Christopher Liaw

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 45

Top Topics

Mixtures Of Gaussians

Top Venues

Publications

Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta, Grigoris Velegkas
User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization. WWW (2024)
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
Agnostic Private Density Estimation via Stable List Decoding. CoRR (2024)
Yang Cai, Christopher Liaw, Aranyak Mehta, Mingfei Zhao
The Power of Two-Sided Recruitment in Two-Sided Markets. STOC (2024)
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. ALT (2024)
Christopher Liaw, Aranyak Mehta, Wennan Zhu
Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints. WWW (2024)
Zhe Feng, Christopher Liaw, Zixin Zhou
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. CoRR (2024)
Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization. CoRR (2023)
Yang Cai, Christopher Liaw, Aranyak Mehta, Mingfei Zhao
The Power of Two-sided Recruitment in Two-sided Markets. CoRR (2023)
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. ICML (2023)
Christopher Liaw, Aranyak Mehta, Andrés Perlroth
Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization. WWW (2023)
Mohammad Afzali, Hassan Ashtiani, Christopher Liaw
Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples. CoRR (2023)
Christopher Liaw, Aranyak Mehta, Wennan Zhu
Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints. CoRR (2023)
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. CoRR (2023)
Zhe Feng, Christopher Liaw, Zixin Zhou
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions. ICML (2023)
Victor Sanches Portella, Christopher Liaw, Nicholas J. A. Harvey
Continuous Prediction with Experts' Advice. CoRR (2022)
Hassan Ashtiani, Christopher Liaw
Private and polynomial time algorithms for learning Gaussians and beyond. COLT (2022)
Christopher Liaw, Aranyak Mehta, Andrés Perlroth
Efficiency of non-truthful auctions under auto-bidding. CoRR (2022)
Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
Privately Learning Mixtures of Axis-Aligned Gaussians. CoRR (2021)
Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. AAAI (2021)
Hassan Ashtiani, Christopher Liaw
Private and polynomial time algorithms for learning Gaussians and beyond. CoRR (2021)
Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
Privately Learning Mixtures of Axis-Aligned Gaussians. NeurIPS (2021)
Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussian Mixtures via Compression Schemes. J. ACM 67 (6) (2020)
Nicholas J. A. Harvey, Christopher Liaw, Edwin A. Perkins, Sikander Randhawa
Optimal anytime regret for two experts. FOCS (2020)
Nicholas J. A. Harvey, Christopher Liaw, Edwin A. Perkins, Sikander Randhawa
Optimal anytime regret with two experts. CoRR (2020)
Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi
Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions. CoRR (2020)
Nicholas J. A. Harvey, Christopher Liaw, Tasuku Soma
Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds. NeurIPS (2020)
Hu Fu, Christopher Liaw, Sikander Randhawa
The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue. EC (2019)
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa
Tight analyses for non-smooth stochastic gradient descent. COLT (2019)
Hu Fu, Christopher Liaw, Sikander Randhawa
The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue. CoRR (2019)
Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent. CoRR (2019)
Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar
A new dog learns old tricks: RL finds classic optimization algorithms. ICLR (Poster) (2019)
Peter L. Bartlett, Nick Harvey, Christopher Liaw, Abbas Mehrabian
Nearly-tight VC-dimension and Pseudodimension Bounds for Piecewise Linear Neural Networks. J. Mach. Learn. Res. 20 (2019)
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa
Tight Analyses for Non-Smooth Stochastic Gradient Descent. CoRR (2018)
Nicholas J. A. Harvey, Christopher Liaw, Paul Liu
Greedy and Local Ratio Algorithms in the MapReduce Model. SPAA (2018)
Nicholas J. A. Harvey, Christopher Liaw, Paul Liu
Greedy and Local Ratio Algorithms in the MapReduce Model. CoRR (2018)
Hassan Ashtiani, Shai Ben-David, Nicholas J. A. Harvey, Christopher Liaw, Abbas Mehrabian, Yaniv Plan
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes. NeurIPS (2018)
Christopher Liaw, Paul Liu, Robert Reiss
Approximation Schemes for Covering and Packing in the Streaming Model. CCCG (2018)
Hu Fu, Christopher Liaw, Pinyan Lu, Zhihao Gavin Tang
The Value of Information Concealment. SODA (2018)
Nicholas J. A. Harvey, Christopher Liaw
Rainbow Hamilton cycles and lopsidependency. Discret. Math. 340 (6) (2017)
Nick Harvey, Christopher Liaw, Abbas Mehrabian
Nearly-tight VC-dimension bounds for piecewise linear neural networks. COLT (2017)
Christopher Liaw, Paul Liu, Robert Reiss
Approximation Schemes for Covering and Packing in the Streaming Model. CoRR (2017)
Petra Berenbrink, Peter Kling, Christopher Liaw, Abbas Mehrabian
Tight Load Balancing Via Randomized Local Search. IPDPS (2017)
Nick Harvey, Christopher Liaw, Abbas Mehrabian
Nearly-tight VC-dimension bounds for piecewise linear neural networks. CoRR (2017)
Petra Berenbrink, Peter Kling, Christopher Liaw, Abbas Mehrabian
Tight Load Balancing via Randomized Local Search. CoRR (2017)
Christopher Liaw, Abbas Mehrabian, Yaniv Plan, Roman Vershynin
A simple tool for bounding the deviation of random matrices on geometric sets. CoRR (2016)