2 citations · 2 across the 11 of their papers we have counts for
6 papers · 1 filter
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures
Jie Gao, Rajesh Jayaram, Benedikt Kolbe +4
Randomized dimensionality reduction is a widely-used algorithmic technique for speeding up large-scale Euclidean optimization problems. In this paper, we study dimension reduction…
Statistical-Computational Trade-offs for Density Estimation
Anders Aamand, Alexandr Andoni, Justin Y. Chen +4
We study the density estimation problem defined as follows: given distributions over a discrete domain , as well as a collection of samples chosen from…
Constant Approximation for Individual Preference Stable Clustering
Anders Aamand, Justin Y. Chen, Allen Liu +4
Individual preference (IP) stability, introduced by Ahmadi et al. (ICML 2022), is a natural clustering objective inspired by stability and fairness constraints. A clustering is …
Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees
Nicholas Schiefer, Justin Y. Chen, Piotr Indyk +3
An -approximate quantile sketch over a stream of inputs approximates the rank of any query point - that is, the number of input points less than - up to an…
Robust Algorithms on Adaptive Inputs from Bounded Adversaries
Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff +3
We study dynamic algorithms robust to adaptive input generated from sources with bounded capabilities, such as sparsity or limited interaction. For example, we consider robust line…
Improved Space Bounds for Learning with Experts
Anders Aamand, Justin Y. Chen, Huy Lê Nguyen +1
We give improved tradeoffs between space and regret for the online learning with expert advice problem over days with experts. Given a space budget of for $δ\in (0,1)…