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 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 …

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 …

A frontier open problem in circuit complexity is to prove P^{NP} is not in SIZE [n^ k] for all k;
this is a necessary intermediate step towards NP is not in P_ {/poly}. Previously, for several …

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 pseudo-deterministic algorithm is a (randomized) algorithm which, when run multiple
times on the same input, with high probability outputs the same result on all executions …

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 investigate randomized LEARN-uniformity, which captures the power of randomness
and equivalence queries (EQ) in the construction of Boolean circuits for an explicit problem …

We exhibit several computational problems that are {\em complete} for multi-
pseudodeterministic computations in the following sense:(1) these problems admit $2 …