# Christopher Liaw

According to our database

Collaborative distances:

^{1}, Christopher Liaw authored at least 14 papers between 2016 and 2020.Collaborative distances:

## Timeline

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Book In proceedings Article PhD thesis Other## Links

#### On csauthors.net:

## Bibliography

2020

Optimal anytime regret with two experts.

CoRR, 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