Beyond worst-case analysis Page 1 88 COMMUNICATIONS OF THE ACM | MARCH 2019 |
VOL. 62 | NO. 3 review articles COMPARING DIFFERENT ALGORITHMS is hard. For almost …

We prove novel algorithmic guarantees for several online problems in the smoothed
analysis model. In this model, at each time step an adversary chooses an input distribution …

Dasgupta recently introduced a cost function for the hierarchical clustering of a set of points
given pairwise similarities between them. He showed that this function is NP-hard to …

Practical and pervasive needs for robustness and privacy in algorithms have inspired the
design of online adversarial and differentially private learning algorithms. The primary …

We study exact recovery conditions for convex relaxations of point cloud clustering
problems, focusing on two of the most common optimization problems for unsupervised …

Motivated by the fact that distances between data points in many real-world clustering
instances are often based on heuristic measures, Bilu and Linial Proceedings of the …

Coresets are arguably the most popular compression paradigm for center-based clustering
objectives such as k-means. Given a point set P, a coreset Ω is a small, weighted summary …

Given k∊ ℕ, the k'th discrete Heisenberg group, denoted ℍ ℤ 2 k+ 1, is the group generated
by the elements a 1, b 1,…, ak, bk, c, subject to the commutator relations a 1, b 1=···= ak, bk …

We study the approximability of the MaxCut problem in the presence of predictions.
Specifically, we consider two models: in the noisy predictions model, for each vertex we are …

We study the notion of stability and perturbation resilience introduced by Bilu and Linial
(2010) and Awasthi, Blum, and Sheffet (2012). A combinatorial optimization problem is α …