23 citations · 43 across the 9 of their papers we have counts for
11 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…
Leveraging Initial Hints for Free in Stochastic Linear Bandits
Ashok Cutkosky, Chris Dann, Abhimanyu Das +2
We study the setting of optimizing with bandit feedback with additional prior knowledge provided to the learner in the form of an initial hint of the optimal action. We present a n…
Optimal Sketching for Trace Estimation
Shuli Jiang, Hai Pham, David P. Woodruff +2
Matrix trace estimation is ubiquitous in machine learning applications and has traditionally relied on Hutchinson's method, which requires matrix-vector product…
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala, Abhimanyu Das, Brendan Juba +4
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ab…
Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization
Daniel Golovin, Qiuyi Zhang
Single-objective black box optimization (also known as zeroth-order optimization) is the process of minimizing a scalar objective , given evaluations at adaptively chosen inp…
Learning the gravitational force law and other analytic functions
Atish Agarwala, Abhimanyu Das, Rina Panigrahy +1
Large neural network models have been successful in learning functions of importance in many branches of science, including physics, chemistry and biology. Recent theoretical work…