| Stein variational gradient descent: A general purpose Bayesian inference algorithm Q Liu, D Wang Advances In Neural Information Processing Systems, 2370-2378, 2016 | 496 | 2016 |
| Variational inference for crowdsourcing Q Liu, J Peng, AT Ihler Advances in Neural Information Processing Systems, 692-700, 2012 | 351 | 2012 |
| A kernelized Stein discrepancy for goodness-of-fit tests Q Liu, J Lee, M Jordan International conference on machine learning, 276-284, 2016 | 253 | 2016 |
| Brain and muscle Arnt-like protein-1 (BMAL1) controls circadian cell proliferation and susceptibility to UVB-induced DNA damage in the epidermis M Geyfman, V Kumar, Q Liu, R Ruiz, W Gordon, F Espitia, E Cam, ... Proceedings of the National Academy of Sciences 109 (29), 11758-11763, 2012 | 187 | 2012 |
| Breaking the curse of horizon: Infinite-horizon off-policy estimation Q Liu, L Li, Z Tang, D Zhou arXiv preprint arXiv:1810.12429, 2018 | 149 | 2018 |
| Stein variational gradient descent as gradient flow Q Liu Advances in neural information processing systems, 3115-3123, 2017 | 115 | 2017 |
| Aggregating ordinal labels from crowds by minimax conditional entropy D Zhou, Q Liu, J Platt, C Meek International conference on machine learning, 262-270, 2014 | 106 | 2014 |
| Communication-efficient Sparse Regression JD Lee, Q Liu, Y Sun, JE Taylor Journal of Machine Learning Research 18 (5), 1-30, 2017 | 94 | 2017 |
| Learning scale free networks by reweighted l1 regularization Q Liu, A Ihler Proceedings of the Fourteenth International Conference on Artificial …, 2011 | 93 | 2011 |
| Stein Variational Policy Gradient Y Liu, P Ramachandran, Q Liu, J Peng arXiv preprint arXiv:1704.02399, 2017 | 91 | 2017 |
| Practical human sensing in the light T Li, Q Liu, X Zhou Proceedings of the 14th Annual International Conference on Mobile Systems …, 2016 | 91 | 2016 |
| Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning D Wang, Q Liu arXiv preprint arXiv:1611.01722, 2016 | 89 | 2016 |
| Regularization matters: Generalization and optimization of neural nets vs their induced kernel C Wei, JD Lee, Q Liu, T Ma Advances in Neural Information Processing Systems, 9709-9721, 2019 | 82 | 2019 |
| Variational algorithms for marginal MAP Q Liu, A Ihler The Journal of Machine Learning Research 14 (1), 3165-3200, 2013 | 82 | 2013 |
| Bounding the Partition Function using Holder's Inequality Q Liu, AT Ihler Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 73 | 2011 |
| On the Discrimination-Generalization Tradeoff in GANs P Zhang, Q Liu, D Zhou, T Xu, X He arXiv preprint arXiv:1711.02771, 2017 | 71 | 2017 |
| An optimization view on dynamic routing between capsules D Wang, Q Liu | 63 | 2018 |
| Scoring workers in crowdsourcing: How many control questions are enough? Q Liu, AT Ihler, M Steyvers Advances in Neural Information Processing Systems, 1914-1922, 2013 | 62 | 2013 |
| Black-box Importance Sampling Q Liu, JD Lee arXiv preprint arXiv:1610.05247, 2016 | 60 | 2016 |
| On the Margin Theory of Feedforward Neural Networks C Wei, JD Lee, Q Liu, T Ma arXiv preprint arXiv:1810.05369, 2018 | 57 | 2018 |
Alexander IhlerUniversity of California, IrvineAdresse e-mail validée de ics.uci.edu
