| 2025 | ALT | Agnostic Private Density Estimation for GMMs via List Global Stability. | Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2025 | WWW | Autobidding With Interdependent Values. | Martino Banchio, Kshipra Bhawalkar, Christopher Liaw, Aranyak Mehta, Andrs Perlroth |
| 2024 | ALT | Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. | Mohammad Afzali, Hassan Ashtiani, Christopher Liaw |
| 2024 | WWW | User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization. | Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas |
| 2024 | WWW | Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints. | Christopher Liaw, Aranyak Mehta, Wennan Zhu |
| 2024 | STOC | The Power of Two-Sided Recruitment in Two-Sided Markets. | Yang Cai, Christopher Liaw, Aranyak Mehta, Mingfei Zhao |
| 2023 | ICML | Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. | Jamil Arbas, Hassan Ashtiani, Christopher Liaw |
| 2023 | ICML | Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. | Zhe Feng, Christopher Liaw, Zixin Zhou |
| 2023 | WWW | Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization. | Christopher Liaw, Aranyak Mehta, Andrs Perlroth |
| 2022 | COLT | Private and polynomial time algorithms for learning Gaussians and beyond. | Hassan Ashtiani, Christopher Liaw |
| 2021 | AAAI | Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. | Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi |
| 2020 | FOCS | Optimal anytime regret for two experts. | Nicholas J. A. Harvey, Christopher Liaw, Edwin A. Perkins, Sikander Randhawa |
| 2019 | COLT | Tight analyses for non-smooth stochastic gradient descent. | Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa |
| 2019 | EC | The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue. | Hu Fu, Christopher Liaw, Sikander Randhawa |
| 2019 | ICLR | A new dog learns old tricks: RL finds classic optimization algorithms. | Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar |
| 2018 | SODA | The Value of Information Concealment. | Hu Fu, Christopher Liaw, Pinyan Lu, Zhihao Gavin Tang |
| 2018 | SPAA | Greedy and Local Ratio Algorithms in the MapReduce Model. | Nicholas J. A. Harvey, Christopher Liaw, Paul Liu |
| 2017 | COLT | Nearly-tight VC-dimension bounds for piecewise linear neural networks. | Nick Harvey, Christopher Liaw, Abbas Mehrabian |