| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 6992 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 3439 | 2024 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| Actor-Attention-Critic for Multi-Agent Reinforcement Learning S Iqbal, F Sha Proceedings of the 36th International Conference on Machine Learning (ICML …, 2019 | 1177 | 2019 |
| Gemma 3 Technical Report G Team, A Kamath, J Ferret, S Pathak, N Vieillard, R Merhej, S Perrin, ... arXiv preprint arXiv:2503.19786, 2025 | 899 | 2025 |
| Faster sorting algorithms discovered using deep reinforcement learning DJ Mankowitz, A Michi, A Zhernov, M Gelmi, M Selvi, C Paduraru, ... Nature 618 (7964), 257-263, 2023 | 333 | 2023 |
| Training Language Models to Self-Correct via Reinforcement Learning A Kumar, V Zhuang, R Agarwal, Y Su, JD Co-Reyes, A Singh, K Baumli, ... arXiv preprint arXiv:2409.12917, 2024 | 314 | 2024 |
| Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning S Iqbal, CAS de Witt, B Peng, W Böhmer, S Whiteson, F Sha Proceedings of the 38th International Conference on Machine Learning (ICML), 2021 | 137* | 2021 |
| Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices JJ MacInnes, S Iqbal, J Pearson, EN Johnson bioRxiv, 299925, 2018 | 113 | 2018 |
| Robotic control system S Iqbal, J Tremblay, TH To, J Cheng, E Leitch, DJ McKay, ST Birchfield US Patent 11,833,681, 2023 | 98 | 2023 |
| Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning S Iqbal, F Sha arXiv preprint arXiv:1905.12127, 2019 | 62 | 2019 |
| Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies H Zhou, X Wan, R Sun, H Palangi, S Iqbal, I Vulić, A Korhonen, SÖ Arık arXiv preprint arXiv:2502.02533, 2025 | 55 | 2025 |
| When MAML Can Adapt Fast and How to Assist When It Cannot S Arnold, S Iqbal, F Sha International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 39* | 2021 |
| ALMA: Hierarchical Learning for Composite Multi-Agent Tasks S Iqbal, R Costales, F Sha Advances in Neural Information Processing Systems 35, 7155-7166, 2022 | 38 | 2022 |
| A domain-agnostic approach for characterization of lifelong learning systems MM Baker, A New, M Aguilar-Simon, Z Al-Halah, SMR Arnold, ... Neural Networks 160, 274-296, 2023 | 37 | 2023 |
| Toward Sim-to-Real Directional Semantic Grasping S Iqbal, J Tremblay, T To, J Cheng, E Leitch, A Campbell, K Leung, ... International Conference on Robotics and Automation (ICRA), 7247-7253, 2020 | 36* | 2020 |
| Latent goal models for dynamic strategic interaction SN Iqbal, L Yin, CB Drucker, Q Kuang, JF Gariépy, ML Platt, JM Pearson PLOS Computational Biology 15 (3), e1006895, 2019 | 11 | 2019 |
| Mobile Gaze Mapping: A Python package for mapping mobile gaze data to a fixed target stimulus J MacInnes, S Iqbal, J Pearson, E Johnson Journal of Open Source Software 3 (31), 984, 2018 | 11 | 2018 |
| Possibility Before Utility: Learning And Using Hierarchical Affordances R Costales, S Iqbal, F Sha International Conference on Learning Representations, 2021 | 6 | 2021 |
| A Goal-Based Movement Model for Continuous Multi-Agent Tasks S Iqbal, J Pearson NIPS BigNeuro Workshop, 2017 | 4 | 2017 |
Fei ShaGoogle ResearchVerified email at feisha.org
