| Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks PL Bartlett, N Harvey, C Liaw, A Mehrabian Journal of Machine Learning Research 20 (63), 1-17, 2019 | 855 | 2019 |
| Tight analyses for non-smooth stochastic gradient descent NJA Harvey, C Liaw, Y Plan, S Randhawa Conference on Learning Theory, 1579-1613, 2019 | 199 | 2019 |
| Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes H Ashtiani, S Ben-David, N Harvey, C Liaw, A Mehrabian, Y Plan Advances in Neural Information Processing Systems 31, 2018 | 152* | 2018 |
| A simple tool for bounding the deviation of random matrices on geometric sets C Liaw, A Mehrabian, Y Plan, R Vershynin Geometric Aspects of Functional Analysis: Israel Seminar (GAFA) 2014–2016 …, 2017 | 85 | 2017 |
| Private and polynomial time algorithms for learning gaussians and beyond H Ashtiani, C Liaw Conference on Learning Theory, 1075-1076, 2022 | 71 | 2022 |
| A new dog learns old tricks: RL finds classic optimization algorithms W Kong, C Liaw, A Mehta, D Sivakumar International conference on learning representations, 2018 | 58 | 2018 |
| Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent NJA Harvey, C Liaw, S Randhawa arXiv preprint arXiv:1909.00843, 2019 | 50 | 2019 |
| Greedy and local ratio algorithms in the mapreduce model NJA Harvey, C Liaw, P Liu Proceedings of the 30th on Symposium on Parallelism in Algorithms and …, 2018 | 47 | 2018 |
| Convergence analysis of no-regret bidding algorithms in repeated auctions Z Feng, G Guruganesh, C Liaw, A Mehta, A Sethi Proceedings of the AAAI Conference on Artificial Intelligence 35 (6), 5399-5406, 2021 | 43 | 2021 |
| Polynomial time and private learning of unbounded gaussian mixture models J Arbas, H Ashtiani, C Liaw International Conference on Machine Learning, 1018-1040, 2023 | 40 | 2023 |
| Auto-bidding and auctions in online advertising: A survey G Aggarwal, A Badanidiyuru, SR Balseiro, K Bhawalkar, Y Deng, Z Feng, ... ACM SIGecom Exchanges 22 (1), 159-183, 2024 | 39 | 2024 |
| Privately learning mixtures of axis-aligned gaussians I Aden-Ali, H Ashtiani, C Liaw Advances in Neural Information Processing Systems 34, 3925-3938, 2021 | 22 | 2021 |
| The value of information concealment H Fu, C Liaw, P Lu, ZG Tang Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 20 | 2018 |
| Optimal anytime regret with two experts NJA Harvey, C Liaw, E Perkins, S Randhawa Mathematical Statistics and Learning 6 (1), 87-142, 2023 | 19 | 2023 |
| Efficiency of non-truthful auctions in auto-bidding: The power of randomization C Liaw, A Mehta, A Perlroth Proceedings of the ACM Web Conference 2023, 3561-3571, 2023 | 19 | 2023 |
| Improved algorithms for online submodular maximization via first-order regret bounds N Harvey, C Liaw, T Soma Advances in Neural Information Processing Systems 33, 123-133, 2020 | 17 | 2020 |
| Efficiency of non-truthful auctions under auto-bidding C Liaw, A Mehta, A Perlroth arXiv preprint arXiv:2207.03630, 2022 | 15 | 2022 |
| Mixtures of gaussians are privately learnable with a polynomial number of samples M Afzali, H Ashtiani, C Liaw International Conference on Algorithmic Learning Theory, 47-73, 2024 | 12 | 2024 |
| Improved online learning algorithms for ctr prediction in ad auctions Z Feng, C Liaw, Z Zhou International Conference on Machine Learning, 9921-9937, 2023 | 12 | 2023 |
| The Vickrey auction with a single duplicate bidder approximates the optimal revenue H Fu, C Liaw, S Randhawa Proceedings of the 2019 ACM Conference on Economics and Computation, 419-420, 2019 | 12 | 2019 |
