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Liam Li
Liam Li
Carnegie Mellon University
Verified email at liamcli.com - Homepage
Title
Cited by
Cited by
Year
Hyperband: A novel bandit-based approach to hyperparameter optimization
L Li, K Jamieson, G DeSalvo, A Rostamizadeh, A Talwalkar
The Journal of Machine Learning Research 18 (1), 6765-6816, 2017
14292017
Random search and reproducibility for neural architecture search
L Li, A Talwalkar
Uncertainty in artificial intelligence, 367-377, 2020
4552020
A system for massively parallel hyperparameter tuning
L Li, K Jamieson, A Rostamizadeh, E Gonina, J Ben-Tzur, M Hardt, ...
Proceedings of Machine Learning and Systems 2, 230-246, 2020
1392020
Massively parallel hyperparameter tuning
L Li, K Jamieson, A Rostamizadeh, E Gonina, M Hardt, B Recht, ...
arXiv preprint arXiv:1810.05934 5, 2018
1082018
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization
AT Liam Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh
ICLR, 2017
108*2017
Geometry-aware gradient algorithms for neural architecture search
L Li, M Khodak, MF Balcan, A Talwalkar
arXiv preprint arXiv:2004.07802, 2020
372020
Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
M Khodak, R Tu, T Li, L Li, MFF Balcan, V Smith, A Talwalkar
Advances in Neural Information Processing Systems 34, 19184-19197, 2021
142021
On data efficiency of meta-learning
M Al-Shedivat, L Li, E Xing, A Talwalkar
International Conference on Artificial Intelligence and Statistics, 1369-1377, 2021
102021
Exploiting reuse in pipeline-aware hyperparameter tuning
L Li, E Sparks, K Jamieson, A Talwalkar
arXiv preprint arXiv:1903.05176, 2019
82019
A System for Massively Parallel Hyperparameter Tuning. arXiv 2020
L Li, K Jamieson, A Rostamizadeh, E Gonina, M Hardt, B Recht, ...
arXiv preprint arXiv:1810.05934, 0
8
Massively parallel hyperparameter tuning, 2018
L Li, K Jamieson, A Rostamizadeh, K Gonina, M Hardt, B Recht, ...
URL https://openreview. net/forum, 0
6
Weight sharing for hyperparameter optimization in federated learning
M Khodak, T Li, L Li, M Balcan, V Smith, A Talwalkar
Int. Workshop on Federated Learning for User Privacy and Data …, 2020
52020
Random search and reproducibility for neural architecture search. arXiv e-prints
L Li, A Talwalkar
arXiv preprint arXiv:1902.07638, 2019
52019
Rethinking neural operations for diverse tasks
N Roberts, M Khodak, T Dao, L Li, C Ré, A Talwalkar
Advances in Neural Information Processing Systems 34, 15855-15869, 2021
42021
Learning operations for neural PDE solvers
N Roberts, M Khodak, T Dao, L Li, C Ré, A Talwalkar
Proc. ICLR SimDL Workshop, 2021
22021
A simple setting for understanding neural architecture search with weight-sharing
M Khodak, L Li, N Roberts, MF Balcan, A Talwalkar
ICML AutoML Workshop, 2020
12020
On Weight-Sharing and Bilevel Optimization in Architecture Search
M Khodak, L Li, MF Balcan, A Talwalkar
12019
Weight-sharing beyond neural architecture search: Efficient feature map selection and federated hyperparameter tuning
M Khodak, L Li, N Roberts, MF Balcan, A Talwalkar
Proc. 2nd SysML Conf., 2019
12019
Towards Efficient Automated Machine Learning
L Li
Carnegie Mellon University, 2020
2020
Searching for Convolutions and a More Ambitious NAS
N Roberts, M Khodak, T Dao, L Li, MF Balcan, C Ré, A Talwalkar
2020
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