| Generation and comprehension of unambiguous object descriptions J Mao, J Huang, A Toshev, O Camburu, AL Yuille, K Murphy CVPR 2016, 11-20, 2016 | 1406 | 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 | 637 | 2018 |
| A Surprisingly Robust Trick for Winograd Schema Challenge V Kocijan, AM Cretu, OM Camburu, Y Yordanov, T Lukasiewicz ACL 2019, 2019 | 127 | 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 | 102 | 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 | 101 | 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 | 71* | 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 | 51* | 2022 |
| Faithfulness Tests for Natural Language Explanations P Atanasova, OM Camburu, C Lioma, T Lukasiewicz, JG Simonsen, ... ACL 2023, 2023 | 50 | 2023 |
| Explaining Deep Neural Networks OM Camburu PhD Thesis, University of Oxford, 2020 | 50 | 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 | 49 | 2020 |
| WikiCREM: A Large Unsupervised Corpus for Coreference Resolution V Kocijan, OM Camburu, AM Cretu, Y Yordanov, P Blunsom, ... EMNLP 2019, 2019 | 36 | 2019 |
| Learning from the Best: Rationalizing Prediction by Adversarial Information Calibration L Sha, OM Camburu, T Lukasiewicz AAAI 2021, 2020 | 35 | 2020 |
| Explaining Chest X-ray Pathologies in Natural Language M Kayser, C Emde, OM Camburu, G Parsons, B Papiez, T Lukasiewicz MICCAI 2022, 2022 | 25 | 2022 |
| Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations Y Yordanov, V Kocijan, T Lukasiewicz, OM Camburu Findings of EMNLP 2022, 2021 | 23* | 2021 |
| 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 | 21 | 2020 |
| Cyclotomic coefficients: gaps and jumps OM Camburu, EA Ciolan, F Luca, P Moree, IE Shparlinski Journal of Number Theory 163, 211-237, 2016 | 14 | 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 | 13 | 2020 |
| Using Natural Language Explanations to Improve Robustness of In-context Learning for Natural Language Inference X He, Y Wu, OM Camburu, P Minervini, P Stenetorp ACL'24, 2023 | 7 | 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 | 7 | 2021 |
| Logical reasoning for natural language inference using generated facts as atoms J Stacey, P Minervini, H Dubossarsky, OM Camburu, M Rei EMNLP 2024, 2023 | 6 | 2023 |
Thomas LukasiewiczVienna University of Technology, Austria; University of Oxford, UKVerified email at tuwien.ac.at
