Generative and multi-phase learning for computer systems optimization Y Ding, N Mishra, H Hoffmann Proceedings of the 46th International Symposium on Computer Architecture, 39-52, 2019 | 57 | 2019 |
An adaptive gradient method for online auc maximization Y Ding, P Zhao, S Hoi, YS Ong Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 49 | 2015 |
A polynomial-time algorithm for learning nonparametric causal graphs M Gao, Y Ding, B Aragam Advances in Neural Information Processing Systems 33, 11599-11611, 2020 | 34 | 2020 |
Multiresolution kernel approximation for Gaussian process regression Y Ding, R Kondor, J Eskreis-Winkler Advances in Neural Information Processing Systems 30, 2017 | 28 | 2017 |
Large scale kernel methods for online auc maximization Y Ding, C Liu, P Zhao, SCH Hoi 2017 IEEE International Conference on Data Mining (ICDM), 91-100, 2017 | 26 | 2017 |
Cafqa: A classical simulation bootstrap for variational quantum algorithms GS Ravi, P Gokhale, Y Ding, W Kirby, K Smith, JM Baker, PJ Love, ... Proceedings of the 28th ACM International Conference on Architectural …, 2022 | 23 | 2022 |
Generalizable and interpretable learning for configuration extrapolation Y Ding, A Pervaiz, M Carbin, H Hoffmann Proceedings of the 29th ACM joint meeting on European software engineering …, 2021 | 22 | 2021 |
Neighborhood street activity and greenspace usage uniquely contribute to predicting crime KE Schertz, J Saxon, C Cardenas-Iniguez, LMA Bettencourt, Y Ding, ... Npj Urban Sustainability 1 (1), 19, 2021 | 19 | 2021 |
Minimax designs for causal effects in temporal experiments with treatment habituation GW Basse, Y Ding, P Toulis Biometrika 110 (1), 155-168, 2023 | 14 | 2023 |
Minimax crossover designs G Basse, Y Ding, P Toulis arXiv preprint arXiv:1908.03531, 2019 | 8 | 2019 |
Adaptive subgradient methods for online AUC maximization Y Ding, P Zhao, SCH Hoi, YS Ong arXiv preprint arXiv:1602.00351, 2016 | 7 | 2016 |
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction Y Ding, A Rao, H Song, R Willett, HH Hoffmann Proceedings of Machine Learning and Systems 4, 190-203, 2022 | 5 | 2022 |
CAFQA: Clifford Ansatz For Quantum Accuracy GS Ravi, P Gokhale ArXivorg, 2022 | 5 | 2022 |
Learning relative similarity by stochastic dual coordinate ascent P Wu, Y Ding, P Zhao, C Miao, S Hoi Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 5 | 2014 |
Cello: Efficient computer systems optimization with predictive early termination and censored regression Y Ding, A Renda, A Pervaiz, M Carbin, H Hoffmann arXiv preprint arXiv:2204.04831, 2022 | 4 | 2022 |
Programming with neural surrogates of programs A Renda, Y Ding, M Carbin Proceedings of the 2021 ACM SIGPLAN International Symposium on New Ideas …, 2021 | 4 | 2021 |
Dynamical systems theory for causal inference with application to synthetic control methods Y Ding, P Toulis International Conference on Artificial Intelligence and Statistics, 1888-1898, 2020 | 4 | 2020 |
Bayesian learning for hardware and software configuration co-optimization Y Ding, A Pervaiz, S Krishnan, H Hoffmann University of Chicago, Tech. Rep 13, 2020 | 4 | 2020 |
Scope: Safe exploration for dynamic computer systems optimization H Kim, A Pervaiz, H Hoffmann, M Carbin, Y Ding arXiv preprint arXiv:2204.10451, 2022 | 1 | 2022 |
Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs A Renda, Y Ding, M Carbin Proceedings of the ACM on Programming Languages 7 (OOPSLA2), 1648-1676, 2023 | | 2023 |