46 citations · 114 across the 18 of their papers we have counts for
5 papers · 1 filter
Dynamic Algorithm for Explainable k-medians Clustering under lp Norm
Konstantin Makarychev, Ilias Papanikolaou, Liren Shan
We study the problem of explainable k-medians clustering introduced by Dasgupta, Frost, Moshkovitz, and Rashtchian (2020). In this problem, the goal is to construct a threshold dec…
LiD-FL: Towards List-Decodable Federated Learning
Hong Liu, Liren Shan, Han Bao +3
Federated learning is often used in environments with many unverified participants. Therefore, federated learning under adversarial attacks receives significant attention. This pap…
Explainable k-means. Don't be greedy, plant bigger trees!
Konstantin Makarychev, Liren Shan
We provide a new bi-criteria competitive algorithm for explainable -means clustering. Explainable -means was recently introduced by Dasgupta, Frost, Mos…
Improved Guarantees for k-means++ and k-means++ Parallel
Konstantin Makarychev, Aravind Reddy, Liren Shan
In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved ap…
Stochastic Linear Optimization with Adversarial Corruption
Yingkai Li, Edmund Y. Lou, Liren Shan
We extend the model of stochastic bandits with adversarial corruption (Lykouriset al., 2018) to the stochastic linear optimization problem (Dani et al., 2008). Our algorithm is agn…