A simple tool for bounding the deviation of random matrices on geometric sets. - Christopher Liaw; Abbas Mehrabian; Yaniv Plan; Roman Vershynin (CoRR, 2016)

Nearly-tight VC-dimension bounds for piecewise linear neural networks. - Nick Harvey; Christopher Liaw; Abbas Mehrabian (COLT, 2017)

Tight Load Balancing Via Randomized Local Search. - Petra Berenbrink; Peter Kling; Christopher Liaw; Abbas Mehrabian (IPDPS, 2017)

Rainbow Hamilton cycles and lopsidependency. - Nicholas J. A. Harvey; Christopher Liaw (Discret. Math., 2017)

Approximation Schemes for Covering and Packing in the Streaming Model. - Christopher Liaw; Paul Liu 001; Robert Reiss (CCCG, 2018)

Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. - Hassan Ashtiani; Shai Ben-David; Nicholas J. A. Harvey; Christopher Liaw; Abbas Mehrabian; Yaniv Plan (NeurIPS, 2018)

The Value of Information Concealment. - Hu Fu 001; Christopher Liaw; Pinyan Lu; Zhihao Gavin Tang (SODA, 2018)

Greedy and Local Ratio Algorithms in the MapReduce Model. - Nicholas J. A. Harvey; Christopher Liaw; Paul Liu 001 (SPAA, 2018)

Tight analyses for non-smooth stochastic gradient descent. - Nicholas J. A. Harvey; Christopher Liaw; Yaniv Plan; Sikander Randhawa (COLT, 2019)

The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue. - Hu Fu 001; Christopher Liaw; Sikander Randhawa (EC, 2019)

A new dog learns old tricks: RL finds classic optimization algorithms. - Weiwei Kong; Christopher Liaw; Aranyak Mehta; D. Sivakumar 001 (ICLR (Poster), 2019)

Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent. - Nicholas J. A. Harvey; Christopher Liaw; Sikander Randhawa (CoRR, 2019)

Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks. - Peter L. Bartlett; Nick Harvey; Christopher Liaw; Abbas Mehrabian (J. Mach. Learn. Res., 2019)

Optimal anytime regret for two experts. - Nicholas J. A. Harvey; Christopher Liaw; Edwin A. Perkins; Sikander Randhawa (FOCS, 2020)

Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds. - Nicholas J. A. Harvey; Christopher Liaw; Tasuku Soma (NeurIPS, 2020)

Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes. - Hassan Ashtiani; Shai Ben-David; Nicholas J. A. Harvey; Christopher Liaw; Abbas Mehrabian; Yaniv Plan (J. ACM, 2020)

Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. - Zhe Feng 004; Guru Guruganesh; Christopher Liaw; Aranyak Mehta; Abhishek Sethi (AAAI, 2021)

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

Private and polynomial time algorithms for learning Gaussians and beyond. - Hassan Ashtiani; Christopher Liaw (COLT, 2022)

Efficiency of non-truthful auctions under auto-bidding. - Christopher Liaw; Aranyak Mehta; Andrés Perlroth (CoRR, 2022)

Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. - Jamil Arbas; Hassan Ashtiani; Christopher Liaw (ICML, 2023)

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. - Zhe Feng 004; Christopher Liaw; Zixin Zhou (ICML, 2023)

Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization. - Christopher Liaw; Aranyak Mehta; Andrés Perlroth (WWW, 2023)

User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization. - Yang Cai 001; Zhe Feng 004; Christopher Liaw; Aranyak Mehta (CoRR, 2023)

Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. - Mohammad Afzali; Hassan Ashtiani; Christopher Liaw (ALT, 2024)

The Power of Two-Sided Recruitment in Two-Sided Markets. - Yang Cai 001; Christopher Liaw; Aranyak Mehta; Mingfei Zhao (STOC, 2024)

User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization. - Yang Cai 001; Zhe Feng 004; Christopher Liaw; Aranyak Mehta; Grigoris Velegkas (WWW, 2024)

Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints. - Christopher Liaw; Aranyak Mehta; Wennan Zhu (WWW, 2024)

Agnostic Private Density Estimation via Stable List Decoding. - Mohammad Afzali; Hassan Ashtiani; Christopher Liaw (CoRR, 2024)

Continuous Prediction with Experts' Advice. - Nicholas J. A. Harvey; Christopher Liaw; Victor S. Portella (J. Mach. Learn. Res., 2024)

Tight analyses for subgradient descent I: Lower bounds. - Nicholas J. A. Harvey; Chris Liaw; Sikander Randhawa (Open J. Math. Optim., 2024)

Auto-Bidding and Auctions in Online Advertising: A Survey. - Gagan Aggarwal; Ashwinkumar Badanidiyuru; Santiago R. Balseiro; Kshipra Bhawalkar; Yuan Deng; Zhe Feng 004; Gagan Goel; Christopher Liaw; Haihao Lu; Mohammad Mahdian; Jieming Mao; Aranyak Mehta; et al. (SIGecom Exch., 2024)

Agnostic Private Density Estimation for GMMs via List Global Stability. - Mohammad Afzali; Hassan Ashtiani; Christopher Liaw (ALT, 2025)

Equilibrium Efficiency and Learning in Budgeted Procurement Marketplaces. - Kshipra Bhawalkar; Jeff Dean; Christopher Liaw; Aranyak Mehta; Neel Patel (EC, 2025)

Autobidding With Interdependent Values. - Martino Banchio; Kshipra Bhawalkar; Christopher Liaw; Aranyak Mehta; Andrés Perlroth (WWW, 2025)

Equilibria and Learning in Modular Marketplaces. - Kshipra Bhawalkar; Jeff Dean; Christopher Liaw; Aranyak Mehta; Neel Patel (CoRR, 2025)