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Agrin Hilmkil
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2020 – today
- 2024
- [j2]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:
Deep End-to-end Causal Inference. Trans. Mach. Learn. Res. 2024 (2024) - [c4]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
A Fixed-Point Approach for Causal Generative Modeling. ICML 2024 - [c3]Jiaqi Zhang, Joel Jennings, Agrin Hilmkil, Nick Pawlowski, Cheng Zhang, Chao Ma:
Towards Causal Foundation Model: on Duality between Optimal Balancing and Attention. ICML 2024 - [i8]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:
The Essential Role of Causality in Foundation World Models for Embodied AI. CoRR abs/2402.06665 (2024) - [i7]Meyer Scetbon, Joel Jennings, Agrin Hilmkil, Cheng Zhang, Chao Ma:
FiP: a Fixed-Point Approach for Causal Generative Modeling. CoRR abs/2404.06969 (2024) - 2023
- [c2]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. ICLR 2023 - [i6]Matthew Ashman, Chao Ma, Agrin Hilmkil, Joel Jennings, Cheng Zhang:
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning. CoRR abs/2303.12703 (2023) - [i5]Cheng Zhang, Stefan Bauer, Paul Bennett, Jiangfeng Gao, Wenbo Gong, Agrin Hilmkil, Joel Jennings, Chao Ma, Tom Minka, Nick Pawlowski, James Vaughan:
Understanding Causality with Large Language Models: Feasibility and Opportunities. CoRR abs/2304.05524 (2023) - [i4]Shantanu Gupta, Cheng Zhang, Agrin Hilmkil:
Learned Causal Method Prediction. CoRR abs/2311.03989 (2023) - 2021
- [j1]Abbas Cheddad
, Hüseyin Kusetogullari, Agrin Hilmkil, Lena Sundin, Amir Yavariabdi, Aouache Mustapha
SHIBR - The Swedish Historical Birth Records: a semi-annotated dataset. Neural Comput. Appl. 33(22): 15863-15875 (2021) - [c1]Agrin Hilmkil, Sebastian Callh, Matteo Barbieri, Leon René Sütfeld, Edvin Listo Zec, Olof Mogren:
Scaling Federated Learning for Fine-Tuning of Large Language Models. NLDB 2021: 15-23 - [i3]Agrin Hilmkil, Sebastian Callh, Matteo Barbieri, Leon René Sütfeld, Edvin Listo Zec, Olof Mogren:
Scaling Federated Learning for Fine-tuning of Large Language Models. CoRR abs/2102.00875 (2021) - 2020
- [i2]Agrin Hilmkil, Carl Thomé, Anders Arpteg:
Perceiving Music Quality with GANs. CoRR abs/2006.06287 (2020)
2010 – 2019
- 2018
- [i1]Agrin Hilmkil, Oscar Ivarsson, Moa Johansson, Dan Kuylenstierna, Teun van Erp:
Towards Machine Learning on data from Professional Cyclists. CoRR abs/1808.00198 (2018)
Coauthor Index
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