Adel Bibi is a senior researcher in machine learning and computer vision at the Department of Engineering Science of the University of Oxford with Philip H.S. Torr. He is a Junior Research Fellow (JRF) of Kellogg College and a member of the ELLIS Society. Prior to that, Bibi was a postdoctoral research assistant and a senior research associate in the same department. He received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) in 2016 and 2020, respectively, working with Bernard Ghanem. In 2018, Bibi was a visiting PhD intern for 6 months at Intel Labs in Munich working with Vladlen Koltun. Bibi received an Amazon Research Award in Fall 2021 and has contributed more than 30 papers published in top machine learning and computer vision conferences like CVPR, ICCV, ECCV, ICCV, ICLR, NeurIPS, TPAMI, AAAI, and UAI. Bibi has also served as an Area Chair for NeurIPS23, AAAI23, and IJCAI23. He has received outstanding reviewer awards in CVPR18, CVPR19, ICCV19, and ICLR22.
Currently, Bibi is interested in large scale offline and online robust and private continual learning. Robustness, in both aspects empirical and provably certifiable, here refers to deep models under $\ell_p$ bounded additive and geometric attacks. Moreover, continual learning refers to the learning from a stream of data in stringent memory and computational settings.
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[Hiring!] We are always looking for strong postdoc candidates and prospective PhD students interested in the areas of robustness and continual learning. Contact me if you are interested.
PhD in Electrical Engineering (4.0/4.0); Machine Learning and Optimization Track, 2020
King Abdullah University of Science and Technology (KAUST)
MSc in Electrical Engineering (4.0/4.0); Computer Vision Track, 2016
King Abdullah University of Science and Technology (KAUST)
BSc in Electrical Engineering (3.99/4.0), 2014
Kuwait University
Improving and guaranteeing the robustness of deep learning models has been a topic of intense research. Ensembling, which combines several classifiers to provide a better model, has shown to be beneficial for generalisation, uncertainty estimation, calibration, and mitigating the effects of concept drift. However, the impact of ensembling on certified robustness is less well understood. In this work, we generalise Lipschitz continuity by introducing S-Lipschitz classifiers, which we use to analyse the theoretical robustness of ensembles. Our results are precise conditions when ensembles of robust classifiers are more robust than any constituent classifier, as well as conditions when they are less robust.
Current evaluations of Continual Learning (CL) methods typically assume that there is no constraint on training time and computation. This is an unrealistic assumption for any real-world setting, which motivates us to propose; a practical real-time evaluation of continual learning, in which the stream does not wait for the model to complete training before revealing the next data for predictions. To do this, we evaluate current CL methods with respect to their computational costs. We conduct extensive experiments on CLOC, a large-scale dataset containing 39 million time-stamped images with geolocation labels. We show that a simple baseline outperforms state-of-the-art CL methods under this evaluation, questioning the applicability of existing methods in realistic settings. In addition, we explore various CL components commonly used in the literature, including memory sampling strategies and regularization approaches. We find that all considered methods fail to be competitive against our simple baseline. This surprisingly suggests that the majority of existing CL literature is tailored to a specific class of streams that is not practical. We hope that the evaluation we provide will be the first step towards a paradigm shift to consider the computational cost in the development of online continual learning methods.
Continual Learning (CL) aims to sequentially train models on streams of incoming data that vary in distribution by preserving previous knowledge while adapting to new data. Current CL literature focuses on restricted access to previously seen data, while imposing no constraints on the computational budget for training. This is unreasonable for applications in-the-wild, where systems are primarily constrained by computational and time budgets, not storage. We revisit this problem with a large-scale benchmark and analyze the performance of traditional CL approaches in a compute-constrained setting, where effective memory samples used in training can be implicitly restricted as a consequence of limited computation. We conduct experiments evaluating various CL sampling strategies, distillation losses, and partial fine-tuning on two large-scale datasets, namely ImageNet2K and Continual Google Landmarks V2 in data incremental, class incremental, and time incremental settings. Through extensive experiments amounting to a total of over 1500 GPU-hours, we find that, under compute-constrained setting, traditional CL approaches, with no exception, fail to outperform a simple minimal baseline that samples uniformly from memory. Our conclusions are consistent in a different number of stream time steps, e.g., 20 to 200, and under several computational budgets. This suggests that most existing CL methods are particularly too computationally expensive for realistic budgeted deployment.