Christopher Liaw

Orcid: 0000-0001-5373-9229

According to our database1, Christopher Liaw authored at least 31 papers between 2016 and 2024.

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Bibliography

2024
Continuous Prediction with Experts' Advice.
J. Mach. Learn. Res., 2024

Auto-bidding and Auctions in Online Advertising: A Survey.
CoRR, 2024

Agnostic Private Density Estimation via Stable List Decoding.
CoRR, 2024

Efficiency of Non-Truthful Auctions in Auto-bidding with Budget Constraints.
Proceedings of the ACM on Web Conference 2024, 2024

User Response in Ad Auctions: An MDP Formulation of Long-term Revenue Optimization.
Proceedings of the ACM on Web Conference 2024, 2024

The Power of Two-Sided Recruitment in Two-Sided Markets.
Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 2024

Mixtures of Gaussians are Privately Learnable with a Polynomial Number of Samples.
Proceedings of the International Conference on Algorithmic Learning Theory, 2024

2023
User Response in Ad Auctions: An MDP Formulation of Long-Term Revenue Optimization.
CoRR, 2023

Efficiency of Non-Truthful Auctions in Auto-bidding: The Power of Randomization.
Proceedings of the ACM Web Conference 2023, 2023

Improved Online Learning Algorithms for CTR Prediction in Ad Auctions.
Proceedings of the International Conference on Machine Learning, 2023

Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models.
Proceedings of the International Conference on Machine Learning, 2023

2022
Efficiency of non-truthful auctions under auto-bidding.
CoRR, 2022

Private and polynomial time algorithms for learning Gaussians and beyond.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
Privately Learning Mixtures of Axis-Aligned Gaussians.
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.
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.
J. ACM, 2020

Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds.
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.
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.
J. Mach. Learn. Res., 2019

Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent.
CoRR, 2019

A new dog learns old tricks: RL finds classic optimization algorithms.
Proceedings of the 7th International Conference on Learning Representations, 2019

The Vickrey Auction with a Single Duplicate Bidder Approximates the Optimal Revenue.
Proceedings of the 2019 ACM Conference on Economics and Computation, 2019

Tight analyses for non-smooth stochastic gradient descent.
Proceedings of the Conference on Learning Theory, 2019

2018
Greedy and Local Ratio Algorithms in the MapReduce Model.
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018

The Value of Information Concealment.
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.
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.
Proceedings of the 30th Canadian Conference on Computational Geometry, 2018

2017
Rainbow Hamilton cycles and lopsidependency.
Discret. Math., 2017

Tight Load Balancing Via Randomized Local Search.
Proceedings of the 2017 IEEE International Parallel and Distributed Processing Symposium, 2017

Nearly-tight VC-dimension bounds for piecewise linear neural networks.
Proceedings of the 30th Conference on Learning Theory, 2017

2016
A simple tool for bounding the deviation of random matrices on geometric sets.
CoRR, 2016


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