According to our database1, Christopher Liaw authored at least 14 papers between 2016 and 2020.
Legend:Book In proceedings Article PhD thesis Other
Optimal anytime regret with two experts.
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.
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
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
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
A simple tool for bounding the deviation of random matrices on geometric sets.