Advances in recent techniques for scientific data collection in the era of big data allow for the
systematic accumulation of large quantities of data at various data-capturing sites. Similarly …

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 …

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

Inspired by the clustering mechanism of human cognitive development, this paper proposes
a density-based evolutionary clustering algorithm based on incremental data (DBEC). The …

In the context of unsupervised learning, Lloyd's algorithm is one of the most widely used
clustering algorithms. It has inspired a plethora of work investigating the correctness of the …

Relation clustering is a general approach for open relation extraction (OpenRE). Current
methods have two major problems. One is that their good performance relies on large …

Spectral clustering techniques are valuable tools in signal processing and machine learning
for partitioning complex data sets. The effectiveness of spectral clustering stems from …

We investigate the complexity of solving stable or perturbation-resilient instances of k-means
and k-median clustering in fixed dimension Euclidean metrics (or more generally doubling …

Many machine learning systems are vulnerable to small perturbations made to inputs either
at test time or at training time. This has received much recent interest on the empirical front …

Kleinberg (2002) stated three axioms that any clustering procedure should satisfy and
showed there is no clustering procedure that simultaneously satisfies all three. One of these …