| Grandmaster level in StarCraft II using multi-agent reinforcement learning O Vinyals, I Babuschkin, WM Czarnecki, M Mathieu, A Dudzik, J Chung, ... Nature 575 (7782), 350-354, 2019 | 3094 | 2019 |
| Population based training of neural networks M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 699 | 2017 |
| Alphastar: Mastering the real-time strategy game starcraft ii O Vinyals, I Babuschkin, J Chung, M Mathieu, M Jaderberg, ... DeepMind blog 2, 20, 2019 | 489 | 2019 |
| Open-ended learning leads to generally capable agents OEL Team, A Stooke, A Mahajan, C Barros, C Deck, J Bauer, J Sygnowski, ... arXiv preprint arXiv:2107.12808, 2021 | 94 | 2021 |
| BOAT: Building auto-tuners with structured Bayesian optimization V Dalibard, M Schaarschmidt, E Yoneki Proceedings of the 26th International Conference on World Wide Web, 479-488, 2017 | 92 | 2017 |
| A generalized framework for population based training A Li, O Spyra, S Perel, V Dalibard, M Jaderberg, C Gu, D Budden, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 59 | 2019 |
| PrefEdge: SSD prefetcher for large-scale graph traversal K Nilakant, V Dalibard, A Roy, E Yoneki Proceedings of International Conference on Systems and Storage, 1-12, 2014 | 37 | 2014 |
| Population based training of neural networks. arXiv M Jaderberg, V Dalibard, S Osindero, WM Czarnecki, J Donahue, ... arXiv preprint arXiv:1711.09846, 2017 | 24* | 2017 |
| Rapid training of deep neural networks without skip connections or normalization layers using deep kernel shaping J Martens, A Ballard, G Desjardins, G Swirszcz, V Dalibard, ... arXiv preprint arXiv:2110.01765, 2021 | 23 | 2021 |
| A framework to build bespoke auto-tuners with structured Bayesian optimisation V Dalibard University of Cambridge, Computer Laboratory, 2017 | 8 | 2017 |
| Learning runtime parameters in computer systems with delayed experience injection M Schaarschmidt, F Gessert, V Dalibard, E Yoneki arXiv preprint arXiv:1610.09903, 2016 | 8 | 2016 |
| Discovering Evolution Strategies via Meta-Black-Box Optimization RT Lange, T Schaul, Y Chen, T Zahavy, V Dallibard, C Lu, S Singh, ... arXiv preprint arXiv:2211.11260, 2022 | 6 | 2022 |
| Faster improvement rate population based training V Dalibard, M Jaderberg arXiv preprint arXiv:2109.13800, 2021 | 5 | 2021 |
| Perception-prediction-reaction agents for deep reinforcement learning A Stooke, V Dalibard, SM Jayakumar, WM Czarnecki, M Jaderberg arXiv preprint arXiv:2006.15223, 2020 | 2 | 2020 |
| Tuning the scheduling of distributed stochastic gradient descent with Bayesian optimization V Dalibard, M Schaarschmidt, E Yoneki arXiv preprint arXiv:1612.00383, 2016 | 2 | 2016 |
| Mitigating I/O latency in SSD-based graph traversal A Roy, K Nilakant, V Dalibard, E Yoneki University of Cambridge, Computer Laboratory, 2012 | 2 | 2012 |
| Discovering Attention-Based Genetic Algorithms via Meta-Black-Box Optimization RT Lange, T Schaul, Y Chen, C Lu, T Zahavy, V Dalibard, S Flennerhag arXiv preprint arXiv:2304.03995, 2023 | 1 | 2023 |
| Population-based training of machine learning models A Li, VC Dalibard, D Budden, O Spyra, ME Jaderberg, TJA Harley, S Perel, ... US Patent App. 16/586,236, 2021 | 1 | 2021 |
| Population Based Training as a Service A Li, O Spyra, S Perel, V Dalibard, M Jaderberg, C Gu, D Budden, ... NIPS Systems for ML Workshop, Montréal, Canada, 2018 | 1 | 2018 |
| Community detection in multi-layer networks V Dalibard Master’s thesis, University of Cambridge, 2012 | 1 | 2012 |
