1 citations · 1 across the 9 of their papers we have counts for
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Learning Confidence Ellipsoids and Applications to Robust Subspace Recovery
Chao Gao, Liren Shan, Vaidehi Srinivas +1
We study the problem of finding confidence ellipsoids for an arbitrary distribution in high dimensions. Given samples from a distribution and a confidence parameter , the go…
Computing High-dimensional Confidence Sets for Arbitrary Distributions
Chao Gao, Liren Shan, Vaidehi Srinivas +1
We study the problem of learning a high-density region of an arbitrary distribution over . Given a target coverage parameter , and sample access to an arbitrary di…
Competitive strategies to use "warm start" algorithms with predictions
Vaidehi Srinivas, Avrim Blum
We consider the problem of learning and using predictions for warm start algorithms with predictions. In this setting, an algorithm is given an instance of a problem, and a predict…
New Tools for Smoothed Analysis: Least Singular Value Bounds for Random Matrices with Dependent Entries
Aditya Bhaskara, Eric Evert, Vaidehi Srinivas +1
We develop new techniques for proving lower bounds on the least singular value of random matrices with limited randomness. The matrices we consider have entries that are given by p…
The Predicted-Updates Dynamic Model: Offline, Incremental, and Decremental to Fully Dynamic Transformations
Quanquan C. Liu, Vaidehi Srinivas
We formulate the predicted-updates dynamic model, one of the first beyond-worst-case models for dynamic algorithms, which generalizes a large set of well-studied dynamic models inc…
Memory Bounds for the Experts Problem
Vaidehi Srinivas, David P. Woodruff, Ziyu Xu +1
Online learning with expert advice is a fundamental problem of sequential prediction. In this problem, the algorithm has access to a set of "experts" who make predictions on ea…