Agrin Hilmkil

According to our database, Agrin Hilmkil authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• 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

Pyramid Vector Quantization for LLMs.

Tycho F. A. van der Ouderaa, Maximilian L. Croci, Agrin Hilmkil, James Hensman

CoRR, 2024

AVID: Adapting Video Diffusion Models to World Models.

Marc Rigter, Tarun Gupta, Agrin Hilmkil, Chao Ma

CoRR, 2024

Zero-Shot Learning of Causal Models.

Divyat Mahajan, Jannes Gladrow, Agrin Hilmkil, Cheng Zhang, Meyer Scetbon

CoRR, 2024

FiP: a Fixed-Point Approach for Causal Generative Modeling.

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

CoRR, 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

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

A Fixed-Point Approach for Causal Generative Modeling.

Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma

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

2023

Learned Causal Method Prediction.

Shantanu Gupta, Cheng Zhang, Agrin Hilmkil

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

Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning.

Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2021

SHIBR - The Swedish Historical Birth Records: a semi-annotated dataset.

Abbas Cheddad, Hüseyin Kusetogullari, Agrin Hilmkil, Lena Sundin, Amir Yavariabdi, Aouache Mustapha, Johan Hall

Neural Comput. Appl., 2021

Scaling Federated Learning for Fine-Tuning of Large Language Models.

Agrin Hilmkil, Sebastian Callh, Matteo Barbieri, Leon René Sütfeld, Edvin Listo Zec, Olof Mogren

Proceedings of the Natural Language Processing and Information Systems, 2021

2020

Perceiving Music Quality with GANs.

Agrin Hilmkil, Carl Thomé, Anders Arpteg

CoRR, 2020

2018

Towards Machine Learning on data from Professional Cyclists.

Agrin Hilmkil, Oscar Ivarsson, Moa Johansson, Dan Kuylenstierna, Teun van Erp

CoRR, 2018