Clustering is an important technique for identifying structural information in large-scale data
analysis, where the underlying dataset may be too large to store. In many applications …

We provide the first sub-linear space and sub-linear regret algorithm for online learning with
expert advice (against an oblivious adversary), addressing an open question raised recently …

In the online learning with experts problem, an algorithm must make a prediction about an
outcome on each of $ T $ days (or times), given a set of $ n $ experts who make predictions …

The majority of streaming problems are defined and analyzed in a static setting, where the
data stream is any worst-case sequence of insertions and deletions which is fixed in …

In the online learning with experts problem, an algorithm makes predictions about an
outcome on each of $ T $ days, given a set of $ n $ experts who make predictions on each …

We study streaming algorithms in the white-box adversarial stream model, where the
internal state of the streaming algorithm is revealed to an adversary who adaptively …

Algebraic data structures are the main subroutine for maintaining distances in fully dynamic
graphs in subquadratic time. However, these dynamic algebraic algorithms generally cannot …

The data management of large companies often prioritize more recent data, as a source of
higher accuracy prediction than outdated data. For example, the Facebook data policy …

Many problems on data streams have been studied at two extremes of difficulty: either
allowing randomized algorithms, in the static setting (where they should err with bounded …

We introduce a new notion of information complexity for multi-pass streaming problems and
use it to resolve several important questions in data streams. In the coin problem, one sees a …