

Jul 10, 2020
Anthony Caterini, Rob Cornish, Dino Sejdinovic, Arnaud Doucet

Figure 1 for Variational Inference with Continuously-Indexed Normalizing Flows
Figure 2 for Variational Inference with Continuously-Indexed Normalizing Flows
Figure 3 for Variational Inference with Continuously-Indexed Normalizing Flows
Figure 4 for Variational Inference with Continuously-Indexed Normalizing Flows
Viaarxiv icon