Nick Pawlowski

According to our database, Nick Pawlowski authored at least 35 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2024

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Agrin Hilmkil, Joel Jennings, Meyer Scetbon, Miltiadis Allamanis, Cheng Zhang

Trans. Mach. Learn. Res., 2024

The Essential Role of Causality in Foundation World Models for Embodied AI.

Tarun Gupta, Wenbo Gong, Chao Ma, Nick Pawlowski, Agrin Hilmkil, Meyer Scetbon, Ade Famoti, Ashley Juan Llorens, Jianfeng Gao, Stefan Bauer, Danica Kragic, Bernhard Schölkopf, Cheng Zhang

CoRR, 2024

Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs.

Joshua Durso-Finley, Berardino Barile, Jean-Pierre R. Falet, Douglas L. Arnold, Nick Pawlowski, Tal Arbel

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2024, 2024

Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention.

Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

CoRR, 2023

Understanding Causality with Large Language Models: Feasibility and Opportunities.

Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan

CoRR, 2023

BayesDAG: Gradient-Based Posterior Inference for Causal Discovery.

Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models.

Joshua Durso-Finley, Jean-Pierre R. Falet, Raghav Mehta, Douglas L. Arnold, Nick Pawlowski, Tal Arbel

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2023, 2023

High Fidelity Image Counterfactuals with Probabilistic Causal Models.

Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker

Proceedings of the International Conference on Machine Learning, 2023

Measuring axiomatic soundness of counterfactual image models.

Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski, Daniel C. Castro, Ben Glocker

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Rhino: Deep Causal Temporal Relationship Learning with History-dependent Noise.

Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Structured Uncertainty in the Observation Space of Variational Autoencoders.

James Langley, Miguel Monteiro, Charles Jones, Nick Pawlowski, Ben Glocker

Trans. Mach. Learn. Res., 2022

Does your dermatology classifier know what it doesn't know? Detecting the long-tail of unseen conditions.

Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zachary Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Gregory S. Corrado, Umesh Telang, Yun Liu, A. Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

Medical Image Anal., 2022

Instructions and Guide: Causal Insights for Learning Paths in Education.

Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang

CoRR, 2022

Deep End-to-end Causal Inference.

Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

CoRR, 2022

Simultaneous Missing Value Imputation and Structure Learning with Groups.

Pablo Morales-Alvarez, Wenbo Gong, Angus Lamb, Simon Woodhead, Simon Peyton Jones, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Deep Structural Causal Shape Models.

Rajat Rasal, Daniel C. Castro, Nick Pawlowski, Ben Glocker

Proceedings of the Computer Vision - ECCV 2022 Workshops, 2022

2021

Normative ascent with local gaussians for unsupervised lesion detection.

Xiaoran Chen, Nick Pawlowski, Ben Glocker, Ender Konukoglu

Medical Image Anal., 2021

2020

An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection.

Rujikorn Charakorn, Yuttapong Thawornwattana, Sirawaj Itthipuripat, Nick Pawlowski, Poramate Manoonpong, Nat Dilokthanakul

CoRR, 2020

Deep Structural Causal Models for Tractable Counterfactual Inference.

Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty.

Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning.

Nat Dilokthanakul, Christos Kaplanis, Nick Pawlowski, Murray Shanahan

IEEE Trans. Neural Networks Learn. Syst., 2019

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors.

Matthew C. H. Lee, Kersten Petersen, Nick Pawlowski, Ben Glocker, Michiel Schaap

IEEE Trans. Medical Imaging, 2019

Needles in Haystacks: On Classifying Tiny Objects in Large Images.

Nick Pawlowski, Suvrat Bhooshan, Nicolas Ballas, Francesco Ciompi, Ben Glocker, Michal Drozdzal

CoRR, 2019

Is Texture Predictive for Age and Sex in Brain MRI?

Nick Pawlowski, Ben Glocker

CoRR, 2019

Representation Disentanglement for Multi-task Learning with Application to Fetal Ultrasound.

Qingjie Meng, Nick Pawlowski, Daniel Rueckert, Bernhard Kainz

Proceedings of the Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis, 2019

Unsupervised Lesion Detection with Locally Gaussian Approximation.

Xiaoran Chen, Nick Pawlowski, Ben Glocker, Ender Konukoglu

Proceedings of the Machine Learning in Medical Imaging - 10th International Workshop, 2019

2018

Deep Generative Models in the Real-World: An Open Challenge from Medical Imaging.

Xiaoran Chen, Nick Pawlowski, Martin Rajchl, Ben Glocker, Ender Konukoglu

CoRR, 2018

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines.

Martin Rajchl, Nick Pawlowski, Daniel Rueckert, Paul M. Matthews, Ben Glocker

CoRR, 2018

Multi-modal Learning from Unpaired Images: Application to Multi-organ Segmentation in CT and MRI.

Vanya V. Valindria, Nick Pawlowski, Martin Rajchl, Ioannis Lavdas, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker

Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision, 2018

2017

Rasa: Open Source Language Understanding and Dialogue Management.

Tom Bocklisch, Joey Faulkner, Nick Pawlowski, Alan Nichol

CoRR, 2017

DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images.

Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl

CoRR, 2017

Implicit Weight Uncertainty in Neural Networks.

Nick Pawlowski, Martin Rajchl, Ben Glocker

CoRR, 2017

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation.

Konstantinos Kamnitsas, Wenjia Bai, Enzo Ferrante, Steven G. McDonagh, Matthew Sinclair, Nick Pawlowski, Martin Rajchl, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2017

Efficient variational Bayesian neural network ensembles for outlier detection.

Nick Pawlowski, Miguel Jaques, Ben Glocker

Proceedings of the 5th International Conference on Learning Representations, 2017