Liaw, Christopher

15 publications

NeurIPS 2025 A Unified Approach to Submodular Maximization Under Noise Kshipra Bhawalkar, Yang Cai, Zhe Feng, Christopher Liaw, Tao Lin

ALT 2025 Agnostic Private Density Estimation for GMMs via List Global Stability Mohammad Afzali, Hassan Ashtiani, Christopher Liaw

JMLR 2024 Continuous Prediction with Experts' Advice Nicholas J. A. Harvey, Christopher Liaw, Victor S. Portella

ALT 2024 Mixtures of Gaussians Are Privately Learnable with a Polynomial Number of Samples Mohammad Afzali, Hassan Ashtiani, Christopher Liaw

ICML 2023 Improved Online Learning Algorithms for CTR Prediction in Ad Auctions Zhe Feng, Christopher Liaw, Zixin Zhou

ICML 2023 Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models Jamil Arbas, Hassan Ashtiani, Christopher Liaw

COLT 2022 Private and Polynomial Time Algorithms for Learning Gaussians and Beyond Hassan Ashtiani, Christopher Liaw

AAAI 2021 Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi

NeurIPS 2021 Privately Learning Mixtures of Axis-Aligned Gaussians Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw

NeurIPS 2020 Improved Algorithms for Online Submodular Maximization via First-Order Regret Bounds Nicholas Harvey, Christopher Liaw, Tasuku Soma

ICLR 2019 A New Dog Learns Old Tricks: RL Finds Classic Optimization Algorithms Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar

JMLR 2019 Nearly-Tight VC-Dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian

COLT 2019 Tight Analyses for Non-Smooth Stochastic Gradient Descent Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa

NeurIPS 2018 Nearly Tight Sample Complexity Bounds for Learning Mixtures of Gaussians via Sample Compression Schemes Hassan Ashtiani, Shai Ben-David, Nicholas Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan

COLT 2017 Nearly-Tight VC-Dimension Bounds for Piecewise Linear Neural Networks Nick Harvey, Christopher Liaw, Abbas Mehrabian