Mark van der Wilk
- GPflow: A Gaussian process library using TensorFlow
2016/10/27 by Alexander Matthews, Matthews, Alexander G. de G., Mark van der Wilk +13 · 13 citations
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML)
- Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty
2020/06/10 by Miguel Monteiro, Loïc Le Folgoc, Monteiro, Miguel +13 · 9 citations
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning and Data Classification
- Bayesian Layers: A Module for Neural Network Uncertainty
2018/12/10 by Dustin Tran, Tran, Dustin, Michael W. Dusenberry +5 · 6 citations
Computer Science · Physics and Astronomy · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Model Reduction and Neural Networks #Programming Languages (cs.PL)
- Rethinking Aleatoric and Epistemic Uncertainty
2024/12/30 by Freddie Bickford Smith, Smith, Freddie Bickford, Jannik Kossen +9 · 13 citations
Arts and Humanities · #Epistemology, Ethics, and Metaphysics #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Rates of Convergence for Sparse Variational Gaussian Process Regression
2019/03/08 by David R. Burt, Carl Edward Rasmussen, Burt, David R. +3 · 4 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Relaxing Equivariance Constraints with Non-stationary Continuous Filters
2022/04/14 by Tycho F. A. van der Ouderaa, van der Ouderaa, Tycho F. A., David W. Romero +3 · 4 citations
Materials Science · Computer Science · Biochemistry, Genetics and Molecular Biology · #Machine Learning in Materials Science #Topic Modeling #Machine Learning in Bioinformatics
- Learning Invariances using the Marginal Likelihood
2018/08/16 by Mark van der Wilk, Matthias Bauer, van der Wilk, Mark +5 · 2 citations
Computer Science · #Gaussian Processes and Bayesian Inference #Machine Learning and Data Classification
- Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization
2022/11/11 by Jose Pablo Folch, Folch, Jose Pablo, Robert M Lee +11 · 3 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Process Optimization and Integration #Machine Learning and Algorithms
- Convolutional Gaussian Processes
2017/09/06 by Mark van der Wilk, van der Wilk, Mark, Carl Edward Rasmussen +3 · 3 citations
Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Scientific Research and Discoveries #Target Tracking and Data Fusion in Sensor Networks
- Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients
2021/02/16 by Artem Artemev, David R. Burt, Artemev, Artem +3 · 2 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Learning Layer-wise Equivariances Automatically using Gradients
2023/10/09 by Tycho F. A. van der Ouderaa, van der Ouderaa, Tycho F. A., Alexander Immer +3 · 3 citations
Computer Science · Materials Science · #Artificial Intelligence (cs.AI) #Computational Physics and Python Applications #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science
- Transition Constrained Bayesian Optimization via Markov Decision Processes
2024/02/13 by Jose Pablo Folch, Folch, Jose Pablo, Calvin Tsay +15 · 3 citations
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Reservoir Engineering and Simulation Methods
- Bayesian Image Classification with Deep Convolutional Gaussian Processes
2019/02/15 by Vincent Dutordoir, Mark van der Wilk, Dutordoir, Vincent +5 · 1 citation
Computer Science · Engineering · #Gaussian Processes and Bayesian Inference #Control Systems and Identification #Fault Detection and Control Systems
- A Framework for Interdomain and Multioutput Gaussian Processes
2020/03/02 by Mark van der Wilk, Vincent Dutordoir, van der Wilk, Mark +9 · 2 citations
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Control Systems and Identification #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
2025/07/07 by Anish Dhir, Cristiana Diaconu, Dhir, Anish +9 · 4 citations
Computer Science · Neuroscience · #Bayesian Modeling and Causal Inference #Gaussian Processes and Bayesian Inference #Functional Brain Connectivity Studies
- GPflux: A Library for Deep Gaussian Processes
2021/04/12 by Vincent Dutordoir, Dutordoir, Vincent, Hugh Salimbeni +17 · 1 citation
Computer Science · #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML)
- Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks
2024/08/10 by Yoav Gelberg, Gelberg, Yoav, Tycho F. A. van der Ouderaa +5 · 2 citations
Computer Science · #Adversarial Robustness in Machine Learning #Explainable Artificial Intelligence (XAI) #Bayesian Modeling and Causal Inference
- Speedy Performance Estimation for Neural Architecture Search
2020/06/08 by Binxin Ru, Ru, Binxin, Clare Lyle +9 · 1 citation
Computer Science · #Advanced Neural Network Applications #Domain Adaptation and Few-Shot Learning #Machine Learning and Data Classification
- Noether's razor: Learning Conserved Quantities
2024/10/10 by Tycho F. A. van der Ouderaa, Mark van der Wilk, van der Ouderaa, Tycho F. A. +3 · 1 voice · 1 citation
#cs.LG #stat.ML
- PSyDUCK: Training-Free Steganography for Latent Diffusion
2025/01/31 by Aqib Mahfuz, Georgia Channing, Mahfuz, Aqib +9 · 2 citations
Computer Science · #Advanced Steganography and Watermarking Techniques #Cryptography and Security (cs.CR) #Digital Media Forensic Detection #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Machine Learning (cs.LG)
- System-Aware Neural ODE Processes for Few-Shot Bayesian Optimization
2024/06/04 by Jixiang Qing, Becky D. Langdon, Qing, Jixiang +11 · 1 citation
Engineering · #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG)
- Continuous Bayesian Model Selection for Multivariate Causal Discovery
2024/11/15 by Anish Dhir, Ruby Sedgwick, Dhir, Anish +7 · 1 citation
Computer Science · Engineering · #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Rough Sets and Fuzzy Logic