Pawlowski, Nick

19 publications

TMLR 2024 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, 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

ICMLW 2024 ProxyTune: Hyperparameter Tuning Through Iteratively Refined Proxies Agrin Hilmkil, Wenbo Gong, Nick Pawlowski, Cheng Zhang

ICML 2024 Towards Causal Foundation Model: On Duality Between Optimal Balancing and Attention Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma

ICMLW 2023 Answering Causal Questions with Augmented LLMs Nick Pawlowski, James Vaughan, Joel Jennings, Cheng Zhang

NeurIPS 2023 BayesDAG: Gradient-Based Posterior Inference for Causal Discovery Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

ICMLW 2023 BayesDAG: Gradient-Based Posterior Sampling for Causal Discovery Yashas Annadani, Nick Pawlowski, Joel Jennings, Stefan Bauer, Cheng Zhang, Wenbo Gong

ICML 2023 High Fidelity Image Counterfactuals with Probabilistic Causal Models Fabio De Sousa Ribeiro, Tian Xia, Miguel Monteiro, Nick Pawlowski, Ben Glocker

ICLR 2023 Measuring Axiomatic Soundness of Counterfactual Image Models Miguel Monteiro, Fabio De Sousa Ribeiro, Nick Pawlowski, Daniel C. Castro, Ben Glocker

ICLR 2023 Rhino: Deep Causal Temporal Relationship Learning with History-Dependent Noise Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski

NeurIPSW 2022 A Causal AI Suite for Decision-Making Emre Kiciman, Eleanor Wiske Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Ness, Nick Pawlowski, Amit Sharma, Cheng Zhang

NeurIPSW 2022 Deep End-to-End Causal Inference Tomas Geffner, Javier Antoran, Adam Foster, Wenbo Gong, Chao Ma, Emre Kiciman, Amit Sharma, Angus Lamb, Martin Kukla, Agrin Hilmkil, Joel Jennings, Nick Pawlowski, Miltiadis Allamanis, Cheng Zhang

ECCVW 2022 Deep Structural Causal Shape Models Rajat Rasal, Daniel C. Castro, Nick Pawlowski, Ben Glocker

NeurIPSW 2022 Rhino: Deep Causal Temporal Relationship Learning with History-Dependent Noise Wenbo Gong, Joel Jennings, Cheng Zhang, Nick Pawlowski

NeurIPS 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

TMLR 2022 Structured Uncertainty in the Observation Space of Variational Autoencoders James Langley, Miguel Monteiro, Charles Jones, Nick Pawlowski, Ben Glocker

NeurIPS 2020 Deep Structural Causal Models for Tractable Counterfactual Inference Nick Pawlowski, Daniel Coelho de Castro, Ben Glocker

NeurIPS 2020 Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty Miguel Monteiro, Loic Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, Ben Glocker

WACV 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

ICLR 2017 Efficient Variational Bayesian Neural Network Ensembles for Outlier Detection Nick Pawlowski, Miguel Jaques, Ben Glocker