Christopher Liaw

According to our database, Christopher Liaw authored at least 28 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints.

Christopher Liaw, Aranyak Mehta, Wennan Zhu

CoRR, 2023

Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples.

Mohammad Afzali, Hassan Ashtiani, Christopher Liaw

CoRR, 2023

The Power of Two-sided Recruitment in Two-sided Markets.

Yang Cai, Christopher Liaw, Aranyak Mehta, Mingfei Zhao

CoRR, 2023

User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization.

Yang Cai, Zhe Feng, Christopher Liaw, Aranyak Mehta

CoRR, 2023

Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization.

Christopher Liaw, Aranyak Mehta, Andrés Perlroth

Proceedings of the ACM Web Conference 2023, 2023

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions.

Zhe Feng, Christopher Liaw, Zixin Zhou

Proceedings of the International Conference on Machine Learning, 2023

Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models.

Jamil Arbas, Hassan Ashtiani, Christopher Liaw

Proceedings of the International Conference on Machine Learning, 2023

2022

Efficiency of non-truthful auctions under auto-bidding.

Christopher Liaw, Aranyak Mehta, Andrés Perlroth

CoRR, 2022

Continuous Prediction with Experts' Advice.

Victor Sanches Portella, Christopher Liaw, Nicholas J. A. Harvey

CoRR, 2022

Private and polynomial time algorithms for learning Gaussians and beyond.

Hassan Ashtiani, Christopher Liaw

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

Privately Learning Mixtures of Axis-Aligned Gaussians.

Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Convergence Analysis of No-Regret Bidding Algorithms in Repeated Auctions.

Zhe Feng, Guru Guruganesh, Christopher Liaw, Aranyak Mehta, Abhishek Sethi

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

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

Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds.

Nicholas J. A. Harvey, Christopher Liaw, Tasuku Soma

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Optimal anytime regret for two experts.

Nicholas J. A. Harvey, Christopher Liaw, Edwin A. Perkins, Sikander Randhawa

Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

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

Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent.

Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa

CoRR, 2019

A new dog learns old tricks: RL finds classic optimization algorithms.

Weiwei Kong, Christopher Liaw, Aranyak Mehta, D. Sivakumar

Proceedings of the 7th International Conference on Learning Representations, 2019

The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue.

Hu Fu, Christopher Liaw, Sikander Randhawa

Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

Tight analyses for non-smooth stochastic gradient descent.

Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan, Sikander Randhawa

Proceedings of the Conference on Learning Theory, 2019

2018

Greedy and Local Ratio Algorithms in the MapReduce Model.

Nicholas J. A. Harvey, Christopher Liaw, Paul Liu

Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018

The Value of Information Concealment.

Hu Fu, Christopher Liaw, Pinyan Lu, Zhihao Gavin Tang

Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Approximation Schemes for Covering and Packing in the Streaming Model.

Christopher Liaw, Paul Liu, Robert Reiss

Proceedings of the 30th Canadian Conference on Computational Geometry, 2018

2017

Rainbow Hamilton cycles and lopsidependency.

Nicholas J. A. Harvey, Christopher Liaw

Discret. Math., 2017

Tight Load Balancing Via Randomized Local Search.

Petra Berenbrink, Peter Kling, Christopher Liaw, Abbas Mehrabian

Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium, 2017

Nearly-tight VC-dimension bounds for piecewise linear neural networks.

Nick Harvey, Christopher Liaw, Abbas Mehrabian

Proceedings of the 30th Conference on Learning Theory, 2017

2016

A simple tool for bounding the deviation of random matrices on geometric sets.

Christopher Liaw, Abbas Mehrabian, Yaniv Plan, Roman Vershynin

CoRR, 2016