48 citations · 53 across the 3 of their papers we have counts for
6 papers
Optimal Query Complexities for Dynamic Trace Estimation
David P. Woodruff, Fred Zhang, Qiuyi Zhang
We consider the problem of minimizing the number of matrix-vector queries needed for accurate trace estimation in the dynamic setting where our underlying matrix is changing slowly…
Faster Fundamental Graph Algorithms via Learned Predictions
Justin Y. Chen, Sandeep Silwal, Ali Vakilian +1
We consider the question of speeding up classic graph algorithms with machine-learned predictions. In this model, algorithms are furnished with extra advice learned from past or si…
Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms
Alexander Wei, Fred Zhang
We study the problem of improving the performance of online algorithms by incorporating machine-learned predictions. The goal is to design algorithms that are both consistent and r…
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Samuel B. Hopkins, Jerry Li, Fred Zhang
We study the problem of estimating the mean of a distribution in high dimensions when either the samples are adversarially corrupted or the distribution is heavy-tailed. Recent dev…
A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates
Zhixian Lei, Kyle Luh, Prayaag Venkat +1
We study the algorithmic problem of estimating the mean of heavy-tailed random vector in , given i.i.d. samples. The goal is to design an efficient estimator that…
SGD on Neural Networks Learns Functions of Increasing Complexity
Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris +4
We perform an experimental study of the dynamics of Stochastic Gradient Descent (SGD) in learning deep neural networks for several real and synthetic classification tasks. We show…