| Solving empirical risk minimization in the current matrix multiplication time YT Lee, Z Song, Q Zhang Conference on Learning Theory, 2140-2157, 2019 | 147 | 2019 |
| Syntax-guided synthesis of datalog programs X Si, W Lee, R Zhang, A Albarghouthi, P Koutris, M Naik Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018 | 80 | 2018 |
| Random hypervolume scalarizations for provable multi-objective black box optimization R Zhang, D Golovin International conference on machine learning, 11096-11105, 2020 | 78 | 2020 |
| Gradientless descent: High-dimensional zeroth-order optimization D Golovin, J Karro, G Kochanski, C Lee, X Song, Q Zhang arXiv preprint arXiv:1911.06317, 2019 | 73 | 2019 |
| Getting aligned on representational alignment I Sucholutsky, L Muttenthaler, A Weller, A Peng, A Bobu, B Kim, BC Love, ... arXiv preprint arXiv:2310.13018, 2023 | 69 | 2023 |
| Towards learning universal hyperparameter optimizers with transformers Y Chen, X Song, C Lee, Z Wang, R Zhang, D Dohan, K Kawakami, ... Advances in Neural Information Processing Systems 35, 32053-32068, 2022 | 61 | 2022 |
| Electron-proton dynamics in deep learning Q Zhang, R Panigrahy, S Sachdeva, A Rahimi arXiv preprint arXiv:1702.00458, 1-31, 2017 | 34 | 2017 |
| Convergence results for neural networks via electrodynamics R Panigrahy, S Sachdeva, Q Zhang arXiv preprint arXiv:1702.00458, 2017 | 25 | 2017 |
| Optimal sketching for trace estimation S Jiang, H Pham, D Woodruff, R Zhang Advances in Neural Information Processing Systems 34, 23741-23753, 2021 | 23 | 2021 |
| Regularized weighted low rank approximation F Ban, D Woodruff, R Zhang Advances in neural information processing systems 32, 2019 | 22 | 2019 |
| One network fits all? modular versus monolithic task formulations in neural networks A Agarwala, A Das, B Juba, R Panigrahy, V Sharan, X Wang, Q Zhang arXiv preprint arXiv:2103.15261, 2021 | 16 | 2021 |
| Preference Learning Algorithms Do Not Learn Preference Rankings A Chen, S Malladi, LH Zhang, X Chen, Q Zhang, R Ranganath, K Cho arXiv preprint arXiv:2405.19534, 2024 | 15 | 2024 |
| New absolute fast converging phylogeny estimation methods with improved scalability and accuracy QR Zhang, S Rao, T Warnow 18th International Workshop on Algorithms in Bioinformatics (WABI 2018), 2018 | 11 | 2018 |
| Robust algorithms on adaptive inputs from bounded adversaries Y Cherapanamjeri, S Silwal, DP Woodruff, F Zhang, Q Zhang, S Zhou arXiv preprint arXiv:2304.07413, 2023 | 10 | 2023 |
| ES-ENAS: combining evolution strategies with neural architecture search at no extra cost for reinforcement learning X Song, K Choromanski, J Parker-Holder, Y Tang, D Peng, D Jain, W Gao, ... CoRR, abs/2101.07415, 2021 | 10 | 2021 |
| Optimal sequence length requirements for phylogenetic tree reconstruction with indels A Ganesh, Q Zhang Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 9 | 2019 |
| Getting aligned on representational alignment. arXiv I Sucholutsky, L Muttenthaler, A Weller, A Peng, A Bobu, B Kim, BC Love, ... | 8 | 2023 |
| Constrained incremental tree building: new absolute fast converging phylogeny estimation methods with improved scalability and accuracy Q Zhang, S Rao, T Warnow Algorithms for Molecular Biology 14, 1-12, 2019 | 8 | 2019 |
| Using Constrained-INC for Large-Scale Gene Tree and Species Tree Estimation T Le, A Sy, EK Molloy, Q Zhang, S Rao, T Warnow IEEE/ACM Transactions on Computational Biology and Bioinformatics 18 (1), 2-15, 2020 | 7 | 2020 |
| Using INC Within Divide-and-Conquer Phylogeny Estimation T Le, A Sy, EK Molloy, Q Zhang, S Rao, T Warnow International Conference on Algorithms for Computational Biology, 167-178, 2019 | 7 | 2019 |
Xingyou (Richard) SongSenior Research Scientist, Google DeepMindE-mail megerősítve itt: google.com
