Oana-Maria Camburu
Oana-Maria Camburu
Senior Research Fellow, University College London
Verified email at
Cited by
Cited by
Generation and comprehension of unambiguous object descriptions
J Mao, J Huang, A Toshev, O Camburu, AL Yuille, K Murphy
CVPR 2016, 11-20, 2016
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
A Surprisingly Robust Trick for Winograd Schema Challenge
V Kocijan, AM Cretu, OM Camburu, Y Yordanov, T Lukasiewicz
ACL 2019, 2019
Make Up Your Mind! Adversarial Generation of Inconsistent Natural Language Explanations
OM Camburu, B Shillingford, P Minervini, T Lukasiewicz, P Blunsom
ACL, 2020, 2019
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
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
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
Explaining Deep Neural Networks
OM Camburu
PhD Thesis, University of Oxford, 2020
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
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution
V Kocijan, OM Camburu, AM Cretu, Y Yordanov, P Blunsom, ...
EMNLP 2019, 2019
Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration
L Sha, OM Camburu, T Lukasiewicz
AAAI 2021, 2020
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
Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations
Y Yordanov, V Kocijan, T Lukasiewicz, OM Camburu
Findings of EMNLP 2022, 2021
Cyclotomic coefficients: gaps and jumps
OM Camburu, EA Ciolan, F Luca, P Moree, IE Shparlinski
Journal of Number Theory 163, 211-237, 2016
The Gap on GAP: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
V Kocijan, OM Camburu, T Lukasiewicz
AAAI 2021, 2020
Explaining Chest X-ray Pathologies in Natural Language
M Kayser, C Emde, OM Camburu, G Parsons, B Papiez, T Lukasiewicz
MICCAI 2022, 2022
Faithfulness Tests for Natural Language Explanations
P Atanasova, OM Camburu, C Lioma, T Lukasiewicz, JG Simonsen, ...
ACL 2023, 2023
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
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
Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution
Y Yordanov, OM Camburu, V Kocijan, T Lukasiewicz
EMNLP 2020, 2020
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