We investigate the adversarial robustness of streaming algorithms. In this context, an
algorithm is considered robust if its performance guarantees hold even if the stream is …

We investigate replicable learning algorithms. Informally a learning algorithm is replicable if
the algorithm outputs the same canonical hypothesis over multiple runs with high probability …

A streaming algorithm is considered to be adversarially robust if it provides correct outputs
with high probability even when the stream updates are chosen by an adversary who may …

A randomized algorithm for a search problem is pseudodeterministic if it produces a fixed
canonical solution to the search problem with high probability. In their seminal work on the …

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 …

We connect the study of pseudodeterministic algorithms to two major open problems about
the structural complexity of BPTIME: proving hierarchy theorems and showing the existence …

We investigate one of the most basic problems in streaming algorithms: approximating the
number of elements in the stream. Famously, Mor78 gave a randomized algorithm achieving …

Many streaming algorithms provide only a high-probability relative approximation. These
two relaxations, of allowing approximation and randomization, seem necessary--for many …

A probabilistic algorithm A is pseudodeterministic if, on every input, there exists a canonical
value that is output with high probability. If the algorithm outputs one of k canonical values …

We study pseudo-deterministic query complexity-randomized query algorithms that are
required to output the same answer with high probability on all inputs. We prove Ω (√ n) …