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Oana-Maria Camburu
Oana-Maria Camburu
Senior Research Fellow, University College London
Verified email at ucl.ac.uk
Title
Cited by
Cited by
Year
Generation and comprehension of unambiguous object descriptions
J Mao, J Huang, A Toshev, O Camburu, AL Yuille, K Murphy
CVPR 2016, 11-20, 2016
11112016
e-SNLI: Natural Language Inference with Natural Language Explanations
OM Camburu, T Rocktäschel, T Lukasiewicz, P Blunsom
Advances in Neural Information Processing Systems (NeurIPS) 2018, 9539-9549, 2018
5002018
A Surprisingly Robust Trick for Winograd Schema Challenge
V Kocijan, AM Cretu, OM Camburu, Y Yordanov, T Lukasiewicz
ACL 2019, 2019
1162019
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
OM Camburu, B Shillingford, P Minervini, T Lukasiewicz, P Blunsom
ACL, 2020, 2019
802019
e-vil: A dataset and benchmark for natural language explanations in vision-language tasks
M Kayser, OM Camburu, L Salewski, C Emde, V Do, Z Akata, ...
ICCV 2021, 1244-1254, 2021
742021
Can I Trust the Explainer? Verifying Post-Hoc Explanatory Methods
OM Camburu, E Giunchiglia, J Foerster, T Lukasiewicz, P Blunsom
NeurIPS 2019 Workshop on Safety and Robustness in Decision Making, Vancouver …, 2019
63*2019
Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations
BP Majumder, O Camburu, T Lukasiewicz, J Mcauley
International Conference on Machine Learning (ICML 2022), 14786-14801, 2022
41*2022
Explaining Deep Neural Networks
OM Camburu
PhD Thesis, University of Oxford, 2020
402020
e-SNLI-VE-2.0: Corrected Visual-Textual Entailment with Natural Language Explanations
V Do, OM Camburu, Z Akata, T Lukasiewicz
IEEE CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision, 2020, 2020
362020
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration
L Sha, OM Camburu, T Lukasiewicz
AAAI 2021, 2020
322020
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
V Kocijan, OM Camburu, AM Cretu, Y Yordanov, P Blunsom, ...
EMNLP 2019, 2019
292019
Faithfulness Tests for Natural Language Explanations
P Atanasova, OM Camburu, C Lioma, T Lukasiewicz, JG Simonsen, ...
ACL 2023, 2023
182023
The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets
OM Camburu, E Giunchiglia, J Foerster, T Lukasiewicz, P Blunsom
AAAI Explainable Agency in Artificial Intelligence Workshop 2021, 2020
172020
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations
Y Yordanov, V Kocijan, T Lukasiewicz, OM Camburu
Findings of EMNLP 2022, 2021
14*2021
Explaining Chest X-ray Pathologies in Natural Language
M Kayser, C Emde, OM Camburu, G Parsons, B Papiez, T Lukasiewicz
MICCAI 2022, 2022
132022
Cyclotomic coefficients: gaps and jumps
OM Camburu, EA Ciolan, F Luca, P Moree, IE Shparlinski
Journal of Number Theory 163, 211-237, 2016
122016
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
V Kocijan, OM Camburu, T Lukasiewicz
AAAI 2021, 2020
102020
Towards explainable and trustworthy autonomous physical systems
D Omeiza, S Anjomshoae, K Kollnig, OM Camburu, K Främling, L Kunze
Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing …, 2021
62021
Logical reasoning for natural language inference using generated facts as atoms
J Stacey, P Minervini, H Dubossarsky, OM Camburu, M Rei
arXiv preprint arXiv:2305.13214, 2023
52023
KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations
M Jang, BP Majumder, J McAuley, T Lukasiewicz, OM Camburu
ACL 2023, 2023
42023
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